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Mathematical LogicTheoretical Computer Science·Captain: wurtle

Weak and strong normalization in pure type systemsResearch Paper

Motivation: does having a normal form imply termination?

Pure type systems (PTSs) are a uniform framework covering the simply typed λ-calculus, System F, the calculus of constructions and the type theories behind proof assistants such as Coq and Lean. A system is specified by its sorts (like ∗\ast∗ and □\square□), axioms (∗:□\ast:\square∗:□) and product rules (which dependent function types may be formed). Normalization is the key metatheoretic property: it underlies logical consistency and decidability of type checking. There are two versions. A term is weakly normalizing if some sequence of β-reductions reaches a normal form, and strongly normalizing if every reduction sequence is finite. Strong normalization is what implementations need (any evaluation strategy terminates) but it is much harder to prove. The Barendregt–Geuvers–Klop conjecture asserts that, for pure type systems, the system-wide weak property already implies the strong one.

Timeline

  • 1989 — Girard's Proofs and Types presents the Tait–Girard reducibility method for strong normalization (book).
  • 1993 — Geuvers states the system-wide conjecture for β and βη reduction in his thesis (Logics and Type Systems, Nijmegen, Conjecture 8.1.2) (thesis).
  • 1997 — Sørensen proves the implication for generalized nondependent, clean, negatable systems via continuation-passing translations (thesis, Copenhagen, Theorem 3.5.20).
  • 2001 — Barthe, Hatcliff and Sørensen prove a uniform nondependent version (TCS 2001).
  • 2006 — Barthe and Coquand show that erasing λ-domain annotations can identify non-convertible terms in non-normalizing PTSs (JFP 2006), a constraint on erasure-based approaches.
  • 2014 — Roux and van Doorn develop a structural theory of PTSs, preserving weak normalization under sums and added rules (RTA–TLCA 2014).
  • 2023 — Mull reduces the conjecture within tiered systems to an irrelevance-eliminating translation (TYPES 2022, LIPIcs 2023) and extends the nondependent results in his thesis (Chicago, 2023).
  • 2025 — Roux proposes internal strong-normalization proofs for specific systems (TYPES 2025 abstract).
  • 2026 — An OpenAI preprint, Weak and strong normalization in pure type systems (OpenAI Math Release, September 25, 2026), claims the β-version of the conjecture for every PTS, with no functionality assumption. The preprint has not been peer reviewed and its theorem is not formally verified.

Setting

A specification consists of a type SSS of sorts, an axiom relation A⊆S2\mathcal A\subseteq S^2A⊆S2 and a rule relation R⊆S3\mathcal R\subseteq S^3R⊆S3; neither is assumed functional. Expressions are

M,N,A,B::=x∣s∣MN∣λx:A. M∣Πx:A. B,s∈S,M,N,A,B ::= x\mid s\mid MN\mid \lambda x{:}A.\,M\mid \Pi x{:}A.\,B,\qquad s\in S,M,N,A,B::=x∣s∣MN∣λx:A.M∣Πx:A.B,s∈S,

up to renaming of bound variables. β-reduction is the compatible closure of (λx:A.M)N→βM[x:=N](\lambda x{:}A.M)N\to_\beta M[x:=N](λx:A.M)N→β​M[x:=N], with steps allowed everywhere, including inside the annotation AAA of a λ and both parts of a Π. Typing Γ⊢M:A\Gamma\vdash M:AΓ⊢M:A is generated by the seven standard PTS rules (axiom, variable, weakening, product, abstraction, application, conversion up to =β=_\beta=β​). A context is valid if each declared type is typed by a sort in the preceding prefix, and MMM is legal in Γ\GammaΓ if Γ⊢M:A\Gamma\vdash M:AΓ⊢M:A or Γ⊢A:M\Gamma\vdash A:MΓ⊢A:M for some AAA.

The system is weakly (strongly) normalizing if every legal expression in every valid context is weakly (strongly) normalizing.

Formalization targets

Goal: β-Barendregt–Geuvers–Klop (Theorem 1)

For every specification P=(S,A,R)\mathcal P=(S,\mathcal A,\mathcal R)P=(S,A,R),

(∀Γ valid, ∀M legal in Γ: M is weakly β-normalizing) ⟹ (∀Γ valid, ∀M legal in Γ: M is strongly β-normalizing).\bigl(\forall \Gamma\ \text{valid},\ \forall M\ \text{legal in }\Gamma:\ M\ \text{is weakly }\beta\text{-normalizing}\bigr) \ \Longrightarrow\ \bigl(\forall \Gamma\ \text{valid},\ \forall M\ \text{legal in }\Gamma:\ M\ \text{is strongly }\beta\text{-normalizing}\bigr).(∀Γ valid, ∀M legal in Γ: M is weakly β-normalizing) ⟹ (∀Γ valid, ∀M legal in Γ: M is strongly β-normalizing).

Both quantifiers range over the whole system; the theorem does not claim that an individual weakly normalizing term is strongly normalizing, and it says nothing about η-reduction. The goal statement is published on the platform with status Open: no machine-checked proof exists yet.

Significance

The result itself. For any PTS, a proof that every legal term has a normal form now yields termination of every β-reduction strategy, including reductions inside types and annotations that may continue after the computational body is normal. Earlier results needed nondependent products, cleanliness, negatability, a tiered sort hierarchy, or a translation requiring extra product rules; the general case — arbitrary sorts and nonfunctional rules — was not covered by any earlier result since Geuvers' 1993 formulation.

Formalizing it. The statement is entirely syntactic and finitary, so it is a natural target for a proof assistant, and the relevant objects (de Bruijn syntax, substitution, typing derivations) are standard formalization material. The proof in the source is long (over 70 pages) and combines reducibility candidates with a fixed-point construction over sort profiles; a machine check would substantially raise confidence. A formal PTS metatheory library (substitution lemmas, confluence, subject reduction) would be reusable for other type-theory formalizations.

Difficulty

The obvious route — reducibility candidates à la Tait–Girard — needs a model of types built by recursion on sorts, which fails for impredicative or nonfunctional specifications where a type can have several sorts and no hierarchy is available. Translation methods (CPS or irrelevance elimination) need specific product rules to preserve typing, which a general specification may lack. Weak normalization of one term cannot be used alone; the argument must exploit normal forms of other open expressions. Annotations add a further complication: erasing them can change the equational theory (Barthe–Coquand), so reduction inside annotations must be tracked rather than erased.

Formalization scope

  • Expressions use de Bruijn indices (Expr S with var, sort, app, lam, pi), so α-equivalence is syntactic; substitution is parallel substitution with lifting.
  • Beta is full compatible closure, including lam_domain, pi_domain and pi_body steps; conversion is Relation.EqvGen Beta.
  • HasType has exactly the seven PTS rules; contexts are lists stored newest-first, each declaration typed in its prefix (ValidContext). Legal includes expressions occurring as types.
  • Weak normalization is reduction to a normal form; strong normalization is accessibility for the converse of Beta.
  • The sort type S is arbitrary (any universe), and axioms and rules are arbitrary relations: no functionality, finiteness or hierarchy assumptions.
  • Needed infrastructure: renaming/substitution lemmas, confluence of β, subject reduction for PTSs, reducibility candidates. All are reusable for any PTS formalization.

Selected references

  • J. H. Geuvers, Logics and Type Systems, PhD thesis, Katholieke Universiteit Nijmegen, 1993. https://www.cs.ru.nl/~herman/PUBS/Proefschrift.pdf
  • J.-Y. Girard, Proofs and Types, Cambridge University Press, 1989. https://www.paultaylor.eu/stable/prot.pdf
  • M. H. B. Sørensen, Normalization in λ-Calculus and Type Theory, PhD thesis, University of Copenhagen, 1997. https://di.ku.dk/forskning/Publikationer/tekniske_rapporter/tekniske-rapporter-1997/97-27.pdf
  • G. Barthe, J. Hatcliff and M. H. Sørensen, Weak normalization implies strong normalization in a class of non-dependent pure type systems, Theoret. Comput. Sci. (2001). https://doi.org/10.1016/S0304-3975(01)00012-3
  • G. Barthe and T. Coquand, Remarks on the equational theory of non-normalizing pure type systems, J. Funct. Programming (2006). https://doi.org/10.1017/S0956796803004726
  • C. Roux and F. van Doorn, The structural theory of pure type systems, RTA–TLCA 2014. https://doi.org/10.1007/978-3-319-08918-8_25
  • N. Mull, An irrelevancy-eliminating translation of pure type systems, TYPES 2022, LIPIcs, 2023. https://doi.org/10.4230/LIPIcs.TYPES.2022.7
  • OpenAI, Weak and strong normalization in pure type systems, OpenAI Math Release preprint, September 25, 2026 (source of the goal; Theorem 1, p. 3). https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/paper.pdf
2 thms1 active userReviewed
Dynamical SystemsMathematical PhysicsProbability·Captain: wurtle

The sharp factor-of-IID threshold for the free Ising model on regular treesResearch Paper

Motivation

A random configuration on the vertices of a graph is a factor of IID if it can be produced by a measurable rule from independent uniform labels at the vertices, where the rule commutes with the graph's automorphisms. On trees this notion connects ergodic theory (which invariant processes are "Bernoulli-like"), local algorithms on sparse random graphs (factors of IID are limits of what local algorithms can produce), and statistical mechanics. The free Ising state on the ddd-regular tree is the basic test case. It is easy to sample from a chosen root by broadcasting spins outward, but a factor rule may not use a root. Lyons asked for which inverse temperatures it is a factor of IID (Lyons 2017), and Nam, Sly and Zhang conjectured the answer tanh⁡β≤(d−1)−1/2\tanh\beta\le(d-1)^{-1/2}tanhβ≤(d−1)−1/2 after proving it for large ddd below a non-explicit constant (Nam–Sly–Zhang 2022).

This mission asks for a formal proof of the exact threshold, including equality, as stated in an OpenAI preprint dated September 26, 2026 (source). The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open.

Background

  • 1995–1996 — Bleher, Ruiz and Zagrebnov (J. Stat. Phys. 1995) and Ioffe (Lett. Math. Phys. 1996): the free state on the regular tree is extremal exactly when (d−1)tanh⁡2β≤1(d-1)\tanh^2\beta\le1(d−1)tanh2β≤1.
  • 2000 — Evans, Kenyon, Peres and Schulman relate broadcasting on trees to the Ising model (Ann. Appl. Probab. 2000).
  • 2010 — Pemantle and Peres give a capacity criterion that also covers critical cases (Ann. Probab. 2010).
  • 2015 — Backhausz, Szegedy and Virág's correlation bound for factors of IID (Random Struct. Alg. 2015), which rules out the free state beyond the threshold.
  • 2017 — Lyons surveys factors of IID on trees, records Sly's obstruction above the reconstruction threshold, and the finite-cluster construction in the uniqueness range tanh⁡β≤1/(d−1)\tanh\beta\le 1/(d-1)tanhβ≤1/(d−1) (CPC 2017).
  • 2022 — Nam, Sly and Zhang construct the free state as a factor for d≥d0d\ge d_0d≥d0​ and tanh⁡β≤c/d−1\tanh\beta\le c/\sqrt{d-1}tanhβ≤c/d−1​, via posterior-drift stochastic differential equations, and conjecture the sharp threshold (CMP 2022).

Setting

Let TdT_dTd​ be the infinite ddd-regular tree with vertex set VVV; concretely, VVV is the set of finite words over {1,…,d}\{1,\dots,d\}{1,…,d} with no two equal consecutive letters, and www is adjacent to wcwcwc. Put θ=tanh⁡β\theta=\tanh\betaθ=tanhβ for β≥0\beta\ge0β≥0. The free zero-field Ising law μd,β\mu_{d,\beta}μd,β​ on {−1,+1}V\{-1,+1\}^V{−1,+1}V is determined by its marginals on finite connected subtrees DDD:

μd,β(σ∣D=η)=12∏{u,v}∈E(D)1+θ ηuηv2.\mu_{d,\beta}(\sigma|_D=\eta)=\frac12\prod_{\{u,v\}\in E(D)}\frac{1+\theta\,\eta_u\eta_v}{2}.μd,β​(σ∣D​=η)=21​{u,v}∈E(D)∏​21+θηu​ηv​​.

Equivalently, a fair spin at one vertex is broadcast along edges, each child agreeing with its parent with probability (1+θ)/2(1+\theta)/2(1+θ)/2. A spin law is a factor of IID if it is the law of Φ(U)\Phi(U)Φ(U) for i.i.d. uniform labels U=(Uv)v∈VU=(U_v)_{v\in V}U=(Uv​)v∈V​ and a measurable Φ:[0,1]V→{−1,1}V\Phi:[0,1]^V\to\{-1,1\}^VΦ:[0,1]V→{−1,1}V such that, for each fixed automorphism ggg, Φ(gU)=gΦ(U)\Phi(gU)=g\Phi(U)Φ(gU)=gΦ(U) almost surely, where (gx)v=xg−1v(gx)_v=x_{g^{-1}v}(gx)v​=xg−1v​.

Formalization targets

Goal: Theorem 1.1 (p. 1)

For every integer d≥3d\ge3d≥3 and every β≥0\beta\ge0β≥0,

μd,β is a factor of IID  ⟺  tanh⁡β≤1d−1.\mu_{d,\beta}\ \text{is a factor of IID}\iff \tanh\beta\le\frac1{\sqrt{d-1}}.μd,β​ is a factor of IID⟺tanhβ≤d−1​1​.

Significance

The result itself. It answers the ferromagnetic case of Lyons's question and proves the Nam–Sly–Zhang conjecture for every d≥3d\ge3d≥3. The threshold coincides with the reconstruction (extremality) threshold of the free state, and the result holds at equality, where non-reconstruction alone gives no factor rule (a weak limit of factors need not be a factor). The preprint also shows that, in this range, the factor can be chosen to commute with every automorphism on every input (Lemma 8.5, p. 46). The converse direction (tanh⁡β>(d−1)−1/2\tanh\beta>(d-1)^{-1/2}tanhβ>(d−1)−1/2 excludes a factor) is known from Sly's obstruction via the Backhausz–Szegedy–Virág bound and is reproved on p. 2.

Formalizing it. The preprint's construction uses Brownian observation processes, stochastic localization, innovation equations of nonlinear filtering, and conditional-copy coupling arguments. Mathlib has Brownian motion only in early form and no stochastic calculus for infinitely many coupled SDEs; a formal proof would build reusable stochastic-analysis infrastructure. The converse needs the BSV correlation bound for factors of IID. No machine-checked factor-of-IID result for a non-trivial Gibbs measure is known.

Difficulty

Rooted sampling is easy; the obstacle is to remove the root while keeping the output a measurable function of the labels. Finite-cluster constructions only work in the uniqueness range θ≤1/(d−1)\theta\le1/(d-1)θ≤1/(d−1). The Nam–Sly–Zhang approach observes Xv(t)=tσv+Bv(t)X_v(t)=t\sigma_v+B_v(t)Xv​(t)=tσv​+Bv​(t) and expresses the posterior through innovations WvW_vWv​, which are independent Brownian motions; but being Brownian does not make XXX a function of WWW, and that is the central step (pp. 2–3). At criticality θ=(d−1)−1/2\theta=(d-1)^{-1/2}θ=(d−1)−1/2 the field-response sums in the innovation equation are only borderline integrable near time zero, so infinite sums must be justified with an exact covariance-energy identity rather than with a contraction estimate (Section 1.2, p. 3).

Formalization scope

  • TreeVertex d is the set of words in Fin d with distinct consecutive letters, treeAdj appends or removes one letter, and TreeAut d is the group of adjacency-preserving bijections.
  • Labels have law Measure.infinitePi of Lebesgue measure on [0,1][0,1][0,1]; spins take values in a two-element type with the discrete σ-algebra, and configurations carry the product σ-algebra.
  • HasFreeIsingLaw θ μ prescribes μ\muμ on every cylinder over a finite connected Finset by the product formula above; these cylinders determine the law.
  • IsFactorOfIID d θ asks for a measurable Phi with pushforward law HasFreeIsingLaw θ and, for each automorphism g, Phi (relabel g U) = relabel g (Phi U) for almost every U (null set may depend on g), matching (1.1).
  • The goal uses θ = Real.tanh β with β≥0\beta\ge0β≥0 and d≥3d\ge3d≥3; at β=0\beta=0β=0 both sides hold trivially (i.i.d. spins), which is consistent with the source. The stronger everywhere-equivariant version is not part of the goal.

Selected references

  • OpenAI, The sharp factor-of-IID threshold for the free Ising model on regular trees, OpenAI Math Release preprint, September 26, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/The-sharp-factor-of-IID-threshold-for-the-free-Ising-model-on-regular-trees-September-26-2026/article.pdf
  • D. Nam, A. Sly, L. Zhang, Ising model on trees and factors of IID, Comm. Math. Phys. 389 (2022), 1009–1046. https://doi.org/10.1007/s00220-021-04260-2
  • R. Lyons, Factors of IID on trees, Combin. Probab. Comput. 26 (2017), 285–300. https://doi.org/10.1017/S096354831600033X
  • Á. Backhausz, B. Szegedy, B. Virág, Ramanujan graphings and correlation decay in local algorithms, Random Structures Algorithms 47 (2015). https://doi.org/10.1002/rsa.20562
  • P. M. Bleher, J. Ruiz, V. A. Zagrebnov, On the purity of the limiting Gibbs state for the Ising model on the Bethe lattice, J. Stat. Phys. 79 (1995). https://doi.org/10.1007/BF02179399
  • D. Ioffe, On the extremality of the disordered state for the Ising model on the Bethe lattice, Lett. Math. Phys. 37 (1996), 137–143. https://doi.org/10.1007/BF00416016
  • W. Evans, C. Kenyon, Y. Peres, L. J. Schulman, Broadcasting on trees and the Ising model, Ann. Appl. Probab. 10 (2000). https://doi.org/10.1214/aoap/1019487349
  • R. Pemantle, Y. Peres, The critical Ising model on trees, concave recursions and nonlinear capacity, Ann. Probab. 38 (2010). https://doi.org/10.1214/09-AOP482
  • R. Eldan, Thin shell implies spectral gap up to polylog via a stochastic localization scheme, GAFA 23 (2013), 532–569. https://doi.org/10.1007/s00039-013-0214-y
  • A. El Alaoui, A. Montanari, An information-theoretic view of stochastic localization, IEEE Trans. Inform. Theory 68 (2022). https://doi.org/10.1109/TIT.2022.3180298
2 thms1 active userReviewed
Graph TheoryGroup TheoryProbability·Captain: wurtle

No percolation at criticality on quasi-transitive graphsResearch Paper

Motivation

In Bernoulli bond percolation on a graph G=(V,E)G=(V,E)G=(V,E), each edge is kept (open) independently with probability ppp. As ppp grows, an infinite connected component of open edges appears at the critical probability pc(G)p_c(G)pc​(G). The criticality problem asks whether an infinite cluster can already exist at pcp_cpc​. A negative answer means the phase transition is continuous. Benjamini and Schramm (1996) conjectured that, on every graph with a large symmetry group and pc<1p_c<1pc​<1, there is no infinite cluster at pcp_cpc​. The question contains the famous open case of Z3\mathbb Z^3Z3 and connects percolation to geometric group theory, since Cayley graphs of finitely generated groups are the main examples.

Timeline

  • 1957. Broadbent and Hammersley introduce percolation (doi:10.1017/S0305004100032680).
  • 1960, 1980. Harris and Kesten settle nearest-neighbour bond percolation on Z2\mathbb Z^2Z2, including θ(pc)=0\theta(p_c)=0θ(pc​)=0 (doi:10.1017/S0305004100034241, doi:10.1007/BF01197577).
  • 1986–1987. Menshikov and Aizenman–Barsky prove sharpness of the subcritical phase; this does not settle behaviour at pcp_cpc​ (doi:10.1007/BF01212322).
  • 1990. Hara and Slade prove mean-field behaviour, including θ(pc)=0\theta(p_c)=0θ(pc​)=0, for Zd\mathbb Z^dZd in sufficiently high dimension (doi:10.1007/BF02108785).
  • 1996. Benjamini and Schramm formulate the conjecture for quasi-transitive graphs with pc<1p_c<1pc​<1 (doi:10.1214/ECP.v1-978).
  • 1999. Benjamini, Lyons, Peres and Schramm prove it for nonamenable graphs with a unimodular quasi-transitive action (doi:10.1214/aop/1022677450).
  • 2006. Timár excludes infinitely many infinite critical clusters on nonunimodular transitive graphs (doi:10.1214/009117906000000494).
  • 2016. Hutchcroft proves the conjecture for every quasi-transitive graph of exponential growth (arXiv:1605.05301). Duminil-Copin, Sidoravicius and Tassion treat slabs (doi:10.1002/cpa.21641).
  • 2017. Fitzner and van der Hofstad reach nearest-neighbour Zd\mathbb Z^dZd, d≥11d\ge11d≥11 (arXiv:1506.07977).
  • 2021. Hermon and Hutchcroft cover certain groups of intermediate growth (arXiv:1809.11112).
  • 2024. Kozma and Nitzan reduce the Zd\mathbb Z^dZd case to a conjectured gluing inequality (arXiv:2401.12397).
  • 2026. Public, non-peer-reviewed announcements report AI-assisted proofs for Zd\mathbb Z^dZd, including a reported Lean formalization of the lattice case (Leder, August 2026).

The source of this mission is an OpenAI preprint dated September 24, 2026, which treats the remaining subexponential-growth case for all quasi-transitive graphs.

Setting

A graph here has vertex set VVV and a set EEE of labelled bonds, each with a pair of endpoints; loops and parallel bonds are allowed. It is locally finite if each vertex lies in finitely many bonds, connected if any two vertices are joined by a path, and quasi-transitive if its automorphism group (bijections of vertices and of bonds respecting endpoints) has finitely many orbits on VVV. Examples include Zd\mathbb Z^dZd and every Cayley graph of a finitely generated group.

For p∈[0,1]p\in[0,1]p∈[0,1], let Pp\mathbb P_pPp​ be the law of a random set ω⊆E\omega\subseteq Eω⊆E containing each bond independently with probability ppp. The cluster Cx(ω)C_x(\omega)Cx​(ω) of xxx is the set of vertices joined to xxx by a finite path of bonds in ω\omegaω (including xxx). Define

pc(G)=inf⁡{p∈[0,1]: Pp(∃x, ∣Cx∣=∞)>0}.p_c(G)=\inf\{p\in[0,1]:\ \mathbb P_p(\exists x,\ |C_x|=\infty)>0\}.pc​(G)=inf{p∈[0,1]: Pp​(∃x, ∣Cx​∣=∞)>0}.

Formalization targets

Goal: Theorem 1.1

For every infinite, connected, locally finite, quasi-transitive graph GGG,

pc(G)<1 ⟹ Ppc(G)(∃x∈V: ∣Cx∣=∞)=0.p_c(G)<1\ \Longrightarrow\ \mathbb P_{p_c(G)}\bigl(\exists x\in V:\ |C_x|=\infty\bigr)=0.pc​(G)<1 ⟹ Ppc​(G)​(∃x∈V: ∣Cx​∣=∞)=0.

The hypothesis pc<1p_c<1pc​<1 is necessary, since at p=1p=1p=1 every infinite connected graph percolates. The Lean statement OAI.CriticalPercolation.BondGraph.no_percolation_at_criticality is open on the platform.

Significance

Theorem 1.1 resolves the Benjamini–Schramm criticality conjecture for Bernoulli bond percolation, and in particular gives θ(pc)=0\theta(p_c)=0θ(pc​)=0 on Zd\mathbb Z^dZd for every d≥2d\ge2d≥2 (Corollary 10.1, p. 39), including the long-open case d=3d=3d=3. With Hutchcroft's exponential-growth theorem, the remaining work is the subexponential case, divided into superpolynomial growth and growth bounded by a polynomial along a sequence of scales. As a consequence the percolation probability is continuous at pcp_cpc​ on every such graph.

The result is proved in an OpenAI preprint that has not been peer reviewed. No machine-checked proof of the general quasi-transitive statement exists; the reported Lean formalization concerns only nearest-neighbour Zd\mathbb Z^dZd. A formal proof would also certify the structural input (Tessera–Tointon's finitary structure theorem) as used here.

Difficulty

The obvious strategy, renormalization from large finite boxes, needs a gluing inequality to connect independently found large clusters, and it needs coordinates: on a general quasi-transitive graph there is no lattice to place boxes in. Planar duality is unavailable, the lace expansion needs high dimension, and Hutchcroft's argument uses exponential growth essentially. In the superpolynomial case one must rule out two large distinct clusters adjacent across an edge; in the polynomial case one needs nilpotent-group coordinates (Gromov, Trofimov, Tessera–Tointon) and a two-direction corridor exploration in which conditional failure probabilities stay uniformly small. Neither uniqueness of the infinite cluster nor independence across coarse blocks can be assumed.

Formalization scope

  • BondGraph V E stores ends : E → Sym2 V; loops and parallel bonds are allowed. LocallyFinite counts bonds at each vertex. Clusters use SimpleGraph.fromEdgeSet of the open bonds, so loops are discarded and parallel bonds collapse; reachability includes the zero-length path.
  • Aut consists of a vertex bijection and a bond bijection that commute with ends; QuasiTransitive says some finite set of vertices meets every orbit.
  • law p is ProbabilityTheory.setBernoulli Set.univ p on Set E: each bond retained independently with probability p : unitInterval.
  • criticalProbability is sInf in unitInterval of {p:Pp(percolates)>0}\{p:\mathbb P_p(\text{percolates})>0\}{p:Pp​(percolates)>0}; if the set were empty this would be 111, which hpc excludes.
  • percolates is the event that some cluster is infinite; the conclusion says it has measure 000 at pcp_cpc​.

A complete development needs Bernoulli product measures, Harris–FKG and conditional correlation inequalities, Hutchcroft's exponential-growth theorem, volume-growth theory of quasi-transitive graphs, and the Gromov–Trofimov–Tessera–Tointon structure theory. Each of these is reusable well beyond this mission. Contributions formalizing Proposition 3.1 (two-cluster bound), Theorem 6.1 (joint gluing), Theorem 7.1 (criticality with a nilpotent quotient action) or Proposition 9.3 (subcritical exploration), or the cited Theorem 2.2 (Hutchcroft), are welcome.

Selected references

  • OpenAI, No percolation at criticality on quasi-transitive graphs, preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/No-percolation-at-criticality-on-quasi-transitive-graphs-September-24-2026/paper.pdf
  • I. Benjamini, O. Schramm, Percolation beyond Zd\mathbb Z^dZd, many questions and a few answers, Electron. Commun. Probab., 1996. https://doi.org/10.1214/ECP.v1-978
  • I. Benjamini, R. Lyons, Y. Peres, O. Schramm, Critical percolation on any nonamenable group has no infinite clusters, Ann. Probab., 1999. https://doi.org/10.1214/aop/1022677450
  • T. Hutchcroft, Critical percolation on any quasi-transitive graph of exponential growth has no infinite clusters, C. R. Math., 2016. https://doi.org/10.1016/j.crma.2016.07.013
  • J. Hermon, T. Hutchcroft, No percolation at criticality on certain groups of intermediate growth, IMRN, 2021. https://doi.org/10.1093/imrn/rnz265
  • R. Tessera, M. C. H. Tointon, A finitary structure theorem for vertex-transitive graphs of polynomial growth, Combinatorica, 2021. https://doi.org/10.1007/s00493-020-4295-6
  • G. Kozma, S. Nitzan, A reduction of the θ(p_c)=0 problem to a conjectured inequality, preprint, 2024. https://arxiv.org/abs/2401.12397
  • T. Hara, G. Slade, Mean-field critical behaviour for percolation in high dimensions, Comm. Math. Phys., 1990. https://doi.org/10.1007/BF02108785
  • H. Kesten, The critical probability of bond percolation on the square lattice equals 1/2, Comm. Math. Phys., 1980. https://doi.org/10.1007/BF01197577
  • Á. Timár, Percolation on nonunimodular transitive graphs, Ann. Probab., 2006. https://doi.org/10.1214/009117906000000494
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CombinatoricsGroup TheoryRepresentation Theory·Captain: wurtle

Universal Tensor Squares for Symmetric GroupsResearch Paper

Motivation: tensor squares that contain everything

The irreducible complex representations of the symmetric group SnS_nSn​ are indexed by partitions λ⊢n\lambda\vdash nλ⊢n and written SλS^\lambdaSλ (Specht modules). Decomposing a tensor product Sλ⊗SμS^\lambda\otimes S^\muSλ⊗Sμ (with the diagonal action) into irreducibles is governed by the Kronecker coefficients

g(λ,μ,ν)=dim⁡Hom⁡Sn ⁣(Sν, Sλ⊗Sμ),g(\lambda,\mu,\nu)=\dim\operatorname{Hom}_{S_n}\!\big(S^\nu,\,S^\lambda\otimes S^\mu\big),g(λ,μ,ν)=dimHomSn​​(Sν,Sλ⊗Sμ),

which have no known positive combinatorial formula and are a central open topic in algebraic combinatorics and geometric complexity theory. A basic test of how far tensor products spread is the tensor square conjecture: in every degree (with a few exceptions) some single irreducible representation has a tensor square containing every irreducible representation.

Timeline

  • 2012 — Saxl proposes (UCLA Combinatorics Seminar, 20 March 2012) that the staircase ρm=(m,m−1,…,1)\rho_m=(m,m-1,\dots,1)ρm​=(m,m−1,…,1) at triangular degree n=m(m+1)/2n=m(m+1)/2n=m(m+1)/2 has this property; recorded by Pak, Panova and Vallejo.
  • 2013 — Heide, Saxl, Tiep and Zalesski conjecture an analogous covering property for alternating simple groups (HSTZ 2013, Rem. 1.3(3)).
  • 2013/2016 — Pak, Panova and Vallejo state the tensor square conjecture for n≥3n\ge3n≥3, n≠4,9n\ne4,9n=4,9, and the Saxl conjecture, and give a character-nonvanishing criterion (PPV 2016, Conj. 1.1–1.2, Lemma 1.3).
  • 2015 — Ikenmeyer proves the staircase assertion for all ν\nuν comparable with the staircase in dominance order (Ikenmeyer 2015, Thm 2.1).
  • 2017 — Luo and Sellke: staircase squares contain almost all partitions, and fourth powers cover everything in large degree (Luo–Sellke 2017).
  • 2018 — Bessenrodt: all double-hook constituents (Bessenrodt 2018, Thm 4.10).
  • 2021 — Li: the triple-hook case, and a conjectured family for nontriangular degrees (Li 2021).
  • 2023 — Harman and Ryba: staircase tensor cubes contain everything (Harman–Ryba 2023, Thm 1.1).
  • 2026 — An OpenAI preprint, Universal Tensor Squares for Symmetric Groups (OpenAI Math Release, September 24, 2026), claims the tensor square conjecture for every n∉{2,4,9}n\notin\{2,4,9\}n∈/{2,4,9}, building on a companion preprint that proves Saxl's conjecture in a cyclic form. Neither preprint is peer reviewed and the main theorem is not formally verified; its proof uses exact computer calculations for degrees up to 646464.

Setting

For a Young diagram λ\lambdaλ with nnn cells, fix a bijection ttt between {0,…,n−1}\{0,\dots,n-1\}{0,…,n−1} and the cells (a tableau). The Lean development realizes SλS^\lambdaSλ inside the space of functions on words Fin n→Fin d\mathrm{Fin}\,n\to\mathrm{Fin}\,dFinn→Find (ddd = number of rows), with SnS_nSn​ acting by permuting letters: the polytabloid ete_tet​ is the signed sum, over the column group of ttt, of the permuted indicator of the row word of ttt, and Specht t is the C\mathbb CC-span of the SnS_nSn​-orbit of ete_tet​. The Kronecker coefficient kronecker a b t is finrank of the space of SnS_nSn​-intertwining maps from spechtRep t to the tensor product spechtRep a ⊗ spechtRep b. canonicalTableau λ is a fixed tableau of shape λ\lambdaλ.

Formalization targets

Goal: a universal tensor square in every degree n∉{2,4,9}n\notin\{2,4,9\}n∈/{2,4,9}

For every positive integer nnn with n≠2,4,9n\ne2,4,9n=2,4,9 there is a partition λ⊢n\lambda\vdash nλ⊢n such that

g(λ,λ,ν)>0for every ν⊢n,g(\lambda,\lambda,\nu)>0\qquad\text{for every }\nu\vdash n ,g(λ,λ,ν)>0for every ν⊢n,

so the tensor square Sλ⊗SλS^\lambda\otimes S^\lambdaSλ⊗Sλ contains every irreducible representation of SnS_nSn​. The Lean statement asserts, for one Young diagram λ\lambdaλ with nnn cells: positivity of kronecker for every target diagram ν\nuν with nnn cells; irreducibility of spechtRep for λ\lambdaλ; and that every finite-dimensional irreducible complex representation of SnS_nSn​ admits an injective intertwining map into Sλ⊗SλS^\lambda\otimes S^\lambdaSλ⊗Sλ. This is Theorem 1.1 of the source together with its "thus" sentence. The goal is published on the platform with status Open.

Significance

The result itself. The theorem settles the tensor square conjecture of Pak, Panova and Vallejo, including all nontriangular degrees. Since the sign representation occurs, the chosen λ\lambdaλ must be self-conjugate. Via Letellier's results it also gives tensor squares of unipotent characters of GLn(Fq)\mathrm{GL}_n(\mathbb F_q)GLn​(Fq​) that contain every unipotent character (Corollary 1.2 of the source). The companion cyclic Saxl theorem is used as input at staircase degrees.

Formalizing it. The proof combines representation-theoretic constructions with exact finite computations (Murnaghan–Nakayama recursion for all eligible n≤64n\le64n≤64, and capacity calculations), which the source certifies by included programs. A formal proof would replace these with checked computation and would require building Specht modules, Kronecker coefficients and branching rules in Lean, infrastructure Mathlib currently lacks.

Difficulty

Knowing that a smaller staircase square contains every constituent does not transfer to an enlarged diagram: constituents must survive a map from the actual enlarged tensor square, and the attached cells introduce alternating terms that can cancel. Almost-all or higher-power results (Luo–Sellke, Harman–Ryba) do not give a universal square. Small degrees cannot be handled by asymptotic estimates and require exact Kronecker computations, and n=2,4,9n=2,4,9n=2,4,9 are genuine exceptions.

Formalization scope

  • Partitions are Mathlib YoungDiagrams with card = n; the representation space is (Fin n → Fin d) → ℂ, and Specht t is the cyclic span of one polytabloid. kronecker is Module.finrank ℂ of Representation.IntertwiningMap, so it is the genuine multiplicity of SνS^\nuSν in the tensor square.
  • The third conjunct quantifies over all finite-dimensional complex irreducible representations in a fixed universe; proving it requires that every irreducible of SnS_nSn​ is isomorphic to some Specht module. The second conjunct (irreducibility of SλS^\lambdaSλ) is a standard fact the source uses implicitly.
  • n>0n>0n>0 and n≠2,4,9n\ne2,4,9n=2,4,9 match the source; n=1n=1n=1 is trivial.
  • Needed infrastructure: Specht module theory (irreducibility, completeness), characters and Murnaghan–Nakayama, Littlewood–Richardson branching, verified computation for n≤64n\le 64n≤64. The Specht and Kronecker layer is shared with the companion Saxl mission.

Selected references

  • I. Pak, G. Panova and E. Vallejo, Kronecker products, characters, partitions, and the tensor square conjectures, Adv. Math. 288 (2016), 702–731. https://doi.org/10.1016/j.aim.2015.11.002
  • G. Heide, J. Saxl, P. H. Tiep and A. E. Zalesski, Conjugacy action, induced representations and the Steinberg square for simple groups of Lie type, Proc. London Math. Soc. 106 (2013), 908–930. https://doi.org/10.1112/plms/pds062
  • C. Ikenmeyer, The Saxl conjecture and the dominance order, Discrete Math. 338 (2015), 1970–1975. https://doi.org/10.1016/j.disc.2015.04.027
  • S. Luo and M. Sellke, The Saxl conjecture for fourth powers via the semigroup property, J. Algebraic Combin. 45 (2017), 33–80. https://arxiv.org/abs/1511.02387v2
  • C. Bessenrodt, Critical classes, Kronecker products of spin characters, and the Saxl conjecture, Algebr. Comb. 1 (2018), 353–369. https://doi.org/10.5802/alco.18
  • X. Li, Saxl Conjecture for triple hooks, Discrete Math. 344 (2021), 112340. https://doi.org/10.1016/j.disc.2021.112340
  • N. Harman and C. Ryba, A tensor-cube version of the Saxl conjecture, Algebr. Comb. 6 (2023), 507–511. https://doi.org/10.5802/alco.267
  • E. Letellier, Tensor products of unipotent characters of general linear groups over finite fields, Transform. Groups 18 (2013), 233–262. https://doi.org/10.1007/s00031-013-9211-3
  • OpenAI, A Cyclic Polytabloid Proof of Saxl's Conjecture, OpenAI Math Release preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/A-Cyclic-Polytabloid-Proof-of-Saxls-Conjecture-September-24-2026/paper.pdf
  • OpenAI, Universal Tensor Squares for Symmetric Groups, OpenAI Math Release preprint, September 24, 2026 (Theorem 1.1, p. 2). https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Universal-Tensor-Squares-for-Symmetric-Groups-September-24-2026/main.pdf
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AlgebraRepresentation Theory·Captain: wurtle

A counterexample to Tachikawa's second conjectureResearch Paper

Motivation

Let AAA be a finite-dimensional algebra over a field. A finite-dimensional module MMM is self-orthogonal if Ext⁡Ai(M,M)=0\operatorname{Ext}^i_A(M,M)=0ExtAi​(M,M)=0 for every i>0i>0i>0. Tachikawa's second conjecture asserts that over a self-injective algebra every self-orthogonal module is projective. It belongs to the cluster of homological conjectures around quasi-Frobenius algebras and dominant dimension recorded in Tachikawa's 1973 monograph, alongside the Nakayama, generalized Nakayama and Auslander–Reiten conjectures. Since Ext⁡Ai(M,A)=0\operatorname{Ext}^i_A(M,A)=0ExtAi​(M,A)=0 automatically when AAA is self-injective, it is exactly the self-injective case of the Auslander–Reiten conjecture. Enomoto and Marczinzik showed that Tachikawa's second conjecture for two-fold trivial extensions implies the Auslander–Reiten conjecture, so the two were expected to stand or fall together.

This mission formalizes the main theorem of an OpenAI preprint dated September 23, 2026 (source), which claims a counterexample over a symmetric algebra. The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open (stated, not yet proved).

Background

  • 1975 — Auslander and Reiten formulate the conjecture in their work on the generalized Nakayama conjecture (Proc. AMS 1975).
  • 1993 — Auslander, Ding and Solberg prove it for complete local complete intersections (J. Algebra 1993).
  • 1994–1995 — Schulz constructs a nonprojective module without self-extensions over a ring outside the Artin-algebra setting (Arch. Math. 1994) and quantum-exterior-algebra modules with eventually vanishing self-extensions (J. Aust. Math. Soc. 1995).
  • 2004 — Huneke and Leuschke prove the conjecture for excellent Cohen–Macaulay normal domains containing Q\mathbb QQ (J. Algebra 2004); Huneke, Şega and Vraciu treat commutative Artinian local rings with radical cube zero (Illinois J. Math. 2004).
  • 2008 — Luo and Huang record the Gorenstein-projective variant (J. Algebra 2008).
  • 2010 — Christensen and Holm show that rings satisfying Auslander's condition satisfy the conjecture (Math. Z. 2010).
  • 2017–2022 — Erdmann's analysis of weakly symmetric algebras with J3=0J^3=0J3=0 (J. Aust. Math. Soc. 2017); Ringel and Zhang on short local algebras (J. LMS 2022).
  • 2026 — Zhang and Zhou prove the conjecture for split algebras with radical cube zero (arXiv:2609.08679) and for Igusa–Todorov algebras (arXiv:2609.14453); Xia for quantum complete intersections (arXiv:2609.24007); Enomoto shows that Tachikawa's second conjecture for two-fold trivial extensions implies the Auslander–Reiten conjecture (arXiv:2609.19172).
  • September 2026 — Two OpenAI preprints claim counterexamples: an explicit eight-simple Gorenstein-projective example (source) and a symmetric-algebra counterexample to Tachikawa's second conjecture (source).

Setting

Let k=F2(q,H1,H2)k=\mathbb F_2(q,H_1,H_2)k=F2​(q,H1​,H2​) be the rational function field in three algebraically independent variables. Write D=Hom⁡k(−,k)D=\operatorname{Hom}_k(-,k)D=Homk​(−,k). A finite-dimensional kkk-algebra AAA is symmetric if A≅DAA\cong DAA≅DA as AAA-bimodules, where (a⋅f⋅b)(c)=f(bca)(a\cdot f\cdot b)(c)=f(bca)(a⋅f⋅b)(c)=f(bca); equivalently there is a kkk-linear isomorphism e:A→DAe:A\to DAe:A→DA with e(ab)(c)=e(b)(ca)=e(a)(bc)e(ab)(c)=e(b)(ca)=e(a)(bc)e(ab)(c)=e(b)(ca)=e(a)(bc). Symmetric algebras are self-injective. For a finite-dimensional left AAA-module MMM, Ext⁡Ai(M,M)\operatorname{Ext}^i_A(M,M)ExtAi​(M,M) is computed in the category of left AAA-modules.

Formalization targets

Goal: Theorem 1.1 (p. 1)

There exist a finite-dimensional associative unital symmetric kkk-algebra AAA and a finite-dimensional unital left AAA-module MMM such that MMM is not projective and

Ext⁡Ai(M,M)=0for every integer i>0.\operatorname{Ext}^i_A(M,M)=0\qquad\text{for every integer }i>0.ExtAi​(M,M)=0for every integer i>0.

In particular Tachikawa's second conjecture is false, and since AAA is self-injective the same pair is a symmetric counterexample to the Auslander–Reiten conjecture.

Significance

The result itself. The theorem disproves Tachikawa's second conjecture in the strongest natural class (symmetric algebras). Through an associated endomorphism algebra, the preprint further claims counterexamples to the classical, generalized and strong Nakayama conjectures, the Auslander–Gorenstein conjecture and the Wakamatsu tilting conjecture, persisting under every field extension (Corollary 1.2, p. 4), together with consequences for finitistic dimensions (Corollary 1.3, p. 5). Those corollaries are not part of this mission's goal. A companion preprint gives an explicit eight-simple triangular counterexample to the Auslander–Reiten conjecture.

Formalizing it. The statement uses only notions already in Mathlib (finite-dimensional algebras, projective modules, Abelian.Ext in ModuleCat), so it is a clean target. The proof needs trivial extensions Λ⋉DΛ\Lambda\ltimes D\LambdaΛ⋉DΛ, totally acyclic complexes, stable categories of symmetric algebras, Koszul computations of Ext⁡\operatorname{Ext}Ext-algebras, and Hochschild bar complexes; these are reusable throughout representation theory.

Difficulty

Known non-examples come close but fail: Schulz's quantum exterior modules have vanishing higher self-extensions but nonzero Ext⁡1\operatorname{Ext}^1Ext1; Böhmler and Marczinzik have a commutative self-injective example with only Ext⁡1=Ext⁡2=0\operatorname{Ext}^1=\operatorname{Ext}^2=0Ext1=Ext2=0. Vanishing in every positive degree requires controlling an infinite resolution. In the preprint, the passage to a symmetric algebra also requires vanishing of the negative part of a complete self-extension complex (condition (1.1), p. 2), which is what the transfer through the trivial extension A=Λ⋉DΛA=\Lambda\ltimes D\LambdaA=Λ⋉DΛ, M=A⊗ΛZM=A\otimes_\Lambda ZM=A⊗Λ​Z (Proposition 3.2, p. 12) consumes. Producing it needs two twists H1,H2H_1,H_2H1​,H2​ acting by distinct scalars on every nonzero homogeneous self-map space, and genuine bimodule lifts of stable maps (Proposition 6.1, p. 21).

Formalization scope

  • The field is FractionRing (MvPolynomial (Fin 3) (ZMod 2)), i.e. F2(q,H1,H2)\mathbb F_2(q,H_1,H_2)F2​(q,H1​,H2​) with independent variables.
  • SymmetricOver k A asks for a kkk-linear equivalence e : A ≃ₗ[k] Module.Dual k A with e (a*b) c = e b (c*a) and e (a*b) c = e a (b*c), i.e. a bimodule isomorphism A≅DAA\cong DAA≅DA.
  • Counterexample asserts existence of A : Type with Ring and Algebra k structures, Module.Finite k A, symmetric; and M with compatible Module A, Module k (IsScalarTower), Module.Finite k M, ¬ Module.Projective A M, and Subsingleton (Abelian.Ext (ModuleCat.of A M) (ModuleCat.of A M) n) for all n > 0.
  • The nonprojectivity clause rules out M=0M=0M=0 and semisimple AAA, so the statement is not trivially satisfiable.

Selected references

  • OpenAI, A counterexample to Tachikawa's second conjecture, OpenAI Math Release preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/A-counterexample-to-Tachikawas-second-conjecture-September-23-2026/paper.pdf
  • OpenAI, An explicit counterexample to the Auslander–Reiten conjecture, OpenAI Math Release preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/An-explicit-counterexample-to-the-Auslander-Reiten-conjecture-September-23-2026/paper.pdf
  • H. Tachikawa, Quasi-Frobenius Rings and Generalizations, Lecture Notes in Mathematics 351, Springer, 1973.
  • K. Diveris, M. Purin, Vanishing of self-extensions over symmetric algebras, J. Pure Appl. Algebra (2014). https://doi.org/10.1016/j.jpaa.2013.10.012
  • K. Erdmann, Ext-finite modules for weakly symmetric algebras with radical cube zero, J. Aust. Math. Soc. (2017). https://doi.org/10.1017/S1446788716000331
  • M. Auslander, I. Reiten, On a generalized version of the Nakayama conjecture, Proc. Amer. Math. Soc. 52 (1975). https://doi.org/10.1090/S0002-9939-1975-0389977-6
  • M. Auslander, S. Ding, Ø. Solberg, Liftings and weak liftings of modules, J. Algebra (1993). https://doi.org/10.1006/jabr.1993.1076
  • C. Huneke, G. J. Leuschke, On a conjecture of Auslander and Reiten, J. Algebra (2004). https://doi.org/10.1016/j.jalgebra.2003.07.018
  • L. W. Christensen, H. Holm, Algebras that satisfy Auslander's condition on vanishing of cohomology, Math. Z. (2010). https://doi.org/10.1007/s00209-009-0500-4
  • R. Luo, Z. Huang, When are torsionless modules projective?, J. Algebra (2008). https://doi.org/10.1016/j.jalgebra.2008.04.027
  • R. Schulz, A non-projective module without self-extensions, Arch. Math. (1994). https://doi.org/10.1007/BF01193735
  • H. Enomoto, Tachikawa's second conjecture implies the Auslander–Reiten conjecture, arXiv:2609.19172 (2026). https://arxiv.org/abs/2609.19172v1
  • X. Zhang, P. Zhou, The Auslander–Reiten conjecture for algebras with radical cube zero, arXiv:2609.08679 (2026). https://arxiv.org/abs/2609.08679v1
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AlgebraCategory TheoryRepresentation Theory·Captain: wurtle

An algebra of infinite little finitistic dimensionResearch Paper

Motivation

Every module over a ring has a projective resolution, and its length, the projective dimension, measures how far the module is from being projective. Over a finite-dimensional algebra, some modules have infinite projective dimension; the finitistic dimension conjectures ask whether, among the modules whose projective dimension is finite, these finite values are bounded. The questions were publicized by Bass in 1960 and became central problems in the representation theory of finite-dimensional algebras, tied to many other homological conjectures (Nakayama, Gorenstein symmetry, Auslander–Reiten), each of which would follow from finiteness of the finitistic dimension.

Timeline

  • 1960. Bass publicizes the finitistic-dimension questions (doi:10.1090/S0002-9947-1960-0157984-8).
  • 1991. Green, Kirkman and Kuzmanovich prove little finitistic finiteness for finite-dimensional monomial algebras (doi:10.1016/0021-8693(91)90062-D); Green and Huisgen-Zimmermann treat Artin algebras with radical cube zero.
  • 1992. Huisgen-Zimmermann constructs monomial algebras whose little and big finitistic dimensions differ (nnn versus n+1n+1n+1), so the two invariants are not equal in general (doi:10.1007/BF02100610).
  • 1995. Huisgen-Zimmermann's survey "a tale of 3.5 decades" describes the state of the problems (doi:10.1007/978-94-011-0443-2_41).
  • 2005. Igusa and Todorov introduce syzygy invariants and prove finiteness for Artin algebras of representation dimension at most three.
  • 2019. Rickard shows that if injective modules generate the unbounded derived category, then the big finitistic dimension is finite (doi:10.1016/j.aim.2019.106735).
  • 2024. Cummings proves that universal little finitistic finiteness is equivalent to its left–right symmetry (doi:10.1112/blms.12954).

The source of this mission is an OpenAI preprint dated September 23, 2026, which constructs a counterexample to the little finitistic-dimension conjecture over C\mathbb CC. A companion OpenAI preprint independently gives a characteristic-two example.

Setting

Let AAA be a finite-dimensional unital algebra over C\mathbb CC. For a left AAA-module NNN, pd⁡AN∈{0,1,2,… }∪{∞}\operatorname{pd}_AN\in\{0,1,2,\dots\}\cup\{\infty\}pdA​N∈{0,1,2,…}∪{∞} is the minimal length of a projective resolution. The little finitistic dimension is

findim⁡A=sup⁡{pd⁡AN: N finitely generated left A-module, pd⁡AN<∞},\operatorname{findim}A=\sup\{\operatorname{pd}_AN:\ N\ \text{finitely generated left } A\text{-module},\ \operatorname{pd}_AN<\infty\},findimA=sup{pdA​N: N finitely generated left A-module, pdA​N<∞},

and the big finitistic dimension Findim⁡A\operatorname{Findim}AFindimA is the same supremum over all left modules. Over a finite-dimensional algebra, finitely generated and finite-dimensional modules coincide. The little finitistic-dimension conjecture asserts findim⁡A<∞\operatorname{findim}A<\inftyfindimA<∞ for every such AAA.

Injective left modules generate if the smallest triangulated subcategory of the unbounded derived category of left modules that contains them and is closed under arbitrary coproducts is the whole category.

Formalization targets

Goal: Theorem 1.1

∃ A finite-dimensional over C:∀m≥1 ∃Nm finitely generated with 2m−2≤pd⁡ANm<∞; hence findim⁡A=∞.\exists\ A\ \text{finite-dimensional over } \mathbb C:\quad \forall m\ge1\ \exists N_m\ \text{finitely generated with}\ 2m-2\le\operatorname{pd}_AN_m<\infty;\ \text{hence}\ \operatorname{findim}A=\infty.∃ A finite-dimensional over C:∀m≥1 ∃Nm​ finitely generated with 2m−2≤pdA​Nm​<∞; hence findimA=∞.

The Lean statement OAI.LittleFinitistic.Main.exists_counterexample is open on the platform.

Milestone: Corollary 1.2

∃ Λ: findim⁡Λ=Findim⁡Λ=∞,findim⁡Λop=Findim⁡Λop=0,\exists\ \Lambda:\ \operatorname{findim}\Lambda=\operatorname{Findim}\Lambda=\infty,\quad \operatorname{findim}\Lambda^{\rm op}=\operatorname{Findim}\Lambda^{\rm op}=0,∃ Λ: findimΛ=FindimΛ=∞,findimΛop=FindimΛop=0,

injective left Λ\LambdaΛ-modules do not generate, and injective right Λ\LambdaΛ-modules do.

Significance

Theorem 1.1 refutes the little finitistic-dimension conjecture: a single finite-dimensional algebra has finitely generated modules with terminating projective resolutions of unbounded length. Since findim⁡≤Findim⁡\operatorname{findim}\le\operatorname{Findim}findim≤Findim, it also refutes the big version for Artin algebras, and by Rickard's theorem gives an algebra for which injectives do not generate the derived category. Corollary 1.2 shows that finiteness can fail on one side and hold on the other in the most extreme way, in contrast with Cummings' equivalence of universal finiteness and left–right symmetry.

The result is proved in an OpenAI preprint; it has not been peer reviewed, and no machine-checked proof exists. Because many homological conjectures were known to follow from the finitistic-dimension conjecture, an independent check of a counterexample is of direct interest.

Difficulty

Positive results cover large classes (monomial, radical cube zero, representation dimension at most three), so a counterexample must escape all of them. The finitistic dimension of one fixed algebra must be unbounded, which requires encoding an unbounded family of terminating processes into fixed finite data. The paper realizes a "selection process" from an infinite finitely presented group algebra by a single bounded bimodule complex over a finite-dimensional algebra; making this realization uniform in the input module, and turning it into ordinary projective resolutions over a trivial extension, are the main obstacles.

Formalization scope

  • Algebras are A : Type with [Ring A] [Algebra ℂ A] and FiniteDimensional ℂ A; modules are objects of ModuleCat.{0} A, finiteness is Module.Finite A N.
  • projectiveDimension is Mathlib's CategoryTheory.projectiveDimension, valued in WithBot ℕ∞, equal to ⊤ for infinite dimension; littleFinitisticDimension and bigFinitisticDimension are suprema over modules of finite projective dimension.
  • Right modules are left modules over Aᵐᵒᵖ.
  • InjectivesGenerate uses HasDerivedCategory.standard and quantifies over isomorphism-closed triangulated properties closed under coproducts indexed by Type u and containing all injectives in degree 0.

A complete development needs derived tensor products of bimodule complexes, trivial extension algebras D⋉XD\ltimes XD⋉X and their bar resolutions, finite presentations of group algebras and their finite-dimensional representations, and (for the milestone) Rickard's theorem and Cummings' triangular algebra. Contributions formalizing Proposition 2.1 (selection), Theorem 4.1 (tensor realization) and Proposition 6.1 (bar splitting) are welcome.

Selected references

  • OpenAI, An algebra of infinite little finitistic dimension, preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/An-algebra-of-infinite-little-finitistic-dimension-September-23-2026/paper.pdf
  • H. Bass, Finitistic dimension and a homological generalization of semi-primary rings, Trans. Amer. Math. Soc., 1960. https://doi.org/10.1090/S0002-9947-1960-0157984-8
  • B. Zimmermann Huisgen, Homological domino effects and the first Finitistic Dimension Conjecture, Invent. Math., 1992. https://doi.org/10.1007/BF02100610
  • B. Zimmermann Huisgen, The finitistic dimension conjectures — a tale of 3.5 decades, in Abelian Groups and Modules, 1995. https://doi.org/10.1007/978-94-011-0443-2_41
  • E. L. Green, E. E. Kirkman, J. Kuzmanovich, Finitistic dimensions of finite dimensional monomial algebras, J. Algebra, 1991. https://doi.org/10.1016/0021-8693(91)90062-D
  • K. Igusa, G. Todorov, On the finitistic global dimension conjecture for Artin algebras, in Representations of Algebras and Related Topics, 2005.
  • J. Rickard, Unbounded derived categories and the finitistic dimension conjecture, Adv. Math., 2019. https://doi.org/10.1016/j.aim.2019.106735
  • C. Cummings, Left–right symmetry of finite finitistic dimension, Bull. London Math. Soc., 2024. https://doi.org/10.1112/blms.12954
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CombinatoricsGraph Theory·Captain: wurtle

Cycle--clique Ramsey numbersResearch Paper

Motivation

The Ramsey number R(H,J)R(H,J)R(H,J) is the least NNN such that every red–blue colouring of the edges of KNK_NKN​ contains a red copy of HHH or a blue copy of JJJ. For a cycle CmC_mCm​ against a clique KnK_nKn​ there is a simple lower-bound construction: take n−1n-1n−1 disjoint red cliques of order m−1m-1m−1 with all edges between them blue. It has (m−1)(n−1)(m-1)(n-1)(m−1)(n−1) vertices, no red CmC_mCm​ (each red component is too small) and no blue KnK_nKn​ (a blue clique meets each red clique at most once). In 1978 Erdős, Faudree, Rousseau and Schelp conjectured that this construction is optimal whenever m≥n≥3m\ge n\ge3m≥n≥3, apart from R(C3,K3)=6R(C_3,K_3)=6R(C3​,K3​)=6. Cycle–complete Ramsey numbers are among the few families where exact values are expected over a whole parameter range, and the conjecture has been attacked case by case for decades.

This mission asks for a formal proof of the full conjecture as stated in an OpenAI preprint dated September 25, 2026 (source). The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open. Part of the proof is a finite case analysis of 3,099 parameter-pattern instances carried out by two exact programs that accompany the paper.

Background

  • 1971 — Chartrand and Schuster settle n=3n=3n=3 (Bull. AMS 1971).
  • 1973 — Bondy and Erdős prove the formula for m≥n2−2m\ge n^2-2m≥n2−2 (JCTB 1973).
  • 1978 — Erdős, Faudree, Rousseau and Schelp state the conjecture (J. Graph Theory 1978).
  • 1999–2008 — The full range for n=4n=4n=4 (Yang–Huang–Zhang, Australas. J. Combin. 1999), n=5n=5n=5 (Bollobás et al., Australas. J. Combin. 2000), n=6n=6n=6 (Schiermeyer, JGT 2003), n=7n=7n=7 (Chen–Cheng–Zhang, Eur. J. Combin. 2008).
  • 2005 — Nikiforov proves the formula for n≥4n\ge4n≥4, m≥4n+2m\ge4n+2m≥4n+2 (CPC 2005).
  • 2007–2023 — Partial results for n=8n=8n=8, including R(C8,K8)R(C_8,K_8)R(C8​,K8​) (Discrete Math. 2009) and R(C9,K8)R(C_9,K_8)R(C9​,K8​) (ISRN Algebra 2011).
  • 2021 — Keevash, Long and Skokan prove the formula for m≥Clog⁡n/log⁡log⁡nm\ge C\log n/\log\log nm≥Clogn/loglogn, leaving finitely many open pairs (IMRN 2021).
  • September 2026 — The OpenAI preprint claims all remaining pairs (Theorem 1.1, p. 2).

Setting

All graphs are finite and simple. CmC_mCm​ is the cycle on exactly mmm vertices and KnK_nKn​ the complete graph on nnn vertices. Identify a red–blue colouring of KNK_NKN​ with its red graph GGG on NNN vertices; the blue graph is the complement G‾\overline GG. A copy of HHH in GGG is an injective homomorphism H→GH\to GH→G (copies need not be induced). The Ramsey property R(m,n,N)\mathcal R(m,n,N)R(m,n,N) says: every graph GGG on NNN vertices contains CmC_mCm​ or its complement contains KnK_nKn​. Then

R(Cm,Kn)=min⁡{N: R(m,n,N)}.R(C_m,K_n)=\min\{N:\ \mathcal R(m,n,N)\}.R(Cm​,Kn​)=min{N: R(m,n,N)}.

Formalization targets

Goal: Theorem 1.1 (p. 2)

For all integers m≥n≥3m\ge n\ge 3m≥n≥3 with (m,n)≠(3,3)(m,n)\ne(3,3)(m,n)=(3,3),

R(Cm,Kn)=(m−1)(n−1)+1,R(C_m,K_n)=(m-1)(n-1)+1,R(Cm​,Kn​)=(m−1)(n−1)+1,

and R(C3,K3)=6R(C_3,K_3)=6R(C3​,K3​)=6.

Significance

The result itself. It completes a programme that over five decades settled the conjecture for n≤7n\le7n≤7, for long cycles, and for all sufficiently large nnn by Keevash–Long–Skokan; the preprint's contribution is the remaining finite but large set of pairs. Since Keevash–Long–Skokan's constant is not explicit, the preprint gives a structural reduction that does not need a numerical value of it.

Formalizing it. The statement uses only Mathlib's cycleGraph, complete graphs, complements and subgraph containment. A formal proof would have to certify both the structural lemmas (independent-set expansion, a large-clique lemma, optimal path systems) and the finite verification of 3,099 pattern instances, which the preprint currently delegates to two programs with deduction traces. Bringing such a computation inside a proof assistant is itself a worthwhile contribution. Neither R(Cm,Kn)R(C_m,K_n)R(Cm​,Kn​) for general parameters nor any of the previously known infinite families has a machine-checked proof.

Difficulty

The lower bound is the explicit construction above; the work is the upper bound for exact cycle length mmm. Methods that find long cycles (Pósa rotation, Chvátal–Erdős) produce cycles of length at least mmm, not exactly mmm, and forbidding exactly one cycle length gives weak structural information. The preprint passes to a minimal counterexample with independent-set expansion (Lemma 2.2, p. 4), finds a clique of order max⁡{3,⌊k/2⌋}\max\{3,\lfloor k/2\rfloor\}max{3,⌊k/2⌋} with k=m−1k=m-1k=m−1 (Theorem 3.1, p. 5), and optimizes systems of paths through that clique so that every improvement would close a cycle of order exactly mmm. Cliques of order t≥9t\ge9t≥9 are excluded by hand (Proposition 8.4, p. 24); the remaining 3≤t≤83\le t\le83≤t≤8, 5≤k≤175\le k\le175≤k≤17 need the finite verification of Section 9 (Proposition 9.1, p. 26).

Formalization scope

  • RamseyProperty m n N: for every G : SimpleGraph (Fin N), cycleGraph m ⊑ G or (⊤ : SimpleGraph (Fin n)) ⊑ Gᶜ, where ⊑ is Mathlib's subgraph containment (injective homomorphism, non-induced).
  • cycleCliqueRamsey m n = sInf {N | RamseyProperty m n N} in ℕ. If the set were empty, sInf would return 0; the goal's equalities exclude that, so a proof must exhibit the Ramsey property.
  • The goal quantifies over integers m,nm,nm,n with 3≤n≤m3\le n\le m3≤n≤m, (m,n)≠(3,3)(m,n)\ne(3,3)(m,n)=(3,3), converting with toNat (harmless since both are at least 333), and separately states cycleCliqueRamsey 3 3 = 6.
  • cycleGraph m on Fin m is the mmm-cycle for m≥3m\ge3m≥3.

Welcome contributions: a verified checker for the pattern enumeration of Section 9, and the classical small cases (n=3n=3n=3, Bondy–Erdős for m≥n2−2m\ge n^2-2m≥n2−2).

Selected references

  • OpenAI, Cycle–clique Ramsey numbers, OpenAI Math Release preprint, September 25, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Cycle-clique-Ramsey-numbers-September-25-2026/Cycle-clique-Ramsey-numbers-September-25-2026.pdf
  • P. Erdős, R. J. Faudree, C. C. Rousseau, R. H. Schelp, On cycle–complete graph Ramsey numbers, J. Graph Theory 2 (1978). https://doi.org/10.1002/jgt.3190020107
  • J. A. Bondy, P. Erdős, Ramsey numbers for cycles in graphs, J. Combin. Theory Ser. B (1973). https://doi.org/10.1016/S0095-8956(73)80005-X
  • G. Chartrand, S. Schuster, On the existence of specified cycles in complementary graphs, Bull. Amer. Math. Soc. (1971). https://doi.org/10.1090/S0002-9904-1971-12832-X
  • I. Schiermeyer, All cycle-complete graph Ramsey numbers r(C_m,K_6), J. Graph Theory (2003). https://doi.org/10.1002/jgt.10145
  • V. Nikiforov, The cycle-complete graph Ramsey numbers, Combin. Probab. Comput. (2005). https://doi.org/10.1017/S096354830400642X
  • Y. Chen, T. C. E. Cheng, Y. Zhang, The Ramsey numbers R(C_m,K_7) and R(C_7,K_8), European J. Combin. (2008). https://doi.org/10.1016/j.ejc.2007.05.007
  • P. Keevash, E. Long, J. Skokan, Cycle-complete Ramsey numbers, Int. Math. Res. Not. (2021). https://doi.org/10.1093/imrn/rnz119
  • V. Chvátal, P. Erdős, A note on Hamiltonian circuits, Discrete Math. (1972). https://doi.org/10.1016/0012-365X(72)90079-9
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CombinatoricsGraph TheoryTheoretical Computer Science·Captain: wurtle

A polynomial-time construction of strong thin treesResearch Paper

Motivation

A thin spanning tree of a graph is a spanning tree that uses only a small fraction of the edges of every cut. Thin trees were introduced for the asymmetric traveling salesman problem: Asadpour, Goemans, Mądry, Oveis Gharan and Saberi showed that a thin tree of low cost can be augmented into a cheap tour, giving an O(log⁡n/log⁡log⁡n)O(\log n/\log\log n)O(logn/loglogn)-approximation (doi:10.1287/opre.2017.1603). Goddyn conjectured that high edge connectivity forces thin trees; the strong thin tree conjecture asks for thinness C/kC/kC/k in every kkk-edge-connected multigraph. For applications one wants not just existence but an efficient algorithm that finds such a tree, also when parallel-edge multiplicities are huge and written in binary.

Background. The maximum-entropy rounding of Asadpour et al. constructs O(log⁡n/(klog⁡log⁡n))O(\log n/(k\log\log n))O(logn/(kloglogn))-thin trees. Oveis Gharan and Saberi constructed O(1/k)O(1/k)O(1/k)-thin trees on planar and bounded-genus graphs (2011, doi:10.1137/1.9781611973082.75). Anari and Oveis Gharan proved existence of poly(log⁡log⁡n)/k\mathrm{poly}(\log\log n)/kpoly(loglogn)/k-thin trees in general (2015, arXiv:1411.4613), without a polynomial-time construction. Klein and Olver (2023, doi:10.1109/FOCS57990.2023.00011) and Klein, Olver and Yeoh (2026, doi:10.4230/LIPIcs.ICALP.2026.129) handled laminar families and near-minimum cuts. A companion OpenAI preprint claims the existence statement of the strong conjecture.

The source of this mission, an OpenAI preprint dated September 23, 2026, claims a deterministic polynomial-time construction.

Setting

Graphs are finite undirected loopless multigraphs; parallel copies count separately. For ∅≠S⊊V(G)\varnothing\ne S\subsetneq V(G)∅=S⊊V(G), δG(S)\delta_G(S)δG​(S) is the multiset of edges with exactly one endpoint in SSS, and GGG is kkk-edge-connected if ∣δG(S)∣≥k|\delta_G(S)|\ge k∣δG​(S)∣≥k for every such SSS. A spanning tree TTT is α\alphaα-thin if ∣δT(S)∣≤α∣δG(S)∣|\delta_T(S)|\le\alpha|\delta_G(S)|∣δT​(S)∣≤α∣δG​(S)∣ for all such SSS. Two input formats are allowed: an explicit list of labelled edge copies, or a list of endpoint pairs with multiplicities written in binary (so the number of edges can be exponential in the input length). Running time is measured in the total binary input length.

Formalization targets

Goal: Theorem 1.1 (algorithmic strong thin trees)

There is an absolute constant CCC and a deterministic algorithm that, given k≥1k\ge1k≥1 and a kkk-edge-connected multigraph GGG on at least one vertex (in either format), returns in time polynomial in the input length a spanning tree TTT with

∣δT(S)∣≤Ck ∣δG(S)∣(∅≠S⊊V(G)),|\delta_T(S)|\le\frac Ck\,|\delta_G(S)|\qquad(\varnothing\ne S\subsetneq V(G)),∣δT​(S)∣≤kC​∣δG​(S)∣(∅=S⊊V(G)),

returning the empty tree for a one-vertex graph. Lean: OAI.AlgorithmicThinTrees.algorithmic_strong_thin_trees, open on the platform.

Significance

The theorem would give an algorithmic resolution of the strong thin tree conjecture: not only do C/kC/kC/k-thin trees exist, they can be found deterministically in polynomial time, even for exponentially many parallel edges. It also yields simultaneous cost-and-cut rounding (Corollary 7.1): from a fractional point whose mass on every cut is at least one, a tree in its support with every cut load and total cost within constant factors, with no metric assumption on costs. The order 1/k1/k1/k is optimal. The result is claimed in an OpenAI preprint that has not been peer reviewed; no machine-checked proof exists.

Difficulty

Existence proofs for thin trees go through the Marcus–Spielman–Srivastava interlacing-families theorem and a fixed-point argument, neither of which is constructive: they show that some choice works without locating it. The algorithm must replace these by explicit, rational, polynomially sized computations (checked signings, rational positive semidefinite decompositions, bounded denominators) and must work with binary multiplicities, where even listing the edges is exponential. Certifying thinness on all exponentially many cuts must also be done without enumerating them.

Formalization scope

  • Graphs: StrongThinTree.MultiGraph n m (vertices Fin n, edges Fin m, loopless). Inputs are ExplicitInput or BinaryInput (ordered, distinct endpoint pairs with multiplicities); a binary input's edge copies are Σ i, Fin (multiplicity i).
  • Valid x: n≥1n\ge1n≥1, k≥1k\ge1k≥1 and EdgeConnected k.
  • The machine model is CurrentKS.Machine: a deterministic machine with finitely many binary stacks and a finite instruction table, run for a fuel bound. Inputs and outputs are self-delimiting binary encodings (CurrentKS.encodeNat).
  • AlgorithmicStrongThinTrees: ∃ C > 0, ∃ M a degree, 0 < a ∧ ∀ x, Valid x → after a * (len + 1)^degree steps M has halted and its output stack encodes a duplicate-free edge list forming a ThinTree C k; for n=1n=1n=1 the output is empty.
  • One machine and one polynomial serve all inputs in both formats.

Needed infrastructure: the existence theory of the companion mission, a model of deterministic computation with polynomial-time bounds, exact rational linear algebra, and constructive spectral sparsification. Contributions formalizing Proposition 2.5, Proposition 3.1, Lemma 6.1, Theorem A.1 (rational rank-one signing) or Corollary 7.1 are welcome.

Selected references

  • OpenAI, A polynomial-time construction of strong thin trees, preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/A-polynomial-time-construction-of-strong-thin-trees-September-23-2026/paper.pdf
  • OpenAI, The strong thin tree conjecture, preprint, September 23, 2026.
  • A. Asadpour, M. X. Goemans, A. Mądry, S. Oveis Gharan, A. Saberi, An O(log n/log log n)-approximation algorithm for the asymmetric traveling salesman problem, Oper. Res., 2017. https://doi.org/10.1287/opre.2017.1603
  • N. Anari, S. Oveis Gharan, Effective-resistance-reducing flows, spectrally thin trees, and asymmetric TSP, FOCS, 2015. https://arxiv.org/abs/1411.4613
  • S. Oveis Gharan, A. Saberi, The asymmetric traveling salesman problem on graphs with bounded genus, SODA, 2011. https://doi.org/10.1137/1.9781611973082.75
  • N. Klein, N. Olver, Thin trees for laminar families, FOCS, 2023. https://doi.org/10.1109/FOCS57990.2023.00011
  • N. Klein, N. Olver, Z. S. Yeoh, Thin trees for near minimum cuts, ICALP, 2026. https://doi.org/10.4230/LIPIcs.ICALP.2026.129
  • O. Svensson, J. Tarnawski, L. A. Végh, A constant-factor approximation algorithm for the asymmetric traveling salesman problem, J. ACM, 2020. https://doi.org/10.1145/3424306
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CombinatoricsGraph TheoryRepresentation Theory·Captain: wurtle

Elementary positivity of chromatic quasisymmetric functionsResearch Paper

Motivation: the Shareshian–Wachs elementary-positivity conjecture

The chromatic symmetric function of a graph, introduced by Stanley, records all proper colorings of the graph with infinitely many colors as a symmetric function; it refines the chromatic polynomial. Whether it expands with nonnegative coefficients in the basis of elementary symmetric functions ("eee-positivity") is a central question in algebraic combinatorics. The Stanley–Stembridge conjecture (1993) predicted eee-positivity for incomparability graphs of (3+1)(3+1)(3+1)-free posets; it arose from positivity questions for immanants of Jacobi–Trudi matrices. Shareshian and Wachs introduced a qqq-graded refinement, the chromatic quasisymmetric function, which tracks ascents of colorings along edges, and conjectured that for natural unit interval graphs its elementary coefficients are polynomials in qqq with nonnegative integer coefficients. The qqq-refinement is tied to geometry: it describes the graded representation of the symmetric group on the cohomology of regular semisimple Hessenberg varieties.

Timeline

  • 1993 — Stanley and Stembridge formulate the eee-positivity conjecture for (3+1)(3+1)(3+1)-free posets (J. Combin. Theory Ser. A 1993); Haiman's immanant results imply Schur positivity (JAMS 1993).
  • 1995 — Stanley introduces the chromatic symmetric function (Adv. Math. 1995).
  • 1996 — Gasharov gives a positive Schur expansion via poset tableaux (Discrete Math. 1996).
  • 2012/2016 — Shareshian and Wachs introduce the chromatic quasisymmetric function, prove its symmetry and Schur positivity for natural unit interval graphs, and conjecture elementary positivity over N[q]\mathbb N[q]N[q] and the Hessenberg connection (Configuration Spaces 2012; Adv. Math. 2016).
  • 2013 — Guay-Paquet reduces the Stanley–Stembridge conjecture to unit interval orders (arXiv:1306.2400).
  • 2015 — Athanasiadis gives a power-sum expansion (Electron. J. Combin. 2015).
  • 2016–2018 — Brosnan–Chow (Adv. Math. 2018) and Guay-Paquet (arXiv:1601.05498) prove the Hessenberg representation identity.
  • 2019–2023 — Positivity for substantial subfamilies: abelian Hessenberg varieties (Harada–Precup 2019), explicit expansions (Cho–Huh 2019), melting lollipops (Huh–Nam–Yoo 2020), abelian Dyck paths via qqq-rook theory (Colmenarejo–Morales–Panova 2023); the modular law of Abreu and Nigro (J. Combin. Theory Ser. A 2021).
  • 2024–2025 — Hikita proves the Stanley–Stembridge conjecture (q=1q=1q=1) via a probabilistic formula (arXiv:2410.12758); his weights do not give coefficientwise positivity in N[q]\mathbb N[q]N[q]. Griffin, Mellit, Romero, Weigl and Wen give an independent proof (arXiv:2504.06936).
  • 2026 — The OpenAI preprint Elementary positivity of chromatic quasisymmetric functions (OpenAI Math Release, September 24, 2026) claims elementary positivity over N[q]\mathbb N[q]N[q] for all natural unit interval graphs, with an explicit permutation witness. It has not been peer reviewed and its theorem is not formally verified.

Setting

Let h:[n]→[n]h:[n]\to[n]h:[n]→[n] be weakly increasing with i≤h(i)i\le h(i)i≤h(i). The natural unit interval graph G=G(h)G=G(h)G=G(h) has vertices [n]={1,…,n}[n]=\{1,\dots,n\}[n]={1,…,n} and edges {i,j}\{i,j\}{i,j} with i<j≤h(i)i<j\le h(i)i<j≤h(i). A proper coloring f:[n]→N>0f:[n]\to\mathbb N_{>0}f:[n]→N>0​ gives different colors to adjacent vertices, and asc⁡G(f)\operatorname{asc}_G(f)ascG​(f) counts edges {a<b}\{a<b\}{a<b} with f(a)<f(b)f(a)<f(b)f(a)<f(b). The chromatic quasisymmetric function is

χG(X;q)=∑f properqasc⁡G(f)∏a=1nxf(a).\chi_G(X;q)=\sum_{f\ \text{proper}} q^{\operatorname{asc}_G(f)}\prod_{a=1}^n x_{f(a)} .χG​(X;q)=f proper∑​qascG​(f)a=1∏n​xf(a)​.

For a partition λ\lambdaλ, eλ=∏ieλie_\lambda=\prod_i e_{\lambda_i}eλ​=∏i​eλi​​ with ek=∑i1<⋯<ikxi1⋯xike_k=\sum_{i_1<\dots<i_k}x_{i_1}\cdots x_{i_k}ek​=∑i1​<⋯<ik​​xi1​​⋯xik​​. Let DG0D^0_GDG0​ be the set of permutations σ\sigmaσ (as words σ1⋯σn\sigma_1\cdots\sigma_nσ1​⋯σn​) such that every adjacent descent σi>σi+1\sigma_i>\sigma_{i+1}σi​>σi+1​ is an edge, and ginv⁡G(σ)\operatorname{ginv}_G(\sigma)ginvG​(σ) the number of inversions i<ji<ji<j, σi>σj\sigma_i>\sigma_jσi​>σj​, with {σi,σj}\{\sigma_i,\sigma_j\}{σi​,σj​} an edge.

In Lean (namespace OAI.ElementaryPositivity), NaturalUnitIntervalGraph n stores a monotone extensive h : Fin n → Fin n; chromatic r is χG\chi_GχG​ in rrr variables as an MvPolynomial (Fin r) (Polynomial ℕ); Nondescent and graphInversions define DG0D^0_GDG0​ and ginv⁡G\operatorname{ginv}_GginvG​; a PermutationWitness is a map θ:DG0→{λ⊢n}\theta:D^0_G\to\{\lambda\vdash n\}θ:DG0​→{λ⊢n} together with the identity below in every number rrr of variables, using Mathlib's MvPolynomial.esymmPart.

Formalization targets

Goal: elementary positivity with a permutation witness (Theorem 1.1)

For every nnn and every natural unit interval graph GGG on [n][n][n] there is a map θG:DG0→{λ⊢n}\theta_G:D^0_G\to\{\lambda\vdash n\}θG​:DG0​→{λ⊢n} with

χG(X;q)=∑σ∈DG0qginv⁡G(σ) eθG(σ)(X).\chi_G(X;q)=\sum_{\sigma\in D^0_G} q^{\operatorname{ginv}_G(\sigma)}\,e_{\theta_G(\sigma)}(X).χG​(X;q)=σ∈DG0​∑​qginvG​(σ)eθG​(σ)​(X).

In particular every elementary coefficient of χG\chi_GχG​ lies in N[q]\mathbb N[q]N[q]. The goal is published on the platform with status Open.

Significance

The result itself. The theorem resolves the elementary-positivity part of the Shareshian–Wachs conjecture, and at q=1q=1q=1, via Guay-Paquet's reduction, recovers the Stanley–Stembridge conjecture first proved by Hikita. It is stronger than positivity: it gives a combinatorial witness, assigning each permutation of DG0D^0_GDG0​ an elementary partition while preserving its graph-inversion weight. Through the Brosnan–Chow/Guay-Paquet identity, it implies that each graded piece of the cohomology of a regular semisimple Hessenberg variety is a direct sum of Young permutation modules with multiplicities counted by the witness (Corollary 1.2). Elementary unimodality, the other part of the Shareshian–Wachs conjecture, is not addressed.

Formalizing it. The statement is a finite polynomial identity for each nnn, GGG and rrr, fully expressible with Mathlib's multivariate polynomials and elementary symmetric polynomials. A machine-checked proof would certify a long bijective argument (walls, triangle moves, witness completion) with many case distinctions.

Difficulty

The coloring definition already has nonnegative coefficients in the monomial basis, but rewriting in the elementary basis involves subtraction, so positivity is not visible from the definition. Earlier positive formulas (Schur expansions, power-sum expansions, the acyclic-orientation formula) group partitions or work at q=1q=1q=1, and Hikita's probabilistic formula has rational qqq-weights that are only positive at real q>0q>0q>0, not coefficientwise. A proof over N[q]\mathbb N[q]N[q] must produce, for each elementary partition and each power of qqq, a nonnegative integer count, uniformly over all graphs.

Formalization scope

  • Graphs are encoded by h : Fin n → Fin n monotone with i ≤ h i (zero-indexed); edges are i<j≤h(i)i<j\le h(i)i<j≤h(i) in either order.
  • Colorings use rrr colors Fin r and the identity is required for every r∈Nr\in\mathbb Nr∈N, which is equivalent to the identity of symmetric functions since both sides are homogeneous of degree nnn.
  • Coefficients live in Polynomial ℕ, so nonnegativity in N[q]\mathbb N[q]N[q] is built into the statement; the witness theta is an arbitrary function on the finite set DG0D^0_GDG0​. The source's additional assertion that θG\theta_GθG​ is produced by a deterministic terminating procedure is not encoded, but any such function on a finite set is computable by search, so nothing of mathematical substance is lost.
  • Needed infrastructure: symmetric functions in finitely many variables, elementary basis manipulations, and bijections on ordered independent-set collections. These are reusable for other chromatic-function and LLT-positivity problems.

Selected references

  • R. P. Stanley and J. R. Stembridge, On immanants of Jacobi–Trudi matrices and permutations with restricted position, J. Combin. Theory Ser. A 62 (1993). https://doi.org/10.1016/0097-3165(93)90048-D
  • R. P. Stanley, A symmetric function generalization of the chromatic polynomial of a graph, Adv. Math. 111 (1995), 166–194. https://doi.org/10.1006/aima.1995.1020
  • J. Shareshian and M. L. Wachs, Chromatic quasisymmetric functions, Adv. Math. 295 (2016). https://doi.org/10.1016/j.aim.2015.12.018
  • M. Guay-Paquet, A modular law for the chromatic symmetric functions of (3+1)-free posets, arXiv:1306.2400 (2013). https://arxiv.org/abs/1306.2400v1
  • P. Brosnan and T. Y. Chow, Unit interval orders and the dot action on the cohomology of regular semisimple Hessenberg varieties, Adv. Math. 329 (2018), 955–1001. https://doi.org/10.1016/j.aim.2018.02.020
  • A. Abreu and A. Nigro, Chromatic symmetric functions from the modular law, J. Combin. Theory Ser. A 180 (2021). https://doi.org/10.1016/j.jcta.2021.105407
  • T. Hikita, A proof of the Stanley–Stembridge conjecture, arXiv:2410.12758 (2024, revised 2025). https://arxiv.org/abs/2410.12758v2
  • S. T. Griffin, A. Mellit, M. Romero, K. Weigl and J. J. Wen, On Macdonald expansions of q-chromatic symmetric functions and the Stanley–Stembridge conjecture, arXiv:2504.06936 (2025). https://arxiv.org/abs/2504.06936v1
  • OpenAI, Elementary positivity of chromatic quasisymmetric functions, OpenAI Math Release preprint, September 24, 2026 (Theorem 1.1, p. 2; Corollary 1.2, p. 4). https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Elementary-Positivity-of-Chromatic-Quasisymmetric-Functions-September-24-2026/paper.pdf
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AlgebraCombinatoricsRepresentation Theory·Captain: wurtle

Combinatorial invariance of Kazhdan–Lusztig polynomialsResearch Paper

Motivation

Kazhdan–Lusztig polynomials Pu,b(q)P_{u,b}(q)Pu,b​(q) are integer polynomials attached to pairs u≤bu\le bu≤b of elements of a Coxeter group. They were introduced through the canonical basis of the Hecke algebra (Kazhdan–Lusztig, 1979). For Weyl groups they compute local intersection cohomology of Schubert varieties, and they enter the Kazhdan–Lusztig conjectures on characters of highest-weight representations. Their definition uses the simple generators of the group, yet in every known example Pu,bP_{u,b}Pu,b​ seemed to depend only on the Bruhat interval [u,b][u,b][u,b] as an abstract partially ordered set. The combinatorial invariance conjecture, attributed to Lusztig and Dyer, asserts this: if two Bruhat intervals, possibly in different Coxeter groups, are isomorphic as posets, their Kazhdan–Lusztig polynomials coincide.

This mission asks for a formal proof of the conjecture, as stated in an OpenAI preprint dated September 24, 2026 (source). The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open.

Background

  • 1979–1980 — Kazhdan and Lusztig define the polynomials (Invent. Math. 1979) and interpret them via Schubert varieties (Proc. Sympos. Pure Math. 36, 1980).
  • 1987–1993 — Dyer's thesis and papers on reflection subgroups and the Bruhat graph (J. Algebra 1990; Compositio 1991); the conjecture's published formulation for finite Coxeter systems and the reconstruction of the Bruhat graph from the interval order.
  • 2000–2006 — Lower intervals [1,b][1,b][1,b]: du Cloux (Adv. Math. 2003), Brenti for symmetric groups (EJC 2004), and for all Coxeter systems Brenti–Caselli–Marietti (Adv. Math. 2006) and Delanoy (J. Alg. Combin. 2006).
  • 2001–2014 — Moment-graph and Soergel-bimodule machinery: Braden–MacPherson (Math. Ann. 2001), Fiebig (Trans. AMS 2008), Soergel (J. Inst. Math. Jussieu 2007), Elias–Williamson (Annals 2014).
  • 2007–2021 — Short intervals (Incitti, JCTA 2007); the coefficient of qqq in simply-laced type (Patimo, IMRN 2021).
  • 2022–2026 — Hypercube decompositions guided by machine learning (Blundell–Buesing–Davies–Veličković–Williamson, Represent. Theory 2022); Barkley–Gaetz (Math. Ann. 2025; IMRN 2026); Barkley–Gaetz–Lam: invariance of the qqq-coefficient in general and full invariance up to rank six (arXiv:2601.07793); Esposito–Marietti–Stella up to rank ten in finite Weyl groups (arXiv:2509.16433).
  • September 2026 — The OpenAI preprint claims the full conjecture for arbitrary Coxeter systems (Theorem 1.1, p. 2).

Setting

A Coxeter system (W,S)(W,S)(W,S) is a group WWW with a generating set SSS of involutions subject only to relations (st)mst=1(st)^{m_{st}}=1(st)mst​=1. Write ℓ\ellℓ for word length in SSS. A reflection is a conjugate of an element of SSS. Bruhat order is the partial order generated by x<txx<txx<tx whenever ttt is a reflection and ℓ(tx)>ℓ(x)\ell(tx)>\ell(x)ℓ(tx)>ℓ(x); the interval [u,b]={x:u≤x≤b}[u,b]=\{x:u\le x\le b\}[u,b]={x:u≤x≤b} is finite and graded of rank ℓ(b)−ℓ(u)\ell(b)-\ell(u)ℓ(b)−ℓ(u), even when WWW is infinite. The equal-parameter R-polynomials Rx,yR_{x,y}Rx,y​ and Kazhdan–Lusztig polynomials Px,yP_{x,y}Px,y​ are the unique families with Rx,x=Px,x=1R_{x,x}=P_{x,x}=1Rx,x​=Px,x​=1, Rx,y=Px,y=0R_{x,y}=P_{x,y}=0Rx,y​=Px,y​=0 unless x≤yx\le yx≤y, the recursion: for s∈Ss\in Ss∈S with sy<ysy<ysy<y,

Rx,y={Rsx,sy,sx<x,(q−1)Rx,sy+q Rsx,sy,sx>x,R_{x,y}=\begin{cases}R_{sx,sy}, & sx<x,\\ (q-1)R_{x,sy}+q\,R_{sx,sy}, & sx>x,\end{cases}Rx,y​={Rsx,sy​,(q−1)Rx,sy​+qRsx,sy​,​sx<x,sx>x,​

the degree bound deg⁡Px,y<(ℓ(y)−ℓ(x))/2\deg P_{x,y}<(\ell(y)-\ell(x))/2degPx,y​<(ℓ(y)−ℓ(x))/2 for x<yx<yx<y, and the inversion formula

qℓ(y)−ℓ(x)Px,y(q−1)=∑x≤z≤yRx,z(q) Pz,y(q).q^{\ell(y)-\ell(x)}P_{x,y}(q^{-1})=\sum_{x\le z\le y}R_{x,z}(q)\,P_{z,y}(q).qℓ(y)−ℓ(x)Px,y​(q−1)=x≤z≤y∑​Rx,z​(q)Pz,y​(q).

Formalization targets

Goal: Theorem 1.1 (p. 2)

Let (W,S)(W,S)(W,S) and (W′,S′)(W',S')(W′,S′) be arbitrary Coxeter systems, u≤bu\le bu≤b in WWW and u′≤b′u'\le b'u′≤b′ in W′W'W′. If ι:[u,b]→[u′,b′]\iota:[u,b]\to[u',b']ι:[u,b]→[u′,b′] is an isomorphism of posets (a bijection preserving and reflecting order), then

Pu,bW(q)=Pu′,b′W′(q).P^{W}_{u,b}(q)=P^{W'}_{u',b'}(q).Pu,bW​(q)=Pu′,b′W′​(q).

The isomorphism carries no labels or root data; the groups may be infinite and noncrystallographic.

Significance

The result itself. It shows that a Kazhdan–Lusztig polynomial, defined algebraically from the Hecke algebra, is determined by a finite poset. Restricting ι\iotaι to subintervals gives the same for every Px,yP_{x,y}Px,y​ with u≤x≤y≤bu\le x\le y\le bu≤x≤y≤b, and hence for the R-polynomials (Corollary 6.1, p. 29). For Weyl groups this means that local intersection cohomology of Schubert varieties is determined by the Bruhat poset. It settles a question open since the 1980s that had been verified only for lower intervals, short intervals, low ranks, and single coefficients.

Formalizing it. Mathlib has Coxeter systems and their length function but no Bruhat order, Hecke algebra, or Kazhdan–Lusztig theory. The proof uses reflection orders (Dyer's path formula, Theorem 2.2, p. 7), Braden–MacPherson moment-graph sheaves over a reflection-faithful real realization (Theorem 2.3, p. 8), and the Elias–Williamson character theorem as external inputs. Building these would be a substantial, reusable contribution to formal representation theory.

Difficulty

The defining recursion uses a simple generator sss with sy<ysy<ysy<y, but a poset isomorphism need not carry simple generators or reflections to anything recognisable, and need not respect root labels. Dyer's reconstruction recovers all reflection edges of the Bruhat graph from the order (Theorem 3.4, p. 12) but not their labels. Lanini's moment-graph invariance requires compatible label automorphisms, which an abstract isomorphism does not provide. The preprint therefore keeps the two realizations separate, transports reflection orders through maximal dihedral subintervals (Proposition 3.5, p. 13), counts later edges exactly, and compares images of edges in two different sheaves by a reciprocal inequality that is then forced to be an equality (Sections 5–6).

Formalization scope

  • Coxeter systems are Mathlib CoxeterSystem M W for arbitrary index types and groups; the two systems may live in different universes.
  • BruhatLE is the reflexive–transitive closure of x→txx\to txx→tx with ttt a reflection and ℓ(x)<ℓ(tx)\ell(x)<\ell(tx)ℓ(x)<ℓ(tx), i.e. strong Bruhat order (not weak order). Interval cs u b carries this order; ι is an order isomorphism ≃o.
  • klPolynomial is the second component of Classical.epsilon (NormalizedKL cs), where NormalizedKL encodes exactly the normalization above (R-recursion with left multiplication, diagonal and vanishing conditions, 2deg⁡P<ℓ(y)−ℓ(x)2\deg P<\ell(y)-\ell(x)2degP<ℓ(y)−ℓ(x), and the inversion formula with Polynomial.reflect). Existence and uniqueness of such families is the classical Kazhdan–Lusztig theorem, and a proof of the goal has to establish them, since otherwise Classical.epsilon returns an arbitrary pair.
  • Sums over intervals use finsum; intervals are finite, so this is the ordinary sum.
  • The hypotheses u≤bu\le bu≤b, u′≤b′u'\le b'u′≤b′ are explicit; there is no vacuous reading.

Welcome contributions: Bruhat order and its basic properties (subword property, finiteness of intervals), R-polynomials, and existence and uniqueness of Kazhdan–Lusztig polynomials for arbitrary Coxeter systems.

Selected references

  • OpenAI, Combinatorial invariance of Kazhdan–Lusztig polynomials, OpenAI Math Release preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Combinatorial-Invariance-of-Kazhdan-Lusztig-Polynomials-September-24-2026/paper.pdf
  • D. Kazhdan, G. Lusztig, Representations of Coxeter groups and Hecke algebras, Invent. Math. 53 (1979), 165–184. https://doi.org/10.1007/BF01390031
  • M. Dyer, On the "Bruhat graph" of a Coxeter system, Compositio Math. 78 (1991), 185–191. https://www.numdam.org/item/CM_1991__78_2_185_0/
  • F. Brenti, F. Caselli, M. Marietti, Special matchings and Kazhdan–Lusztig polynomials, Adv. Math. 202 (2006), 555–601. https://doi.org/10.1016/j.aim.2005.01.011
  • E. Delanoy, Combinatorial invariance of Kazhdan–Lusztig polynomials on intervals starting from the identity, J. Algebraic Combin. 24 (2006), 437–463. https://doi.org/10.1007/s10801-006-0014-7
  • F. du Cloux, Rigidity of Schubert closures and invariance of Kazhdan–Lusztig polynomials, Adv. Math. 180 (2003), 146–175. https://doi.org/10.1016/S0001-8708(02)00100-7
  • T. Braden, R. MacPherson, From moment graphs to intersection cohomology, Math. Ann. 321 (2001), 533–551. https://doi.org/10.1007/s002080100232
  • B. Elias, G. Williamson, The Hodge theory of Soergel bimodules, Ann. of Math. 180 (2014), 1089–1136. https://doi.org/10.4007/annals.2014.180.3.6
  • C. Blundell, L. Buesing, A. Davies, P. Veličković, G. Williamson, Towards combinatorial invariance for Kazhdan–Lusztig polynomials, Represent. Theory 26 (2022), 1145–1191. https://doi.org/10.1090/ert/624
  • G. T. Barkley, C. Gaetz, T. Lam, Combinatorial invariance for the coefficient of q in Kazhdan–Lusztig polynomials, arXiv:2601.07793 (2026). https://arxiv.org/abs/2601.07793v2
  • A. Björner, F. Brenti, Combinatorics of Coxeter Groups, GTM 231, Springer, 2005. https://doi.org/10.1007/3-540-27596-7
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CombinatoricsGraph Theory·Captain: wurtle

A counterexample to Ryser's covering conjectureResearch Paper

Motivation

A vertex cover of a hypergraph HHH is a set of vertices meeting every edge; the covering number τ(H)\tau(H)τ(H) is the minimum size of a cover, and the matching number ν(H)\nu(H)ν(H) is the maximum number of pairwise disjoint edges. In an rrr-partite rrr-uniform hypergraph the vertices are split into rrr parts and every edge contains exactly one vertex from each part. Ryser's covering conjecture asserts

τ(H)≤(r−1) ν(H).\tau(H)\le(r-1)\,\nu(H).τ(H)≤(r−1)ν(H).

For r=2r=2r=2 it is Kőnig's theorem, so it proposes a higher-dimensional analogue of the most basic min–max theorem of bipartite matching theory. In the intersecting case (ν=1\nu=1ν=1) it asks for a cover of size r−1r-1r−1, and it is equivalent to Gyárfás's conjecture that r−1r-1r−1 monochromatic trees cover any rrr-edge-coloured complete graph.

Background. An equivalent formulation appears in Henderson's 1971 thesis (doi:10.7907/J1Z1-SK19). Aharoni proved the case r=3r=3r=3 in 2001 (doi:10.1007/s004930170001); Gyárfás and Tuza settled the intersecting case through r=5r=5r=5; Haxell and Scott (2012) proved τ≤(r−ϵ)ν\tau\le(r-\epsilon)\nuτ≤(r−ϵ)ν for r=4,5r=4,5r=4,5 (doi:10.37236/1175); Francetić, Herke, McKay and Wanless (2017) handled linear intersecting hypergraphs through rank nine (doi:10.1016/j.ejc.2016.10.004); Bishnoi, Das, Morris and Szabó (2021) treated ttt-intersecting hypergraphs (doi:10.1016/j.jcta.2020.105366). Truncated projective planes give equality whenever r−1r-1r−1 is a prime power; equality examples were studied by Mansour, Song and Yuster (2009, doi:10.1007/s00373-008-0821-9), Aharoni, Barát and Wanless (2016, doi:10.1007/s00373-015-1575-9) and Abu-Khazneh, Barát, Pokrovskiy and Szabó (2019, doi:10.1016/j.jcta.2018.07.011). Clow, Haxell and Mohar disproved Lovász's stronger matching-reduction conjecture at r=3r=3r=3 (doi:10.1007/s00493-026-00220-3), without contradicting Ryser's inequality.

The source of this mission, an OpenAI preprint dated September 23, 2026, claims intersecting counterexamples for ranks r=sn+1r=s^n+1r=sn+1.

Setting

A finite hypergraph is a finite set of edges, each a finite set of vertices. It is rrr-partite rrr-uniform if there is a map assigning each vertex one of rrr parts such that every edge contains exactly one vertex of each part. It is intersecting if it is nonempty and any two distinct edges share a vertex, so ν(H)=1\nu(H)=1ν(H)=1. Every edge of an intersecting hypergraph is itself a cover, so τ(H)≤r\tau(H)\le rτ(H)≤r always; a counterexample to Ryser's conjecture in the intersecting case needs τ(H)=r\tau(H)=rτ(H)=r.

Formalization targets

Milestone: special case of Theorem 1.1

There are a prime p>5p>5p>5 with p≡2(mod3)p\equiv2\pmod3p≡2(mod3) and NNN such that for every prime n≥Nn\ge Nn≥N, n>2n>2n>2, an intersecting (pn+1)(p^n+1)(pn+1)-partite (pn+1)(p^n+1)(pn+1)-uniform hypergraph with ν=1\nu=1ν=1 and τ=pn+1\tau=p^n+1τ=pn+1 exists.

Goal: Theorem 1.1

There is s0s_0s0​ such that for every prime s≥s0s\ge s_0s≥s0​ with s≡2(mod3)s\equiv2\pmod 3s≡2(mod3) there is n0(s)≥3n_0(s)\ge3n0​(s)≥3 such that for every odd n≥n0(s)n\ge n_0(s)n≥n0​(s), with q=snq=s^nq=sn, there is a finite intersecting (q+1)(q+1)(q+1)-partite (q+1)(q+1)(q+1)-uniform hypergraph HHH with

τ(H)=q+1>q=(r−1) ν(H).\tau(H)=q+1>q=(r-1)\,\nu(H).τ(H)=q+1>q=(r−1)ν(H).

In particular infinitely many ranks fail. Lean: OAI.RyserOdd.eventualOddFailures_and_infinite, open on the platform.

Significance

The theorem would disprove Ryser's conjecture in its intersecting case, and through the standard transfer (Corollary 1.2) also Gyárfás's monochromatic tree-cover conjecture, for the ranks sn+1s^n+1sn+1. The value τ=r\tau=rτ=r is the maximum possible. A companion preprint of the same family covers a different range of ranks (prime qqq, with balanced parts); the two constructions are independent apart from three elementary estimates. Thresholds are not numerical. The result is claimed in an OpenAI preprint that has not been peer reviewed; no machine-checked proof exists.

Difficulty

The truncated-plane examples sit exactly at τ=r−1\tau=r-1τ=r−1, and covers may mix vertices from different parts, so enlarging individual parts does not help. One must insert exceptional edges that destroy every cover of size qqq while preserving the intersecting property. Ruling out all small covers requires showing that any small cover contains almost a full pencil of lines, which over Fsn\mathbb F_{s^n}Fsn​ needs an incidence bound with a power saving (a Bourgain–Katz–Tao-type sum–product estimate) valid in fields whose proper subfields are small. This is where odd exponents and the congruence s≡2(mod3)s\equiv2\pmod3s≡2(mod3) enter.

Formalization scope

  • Hypergraph V := Finset (Finset V) over a finite type V with decidable equality.
  • RPartiteUniform r H: some part : V → Fin r with exactly one vertex of each part in every edge. Uniform r H: every edge has r vertices.
  • Intersecting: distinct edges meet. matchingNumber is the maximal size of a pairwise disjoint subfamily; coverNumber is sInf of cover sizes, and existence of a cover is asserted separately so the sInf is not the empty-set default.
  • ExplicitFailureAt r packages all of these with ν=1\nu=1ν=1, τ=r\tau=rτ=r and (r−1)ν<τ(r-1)\nu<\tau(r−1)ν<τ; the goal asserts it for the stated ranks and that the set of failing ranks is infinite.

Needed infrastructure: finite fields Fsn\mathbb F_{s^n}Fsn​ and their projective planes, incidence and sum–product estimates, and probabilistic selection arguments. Contributions formalizing Theorem 2.1 (uniform incidence saving), Lemma 3.2 (line covers after random deletions) or Theorem 4.1 (compatible translates from independent pools) are welcome.

Selected references

  • OpenAI, A counterexample to Ryser's covering conjecture, preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/A-Counterexample-to-Rysers-Covering-Conjecture-September-23-2026/paper.pdf
  • J. R. Henderson, Permutation decompositions of (0,1)-matrices and decomposition transversals, PhD thesis, 1971. https://doi.org/10.7907/J1Z1-SK19
  • R. Aharoni, Ryser's conjecture for tripartite 3-graphs, Combinatorica, 2001. https://doi.org/10.1007/s004930170001
  • P. Haxell, A. Scott, On Ryser's conjecture, Electron. J. Combin., 2012. https://doi.org/10.37236/1175
  • N. Francetić, S. Herke, B. D. McKay, I. M. Wanless, On Ryser's conjecture for linear intersecting multipartite hypergraphs, European J. Combin., 2017. https://doi.org/10.1016/j.ejc.2016.10.004
  • A. Abu-Khazneh, J. Barát, A. Pokrovskiy, T. Szabó, A family of extremal hypergraphs for Ryser's conjecture, J. Combin. Theory Ser. A, 2019. https://doi.org/10.1016/j.jcta.2018.07.011
  • J. Bourgain, N. Katz, T. Tao, A sum-product estimate in finite fields, and applications, Geom. Funct. Anal., 2004. https://doi.org/10.1007/s00039-004-0451-1
  • A. Clow, P. Haxell, B. Mohar, A counterexample to a conjecture of Lovász, Combinatorica, 2026. https://doi.org/10.1007/s00493-026-00220-3
  • OpenAI, Balanced counterexamples to Ryser's conjecture at prime orders, preprint, September 27, 2026.
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Complexity TheoryOperations ResearchTheoretical Computer Science·Captain: wurtle

The approximation threshold for metric k-medianResearch Paper

Motivation

In metric kkk-median, a finite set JJJ (or DDD) of clients and a finite set FFF of candidate facilities lie in a common finite metric with rational distances. Given an integer k≥1k\ge1k≥1, one opens a nonempty set S⊆FS\subseteq FS⊆F with ∣S∣≤k|S|\le k∣S∣≤k and pays

cost(S)=∑j∈Jd(j,S),d(j,S)=min⁡i∈Sd(j,i),OPTk=min⁡S⊆F, 1≤∣S∣≤kcost(S).\mathrm{cost}(S)=\sum_{j\in J}d(j,S),\qquad d(j,S)=\min_{i\in S}d(j,i),\qquad \mathrm{OPT}_k=\min_{S\subseteq F,\ 1\le|S|\le k}\mathrm{cost}(S).cost(S)=j∈J∑​d(j,S),d(j,S)=i∈Smin​d(j,i),OPTk​=S⊆F, 1≤∣S∣≤kmin​cost(S).

Candidate facilities are part of the input and need not coincide with the clients. It is one of the basic clustering and facility-location objectives, and its approximability has been a testing ground for LP rounding, primal-dual methods and local search. A lower bound has long been known: if P≠NP\mathrm P\ne\mathrm{NP}P=NP, no polynomial-time algorithm achieves a factor below 1+2/e≈1.7361+2/e\approx1.7361+2/e≈1.736 in this candidate-facility model, by a reduction from Feige's coverage gap. The best polynomial-time algorithms had reached 2+ε2+\varepsilon2+ε, leaving a gap between 1.7361.7361.736 and 222.

This mission asks for a formal proof that the threshold is exactly 1+2/e1+2/e1+2/e, as stated in an OpenAI preprint dated September 24, 2026 (source): a deterministic polynomial-time (1+2/e+ε)(1+2/e+\varepsilon)(1+2/e+ε)-approximation for every fixed ε>0\varepsilon>0ε>0, and hence, under P≠NP\mathrm P\ne\mathrm{NP}P=NP, an infimal approximation factor of exactly 1+2/e1+2/e1+2/e. The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open.

Background

  • 2001–2002 — Jain and Vazirani connect kkk-median to facility location by Lagrangian relaxation and primal-dual methods (J. ACM 2001); Charikar, Guha, Tardos and Shmoys give the first constant-factor approximation by LP rounding (JCSS 2002); Jain, Mahdian and Saberi introduce greedy dual fitting (STOC 2002; J. ACM 2003).
  • 2004 — Arya et al. show that bounded-swap local search achieves 3+ε3+\varepsilon3+ε (SICOMP 2004).
  • 2016 — Li and Svensson show that constant-additive pseudo-approximations can be converted to true approximations at arbitrarily small loss, breaking the factor-333 barrier (SICOMP 2016).
  • 2017–2023 — Bi-point rounding: 2.675+ε2.675+\varepsilon2.675+ε (Byrka et al., TALG 2017) and 2.6132.6132.613 (Gowda et al., SODA 2023).
  • 2019 — Cohen-Addad, Gupta, Kumar, Lee and Li give a tight FPT (1+2/e+ε)(1+2/e+\varepsilon)(1+2/e+ε)-approximation in time kO(k)polyk^{O(k)}\mathrm{poly}kO(k)poly (ICALP 2019).
  • 2025–2026 — Cohen-Addad, Grandoni, Lee, Schwiegelshohn and Svensson reach 2+ε2+\varepsilon2+ε (arXiv:2503.10972); Byrka et al. develop iterative randomized rounding with 2+ε2+\varepsilon2+ε (arXiv:2604.06046).
  • September 2026 — Two OpenAI preprints claim a deterministic (1+2/e+ε)(1+2/e+\varepsilon)(1+2/e+ε)-approximation, matching the known hardness threshold (source), and an independent randomized (2−σ)(2-\sigma)(2−σ)-approximation via anchor recovery and bounded-price strictness (source).
  • Hardness — Feige proves the ln⁡n\ln nlnn threshold for set cover and the perfect-completeness Max-kkk-Coverage gap (J. ACM 1998, Theorem 5.3); Anand and Lee record the resulting 1+2/e1+2/e1+2/e lower bound for kkk-median with specified facilities under P≠NP\mathrm P\ne\mathrm{NP}P=NP (IPCO 2024).

Setting

An instance consists of nnn points with a rational distance table ddd that is a metric (nonnegative, zero exactly on the diagonal, symmetric, triangle inequality), a client set JJJ, a facility set FFF (index sets that may overlap), and an integer 1≤k≤∣F∣1\le k\le|F|1≤k≤∣F∣, encoded in binary. A feasible solution is a nonempty S⊆FS\subseteq FS⊆F with ∣S∣≤k|S|\le k∣S∣≤k. An algorithm is an α\alphaα-approximation if it always outputs a feasible SSS with cost(S)≤α OPTk\mathrm{cost}(S)\le\alpha\,\mathrm{OPT}_kcost(S)≤αOPTk​, and it is polynomial-time if it runs on a deterministic multi-stack Turing machine with finite alphabets in time polynomial in the encoding length.

Formalization targets

Milestone: Theorem 1.1 (p. 1)

For every fixed ε>0\varepsilon>0ε>0 there is a deterministic polynomial-time algorithm which, on every instance, returns a feasible SSS with

∑j∈Jd(j,S)≤(1+2e+ε)OPTk.\sum_{j\in J}d(j,S)\le\Bigl(1+\frac2e+\varepsilon\Bigr)\mathrm{OPT}_k .j∈J∑​d(j,S)≤(1+e2​+ε)OPTk​.

Goal: Corollary 1.2 (p. 2)

If P≠NP\mathrm P\ne\mathrm{NP}P=NP, then

inf⁡{α: a deterministic polynomial-time α-approximation exists}=1+2e.\inf\{\alpha:\ \text{a deterministic polynomial-time }\alpha\text{-approximation exists}\}=1+\frac2e .inf{α: a deterministic polynomial-time α-approximation exists}=1+e2​.

The infimum is not asserted to be attained.

Significance

The result itself. It closes the approximability of metric kkk-median with specified candidate facilities up to arbitrarily small additive error in the factor, ending a sequence of improvements from LP rounding through local search (3+ε3+\varepsilon3+ε), bi-point rounding (2.6752.6752.675, 2.6132.6132.613) to 2+ε2+\varepsilon2+ε. It shows that the FPT factor 1+2/e1+2/e1+2/e of Cohen-Addad et al. is achievable in polynomial time. The case F=JF=JF=J has a different and still separate lower-bound question.

Formalizing it. The goal combines an algorithmic theorem with a hardness reduction. A formal proof needs: the Li–Svensson reduction from constant-additive pseudo-approximations (Theorem 2.2, p. 4); the grid preparation and iterative rounding with a finite directed comparison inequality whose scalar estimates are proved by rational polynomial certificates (Sections 3–5, Appendix A); derandomization by moment-matching distributions of polynomial size (Section 6); and, for the lower bound, NP-hardness of Feige's coverage gap in the Lean computation model. All are reusable for other clustering and covering problems.

Difficulty

Two obstacles meet. The cardinality budget is hard: allowing a constant number of extra facilities makes good connection cost much easier, and earlier rounding analyses in the iterative graph framework only certify factors at least 222 (p. 2). The preprint's new ingredient is a potential that keeps track of surviving assignment copies after an opening and a finite directed comparison inequality (Theorem 4.1, p. 13) that pays for erasures. Then the randomized rounding must be made deterministic and polynomial-time while keeping the analysis valid, which requires preserving exactly the low-order moments the analysis uses (Lemma 6.1, p. 28; Theorem 6.2, p. 33), before Li–Svensson removes the additive surplus. The hardness side is classical but rests on the PCP-based Max-kkk-Coverage gap.

Formalization scope

  • Instance carries n, a Fin n → Fin n → ℚ metric (distance_eq_zero is an iff, so a true metric), client and facility Finsets, and 1 ≤ k ≤ facilities.card. cost sums nearest distances over clients; optimum is the minimum over nonempty facility subsets of size ≤k\le k≤k.
  • Outputs are membership bit masks of an actual feasible set; FactorCorrect α I out requires that mask to describe a nonempty S⊆FS\subseteq FS⊆F with ∣S∣≤k|S|\le k∣S∣≤k and cost(S)≤α OPTk\mathrm{cost}(S)\le\alpha\,\mathrm{OPT}_kcost(S)≤αOPTk​.
  • Polynomial time is Turing.TM2ComputableInPolyTime with finite tape alphabets, on the fixed binary instance encoding.
  • approximationFactors is the set of α\alphaα admitting such an algorithm, and the goal is sInf approximationFactors = 1 + 2/Real.exp 1 under the hypothesis Complexity.PNeNP, where P and NP are defined in the same machine model (NP via polynomial-length witnesses and a finite-alphabet polynomial-time verifier). The set is nonempty by the milestone and bounded below, so sInf is meaningful.
  • The hypothesis P≠NP\mathrm P\ne\mathrm{NP}P=NP is not provable, so the goal is a conditional statement; deriving the lower bound requires a Cook–Levin-type NP-hardness of the coverage gap in this model.

Selected references

  • OpenAI, The approximation threshold for metric k-median, OpenAI Math Release preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/The-Approximation-Threshold-for-Metric-k-Median-September-24-2026/main.pdf
  • OpenAI, Single-exponential recovery and bounded-price strictness for metric k-median, OpenAI Math Release preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Single-Exponential-Recovery-and-Bounded-Price-Strictness-for-Metric-k-Median-September-24-2026/paper.pdf
  • U. Feige, A threshold of ln n for approximating set cover, J. ACM 45 (1998). https://doi.org/10.1145/285055.285059
  • A. Anand, E. Lee, Separating k-median from the supplier version, IPCO 2024. https://doi.org/10.1007/978-3-031-59835-7_2
  • K. Jain, M. Mahdian, E. Markakis, A. Saberi, V. V. Vazirani, Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP, J. ACM 50 (2003). https://doi.org/10.1145/950620.950621
  • M. Charikar, S. Li, A dependent LP-rounding approach for the k-median problem, ICALP 2012. https://doi.org/10.1007/978-3-642-31594-7_17
  • R. Gandhi, S. Khuller, S. Parthasarathy, A. Srinivasan, Dependent rounding and its applications to approximation algorithms, J. ACM 53 (2006). https://doi.org/10.1145/1147954.1147956
  • K. N. Gowda, T. Pensyl, A. Srinivasan, K. Trinh, Improved bi-point rounding algorithms and a golden barrier for k-median, SODA 2023. https://doi.org/10.1137/1.9781611977554.ch38
  • K. Jain, V. V. Vazirani, Approximation algorithms for metric facility location and k-median problems using the primal-dual schema and Lagrangian relaxation, J. ACM 48 (2001). https://doi.org/10.1145/375827.375845
  • M. Charikar, S. Guha, É. Tardos, D. B. Shmoys, A constant-factor approximation algorithm for the k-median problem, JCSS 65 (2002). https://doi.org/10.1006/jcss.2002.1882
  • V. Arya, N. Garg, R. Khandekar, A. Meyerson, K. Munagala, V. Pandit, Local search heuristics for k-median and facility location problems, SIAM J. Comput. 33 (2004). https://doi.org/10.1137/S0097539702416402
  • S. Li, O. Svensson, Approximating k-median via pseudo-approximation, SIAM J. Comput. 45 (2016). https://doi.org/10.1137/130938645
  • J. Byrka, T. Pensyl, B. Rybicki, A. Srinivasan, K. Trinh, An improved approximation for k-median and positive correlation in budgeted optimization, ACM TALG 13 (2017). https://doi.org/10.1145/2981561
  • V. Cohen-Addad, A. Gupta, A. Kumar, E. Lee, J. Li, Tight FPT approximations for k-median and k-means, ICALP 2019. https://doi.org/10.4230/LIPIcs.ICALP.2019.42
  • V. Cohen-Addad, F. Grandoni, E. Lee, C. Schwiegelshohn, O. Svensson, A (2+ε)-approximation algorithm for metric k-median, arXiv:2503.10972 (2026 version). https://arxiv.org/abs/2503.10972v2
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Operations ResearchTheoretical Computer Science·Captain: wurtle

Single-exponential recovery and bounded-price strictness for metric k-medianResearch Paper

Motivation

In metric kkk-median, a finite set JJJ (or DDD) of clients and a finite set FFF of candidate facilities lie in a common finite metric with rational distances. Given an integer k≥1k\ge1k≥1, one opens a nonempty set S⊆FS\subseteq FS⊆F with ∣S∣≤k|S|\le k∣S∣≤k and pays

cost(S)=∑j∈Jd(j,S),d(j,S)=min⁡i∈Sd(j,i),OPTk=min⁡S⊆F, 1≤∣S∣≤kcost(S).\mathrm{cost}(S)=\sum_{j\in J}d(j,S),\qquad d(j,S)=\min_{i\in S}d(j,i),\qquad \mathrm{OPT}_k=\min_{S\subseteq F,\ 1\le|S|\le k}\mathrm{cost}(S).cost(S)=j∈J∑​d(j,S),d(j,S)=i∈Smin​d(j,i),OPTk​=S⊆F, 1≤∣S∣≤kmin​cost(S).

Candidate facilities are part of the input and need not coincide with the clients. It is one of the basic clustering and facility-location objectives, and its approximability has been a testing ground for LP rounding, primal-dual methods and local search. The hard part of the problem is the facility budget: a solution with small connection cost is much easier to find if a few extra facilities may be opened, and every approximation algorithm must eventually enforce ∣S∣≤k|S|\le k∣S∣≤k on its output.

This mission concerns two tools for enforcing that budget and their combination into a randomized approximation strictly below factor 222 on arbitrary finite rational metrics, as stated in an OpenAI preprint dated September 24, 2026 (source). The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open.

Background

  • 2001–2002 — Jain and Vazirani connect kkk-median to facility location by Lagrangian relaxation and primal-dual methods (J. ACM 2001); Charikar, Guha, Tardos and Shmoys give the first constant-factor approximation by LP rounding (JCSS 2002); Jain, Mahdian and Saberi introduce greedy dual fitting (STOC 2002; J. ACM 2003).
  • 2004 — Arya et al. show that bounded-swap local search achieves 3+ε3+\varepsilon3+ε (SICOMP 2004).
  • 2016 — Li and Svensson show that constant-additive pseudo-approximations can be converted to true approximations at arbitrarily small loss, breaking the factor-333 barrier (SICOMP 2016).
  • 2017–2023 — Bi-point rounding: 2.675+ε2.675+\varepsilon2.675+ε (Byrka et al., TALG 2017) and 2.6132.6132.613 (Gowda et al., SODA 2023).
  • 2019 — Cohen-Addad, Gupta, Kumar, Lee and Li give a tight FPT (1+2/e+ε)(1+2/e+\varepsilon)(1+2/e+ε)-approximation in time kO(k)polyk^{O(k)}\mathrm{poly}kO(k)poly (ICALP 2019).
  • 2025–2026 — Cohen-Addad, Grandoni, Lee, Schwiegelshohn and Svensson reach 2+ε2+\varepsilon2+ε (arXiv:2503.10972); Byrka et al. develop iterative randomized rounding with 2+ε2+\varepsilon2+ε (arXiv:2604.06046).
  • September 2026 — Two OpenAI preprints claim a deterministic (1+2/e+ε)(1+2/e+\varepsilon)(1+2/e+ε)-approximation, matching the known hardness threshold (source), and an independent randomized (2−σ)(2-\sigma)(2−σ)-approximation via anchor recovery and bounded-price strictness (source).

Setting

Write n=∣D∣n=|D|n=∣D∣ and N=∣D∣+∣F∣N=|D|+|F|N=∣D∣+∣F∣. A comparison solution O⊆FO\subseteq FO⊆F of size hhh assigns each client to a nearest member of OOO (ties broken by fixed priorities); CiC_iCi​ is the cluster of i∈Oi\in Oi∈O and Pi=∑p∈Cid(p,i)P_i=\sum_{p\in C_i}d(p,i)Pi​=∑p∈Ci​​d(p,i), with P=cost(O)P=\mathrm{cost}(O)P=cost(O). An anchor is a supplied feasible S⊆FS\subseteq FS⊆F with ∣S∣=h|S|=h∣S∣=h. A certificate partitions OOO into good and bad centres and gives distinct proxies φi∈S\varphi_i\in Sφi​∈S for the good centres. A randomized polynomial-time algorithm is a polynomial-time multi-stack Turing machine that reads the encoded instance together with a string of fair random bits whose length is a fixed polynomial in the input length.

Formalization targets

Milestone: refined recovery (Theorem 1.1, p. 2)

Let D,FD,FD,F be disjoint, with distances between distinct indices positive integers bounded by a fixed polynomial in NNN. Fix ε>0\varepsilon>0ε>0, 0<L0<∞0<L_0<\infty0<L0​<∞ and a rational 0<μ≤min⁡{1/4,ε/12}0<\mu\le\min\{1/4,\varepsilon/12\}0<μ≤min{1/4,ε/12}. There is a randomized algorithm that, given an anchor SSS with ∣S∣=h≥1|S|=h\ge1∣S∣=h≥1 and a failure parameter ζ∈(0,1)\zeta\in(0,1)ζ∈(0,1), always returns S^⊆F\hat S\subseteq FS^⊆F with ∣S^∣≤h|\hat S|\le h∣S^∣≤h, runs in time polynomial in NNN and log⁡(1/ζ)\log(1/\zeta)log(1/ζ), and, whenever some comparison solution OOO of size hhh and cost P>0P>0P>0 admits a certificate with

∑i good∑p∈Cid(p,φi)≤∑i goodPi+μP,#{i bad}≤L0log⁡N,\sum_{i\ \mathrm{good}}\sum_{p\in C_i}d(p,\varphi_i)\le \sum_{i\ \mathrm{good}}P_i+\mu P,\qquad \#\{i\ \mathrm{bad}\}\le L_0\log N,i good∑​p∈Ci​∑​d(p,φi​)≤i good∑​Pi​+μP,#{i bad}≤L0​logN,

satisfies cost(S^)≤(1+2/e+ε)P\mathrm{cost}(\hat S)\le(1+2/e+\varepsilon)Pcost(S^)≤(1+2/e+ε)P with probability at least 1−ζ1-\zeta1−ζ. The algorithm is not given OOO, PPP or the certificate.

Goal: global application (Theorem 1.2, p. 3)

There is an absolute constant σ>0\sigma>0σ>0 such that for every fixed a>0a>0a>0 some randomized polynomial-time algorithm for metric kkk-median always returns a feasible SSS (nonempty, S⊆FS\subseteq FS⊆F, ∣S∣≤k|S|\le k∣S∣≤k) and satisfies

Pr⁡[cost(S)≤(2−σ) OPTk] ≥ 1−(∣D∣+∣F∣+2)−a,E cost(S)≤(2−σ) OPTk.\Pr\bigl[\mathrm{cost}(S)\le(2-\sigma)\,\mathrm{OPT}_k\bigr]\ \ge\ 1-(|D|+|F|+2)^{-a},\qquad \mathbb E\,\mathrm{cost}(S)\le(2-\sigma)\,\mathrm{OPT}_k .Pr[cost(S)≤(2−σ)OPTk​] ≥ 1−(∣D∣+∣F∣+2)−a,Ecost(S)≤(2−σ)OPTk​.

Significance

The result itself. Theorem 1.1 makes the running time single-exponential in the number of clusters without accurate proxies, so logarithmically many exceptions are tractable; FPT methods with kO(k)k^{O(k)}kO(k) dependence do not give this by setting k=O(log⁡N)k=O(\log N)k=O(logN). Combined with a new proof of bounded-price strictness for one compatible execution of the logarithmic-surplus construction of Cohen-Addad, Grandoni, Lee, Schwiegelshohn and Svensson (Lemma 8.1, p. 43), it yields Theorem 1.2: a randomized approximation strictly below 222, both with high probability and in expectation, that opens at most kkk facilities on every output. A companion preprint proves a stronger deterministic (1+2/e+ε)(1+2/e+\varepsilon)(1+2/e+ε) bound by an independent route; Theorem 1.2's interest is the way recovery and payment accounting together enforce the budget.

Formalizing it. The statements are fully explicit about the machine model, the random bits, and the input encoding. A formal proof would need local-search anchoring (Theorem 3.3), leader/ball sampling with amortized radius guesses (Section 4), a finite categorical continuous-greedy algorithm with explicit error (Lemma 5.1, p. 21), the surplus construction as an external interface (Theorem 7.1, p. 35), and metric rounding and transfer (Lemma 2.1, p. 6). The submodular-optimization and local-search components are reusable.

Difficulty

Every factor-222 dual-fitting argument charges λ+2PC\lambda+2P_Cλ+2PC​ to a client set CCC served by a comparison facility, which gives exactly 222; improving the constant requires saving on connection cost while still paying for all hhh regular openings with one final price, one set of copy lengths and the budgets of one actual replay of the construction (Section 1.2, p. 3). On the recovery side, guessing a good solution from an anchor naively costs kO(k)k^{O(k)}kO(k); keeping the dependence single-exponential in the number of bad clusters requires interleaving leader sampling and removals and amortizing radius guesses over disjoint client sets (Section 4). Theorem 1.1 holds only for normalized integral metrics; the global application must construct its anchors after normalization and transfer the final cost bound back (Lemma 2.1, p. 6).

Formalization scope

  • RationalMetricInput: points Fin pointCount, a rational pseudometric table (zero on the diagonal, symmetric, triangle inequality), client and facility Finsets covering all points (they may overlap, so clients and facilities may share locations), a nonempty facility set and a positive budget kkk. Feasible S is S⊆FS\subseteq FS⊆F, ∣S∣≤k|S|\le k∣S∣≤k, and SSS nonempty when there are clients; optimum minimizes over nonempty subsets of size ≤k\le k≤k.
  • A BinaryRandomizedAlgorithm is a TM2ComputableInPolyTime function of (input bits, random bits) with finite alphabets, using randomBits.eval (input length) fair bits; probabilities and expectations are uniform averages over all seeds.
  • In the goal, feasibility holds for every seed; the success bound is 1−(N+2)−a1-(N+2)^{-a}1−(N+2)−a with N=∣D∣+∣F∣N=|D|+|F|N=∣D∣+∣F∣ (Real.rpow); the same algorithm must also satisfy the expectation bound.
  • In the milestone, Input adds disjointness of DDD and FFF, integral distances, positivity between distinct indices, and an anchor with anchor.card = budget; Bounded bound caps distances by a fixed polynomial in NNN; ζ is passed in unary precision ⌈log⁡2(1/ζ)⌉\lceil\log_2(1/\zeta)\rceil⌈log2​(1/ζ)⌉, and PolynomialWork bounds work by c(N+1)d(1+log⁡(1/ζ))dc(N+1)^d(1+\log(1/\zeta))^dc(N+1)d(1+log(1/ζ))d. A Certificate encodes OOO with ∣O∣=h|O|=h∣O∣=h, P>0P>0P>0, the good set, injective proxies into the anchor, the proxy-cost promise with lexicographic tie-breaking, and at most L0log⁡NL_0\log NL0​logN bad centres.

Selected references

  • OpenAI, Single-exponential recovery and bounded-price strictness for metric k-median, OpenAI Math Release preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Single-Exponential-Recovery-and-Bounded-Price-Strictness-for-Metric-k-Median-September-24-2026/paper.pdf
  • OpenAI, The approximation threshold for metric k-median, OpenAI Math Release preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/The-Approximation-Threshold-for-Metric-k-Median-September-24-2026/main.pdf
  • V. Cohen-Addad, F. Grandoni, E. Lee, C. Schwiegelshohn, Breaching the 2 LMP approximation barrier for facility location with applications to k-median, arXiv:2207.05150 (2026 version). https://arxiv.org/abs/2207.05150v2
  • V. Cohen-Addad, C. Schwiegelshohn, On the local structure of stable clustering instances, FOCS 2017. https://doi.org/10.1109/FOCS.2017.14
  • G. Călinescu, C. Chekuri, M. Pál, J. Vondrák, Maximizing a monotone submodular function subject to a matroid constraint, SIAM J. Comput. 40 (2011). https://doi.org/10.1137/080733991
  • J. Vondrák, Optimal approximation for the submodular welfare problem in the value oracle model, STOC 2008. https://doi.org/10.1145/1374376.1374389
  • K. Jain, M. Mahdian, E. Markakis, A. Saberi, V. V. Vazirani, Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP, J. ACM 50 (2003). https://doi.org/10.1145/950620.950621
  • K. Jain, V. V. Vazirani, Approximation algorithms for metric facility location and k-median problems using the primal-dual schema and Lagrangian relaxation, J. ACM 48 (2001). https://doi.org/10.1145/375827.375845
  • M. Charikar, S. Guha, É. Tardos, D. B. Shmoys, A constant-factor approximation algorithm for the k-median problem, JCSS 65 (2002). https://doi.org/10.1006/jcss.2002.1882
  • V. Arya, N. Garg, R. Khandekar, A. Meyerson, K. Munagala, V. Pandit, Local search heuristics for k-median and facility location problems, SIAM J. Comput. 33 (2004). https://doi.org/10.1137/S0097539702416402
  • S. Li, O. Svensson, Approximating k-median via pseudo-approximation, SIAM J. Comput. 45 (2016). https://doi.org/10.1137/130938645
  • J. Byrka, T. Pensyl, B. Rybicki, A. Srinivasan, K. Trinh, An improved approximation for k-median and positive correlation in budgeted optimization, ACM TALG 13 (2017). https://doi.org/10.1145/2981561
  • V. Cohen-Addad, A. Gupta, A. Kumar, E. Lee, J. Li, Tight FPT approximations for k-median and k-means, ICALP 2019. https://doi.org/10.4230/LIPIcs.ICALP.2019.42
  • V. Cohen-Addad, F. Grandoni, E. Lee, C. Schwiegelshohn, O. Svensson, A (2+ε)-approximation algorithm for metric k-median, arXiv:2503.10972 (2026 version). https://arxiv.org/abs/2503.10972v2
2 thms1 active userReviewed
Complexity TheoryOperations ResearchTheoretical Computer Science·Captain: wurtle

A Polynomial-Time Algorithm for Three-Machine Unit-Job SchedulingResearch Paper

Motivation

A set of unit-length jobs with precedence constraints is to be run on a fixed number of identical machines so that all jobs finish as early as possible. For two machines this was solved in 1969–1972; for a number of machines given as part of the input the problem is NP-complete (Ullman, 1975). Whether the problem is polynomial-time solvable for three machines, written P3∣prec,pj=1∣Cmax⁡P3\mid\mathrm{prec},p_j=1\mid C_{\max}P3∣prec,pj​=1∣Cmax​, is problem OPEN8 in the appendix of Garey and Johnson's 1979 book and is one of the best-known open questions on the complexity of scheduling.

Timeline

  • 1969–1971. Fujii, Kasami and Ninomiya solve the two-processor case by matching (doi:10.1137/0117070, erratum doi:10.1137/0120018).
  • 1972. Coffman and Graham give an optimal list-scheduling algorithm for two processors (doi:10.1007/BF00288685).
  • 1975. Ullman proves NP-completeness when the number of machines is part of the input (doi:10.1016/S0022-0000(75)80008-0).
  • 1979. Garey and Johnson list the fixed-three-machine problem as OPEN8; Graham, Lawler, Lenstra and Rinnooy Kan introduce the standard notation (doi:10.1016/S0167-5060(08)70356-X).
  • 1983. Garey, Johnson, Tarjan and Yannakakis give a linear-time algorithm on three processors for opposing forests (doi:10.1137/0604011).
  • 1984. Dolev and Warmuth give an O(nh(m−1)+1)O(n^{h(m-1)+1})O(nh(m−1)+1) algorithm for precedence graphs of height hhh (doi:10.1016/0196-6774(84)90039-7).
  • 2016–2022. Approximation schemes for fixed machine counts: Levey and Rothvoß via LP hierarchies (doi:10.1145/2897518.2897532), Garg's quasi-PTAS (doi:10.4230/LIPIcs.ICALP.2018.59), Li (doi:10.1137/1.9781611976465.178), Das and Wiese (doi:10.4230/LIPIcs.ESA.2022.40).
  • 2025. Nederlof, Swennenhuis and Węgrzycki give an exact algorithm in time 2O(nlog⁡n)2^{O(\sqrt n\log n)}2O(n​logn) for three machines (doi:10.1137/1.9781611978322.16).

The source of this mission, an OpenAI preprint dated September 24, 2026, claims a deterministic polynomial-time algorithm.

Setting

The input is a directed acyclic graph G=(V,E)G=(V,E)G=(V,E) on V={1,…,n}V=\{1,\dots,n\}V={1,…,n}, n≥1n\ge1n≥1, given as an explicit list of arcs, and optionally an integer deadline TTT with 1≤T≤n1\le T\le n1≤T≤n. A feasible schedule with makespan at most TTT is a map

τ:V→{1,…,T}\tau:V\to\{1,\dots,T\}τ:V→{1,…,T}

such that each value is taken at most three times (three machines) and τ(u)<τ(v)\tau(u)<\tau(v)τ(u)<τ(v) for every arc (u,v)∈E(u,v)\in E(u,v)∈E (precedence). Slot ttt means execution during [t−1,t)[t-1,t)[t−1,t). There are no release dates, communication delays or other resources. The input length LLL is the length of the binary encoding of the graph and deadline.

Formalization targets

Goal: Theorem 1.1

There is a single deterministic multitape Turing machine MMM and a constant CCC such that, on every such input of length LLL, MMM halts within

C (L+2)150020C\,(L+2)^{150020}C(L+2)150020

steps and

  • without a deadline, outputs a feasible schedule of minimum makespan;
  • with a deadline TTT, outputs "infeasible" exactly when no feasible schedule with makespan at most TTT exists, and otherwise outputs one.

The exponent is the one stated in the paper and is not optimized; the content of the theorem is that a fixed polynomial bound exists. The Lean statement OAI.ThreeMachine.main_theorem is open on the platform.

Significance

The theorem resolves the polynomial-time side of Garey and Johnson's OPEN8: unit-job makespan scheduling with arbitrary precedence constraints on three identical machines lies in P. Previous exact algorithms were either restricted to special precedence structures (forests, bounded height) or subexponential, and approximation schemes did not decide the optimum. The structural statement behind the algorithm, that feasible schedules admit recursive decompositions whose interval job sets have descriptions of bounded size, may be of independent use for other fixed-machine scheduling problems. No practical running time is claimed.

The result is claimed in an OpenAI preprint; it has not been peer reviewed and no machine-checked proof exists. A formal proof would also certify the running-time analysis in an explicit machine model, which is rarely done for algorithmic results of this size.

Difficulty

Removing selected slots from a feasible schedule leaves gaps that can be rescheduled independently, which suggests a recursive search over the job sets of gaps. The obstacle is their number: naming a gap by its two boundary triples does not determine its job set, and intersecting descriptions inherited from ancestors can make the descriptions grow without bound along a recursion of linear depth. The subexponential decomposition of Nederlof–Swennenhuis–Węgrzycki controls the number of subproblems only up to 2O(nlog⁡n)2^{O(\sqrt n\log n)}2O(n​logn); a polynomial bound needs every subproblem to have a global description of constant size.

Formalization scope

  • Instance n is a list of arcs on Fin n; acyclic forbids a Relation.TransGen cycle. Jobs are Fin n (shifted to 1,…,n1,\dots,n1,…,n in the encoding).
  • Feasible G T τ: slots in [1,T][1,T][1,T], at most three jobs per slot, strict precedence.
  • CorrectOutput distinguishes the optimization mode (none) and the decision mode (some T).
  • The machine model is a custom multitape Turing machine Machine k q g over Fin (g+3) with k+1k+1k+1 tapes; input on tape 0 encoded by encodeInput (self-delimiting binary numbers), output read from tape 0 after halting. The time bound is C (L+2)150020C\,(L+2)^{150020}C(L+2)150020 with LLL the encoded length.
  • The quantifiers are ordered so that the machine and constant are fixed before the instance (uniformity).

A complete development needs the combinatorics of the decomposition (separators and bounded global descriptions), a dynamic program over polynomially many descriptions, and its implementation and time analysis on the given Turing machine model. Contributions formalizing the purely combinatorial structure theorem, independently of the machine model, are welcome.

Selected references

  • OpenAI, A Polynomial-Time Algorithm for Three-Machine Unit-Job Scheduling, preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/A-polynomial-time-algorithm-for-three-machine-unit-job-scheduling-September-24-2026/paper.pdf
  • M. R. Garey, D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman, 1979.
  • E. G. Coffman Jr., R. L. Graham, Optimal scheduling for two-processor systems, Acta Inform., 1972. https://doi.org/10.1007/BF00288685
  • J. D. Ullman, NP-complete scheduling problems, J. Comput. System Sci., 1975. https://doi.org/10.1016/S0022-0000(75)80008-0
  • D. Dolev, M. K. Warmuth, Scheduling precedence graphs of bounded height, J. Algorithms, 1984. https://doi.org/10.1016/0196-6774(84)90039-7
  • J. Nederlof, C. M. F. Swennenhuis, K. Węgrzycki, A subexponential time algorithm for makespan scheduling of unit jobs with precedence constraints, SODA 2025. https://doi.org/10.1137/1.9781611978322.16
  • E. Levey, T. Rothvoß, A (1+ϵ)(1+\epsilon)(1+ϵ)-approximation for makespan scheduling with precedence constraints using LP hierarchies, STOC 2016. https://doi.org/10.1145/2897518.2897532
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An FPRAS for Cell-Bounded Contingency TablesResearch Paper

Motivation

A cell-bounded contingency table is a nonnegative integer matrix with prescribed row sums, column sums and an upper bound on each entry. Bounds of zero forbid entries (structural zeros), and bounds of one give bipartite graphs with prescribed degrees, so the model contains degree-constrained subgraph counting and perfect matchings (the permanent). Counting such tables is central to exact conditional inference in statistics and is equivalent to counting integer flows in bipartite networks. Exact counting is #P-complete even for two-row ordinary tables (Dyer, Kannan and Mount, 1997), so the target is a fully polynomial randomized approximation scheme (FPRAS): a randomized algorithm that, for any ε,δ∈(0,1)\varepsilon,\delta\in(0,1)ε,δ∈(0,1), returns a (1±ε)(1\pm\varepsilon)(1±ε)-approximation with probability at least 1−δ1-\delta1−δ in time polynomial in the input length, 1/ε1/\varepsilon1/ε and log⁡(1/δ)\log(1/\delta)log(1/δ).

Timeline

  • 1986. Jerrum, Valiant and Vazirani relate approximate counting and sampling through self-reducibility.
  • 1995. Diaconis and Gangolli survey fixed-margin arrays (doi:10.1007/978-1-4612-0801-3_3).
  • 1997. Dyer, Kannan and Mount prove #P-completeness for two-row tables and give geometric methods for large margins; Morris improves the margin requirements in 2002 (doi:10.1002/rsa.10049).
  • 2000–2003. Dyer and Greenhill (two rows), Cryan and Dyer (fixed number of rows), Dyer (dynamic programming) give approximate counting for a bounded number of rows.
  • 2004. Jerrum, Sinclair and Vigoda's permanent algorithm yields an FPRAS for all 0/1 caps (doi:10.1145/1008731.1008738).
  • 2009–2010. Barvinok bounds weighted table counts within NO(m+n)N^{O(m+n)}NO(m+n) (doi:10.1093/imrn/rnn133); Barvinok, Luria, Samorodnitsky and Yong give quasipolynomial randomized approximations for smooth margins (doi:10.1002/rsa.20301).
  • 2010. Cryan, Dyer and Randall give an FPRAS for integral flows with large tight capacities and for cell-bounded tables with a fixed number of rows, and list unrestricted approximation as open (doi:10.1137/060650544).
  • 2023. Brändén, Leake and Pak give capacity lower bounds via Lorentzian polynomials (doi:10.1007/s11856-022-2364-9); Guo and Jerrum count vertices of certain 0/1 polytopes (doi:10.1007/s00454-022-00406-8).

The source of this mission, an OpenAI preprint dated September 24, 2026, claims an FPRAS with no restriction on dimensions, margins or caps.

Setting

For positive integers m,nm,nm,n, margins r∈Z≥0mr\in\mathbb Z_{\ge0}^mr∈Z≥0m​, c∈Z≥0nc\in\mathbb Z_{\ge0}^nc∈Z≥0n​ with equal totals, and bounds b∈Z≥0m×nb\in\mathbb Z_{\ge0}^{m\times n}b∈Z≥0m×n​,

Ω(r,c,b)={X∈Z≥0m×n: ∑jXij=ri, ∑iXij=cj, Xij≤bij},Z(r,c,b)=∣Ω(r,c,b)∣.\Omega(r,c,b)=\Bigl\{X\in\mathbb Z_{\ge0}^{m\times n}:\ \textstyle\sum_jX_{ij}=r_i,\ \sum_iX_{ij}=c_j,\ X_{ij}\le b_{ij}\Bigr\},\qquad Z(r,c,b)=|\Omega(r,c,b)|.Ω(r,c,b)={X∈Z≥0m×n​: ∑j​Xij​=ri​, ∑i​Xij​=cj​, Xij​≤bij​},Z(r,c,b)=∣Ω(r,c,b)∣.

All numbers are written in binary, so a capacity can be exponentially larger than its description. LLL denotes the total binary input length including the rational accuracy parameters ε,δ\varepsilon,\deltaε,δ. The algorithm uses independent unbiased random bits and is charged for every bit operation.

Formalization targets

Goal: Theorem 1.1 (FPRAS)

One randomized algorithm, given (r,c,b)(r,c,b)(r,c,b) and rational ε,δ∈(0,1)\varepsilon,\delta\in(0,1)ε,δ∈(0,1), outputs a nonnegative rational Z^\widehat ZZ with

Pr⁡[(1−ε)Z(r,c,b)≤Z^≤(1+ε)Z(r,c,b)]≥1−δ,\Pr\bigl[(1-\varepsilon)Z(r,c,b)\le\widehat Z\le(1+\varepsilon)Z(r,c,b)\bigr]\ge1-\delta,Pr[(1−ε)Z(r,c,b)≤Z≤(1+ε)Z(r,c,b)]≥1−δ,

outputs exactly 000 on every execution when Ω(r,c,b)=∅\Omega(r,c,b)=\emptysetΩ(r,c,b)=∅, and performs at most poly(L,ε−1,log⁡δ−1)\mathrm{poly}(L,\varepsilon^{-1},\log\delta^{-1})poly(L,ε−1,logδ−1) bit operations on every execution. Lean: OAI.ContingencyTables.counting, open on the platform.

Significance

The theorem would answer the question left open by Cryan, Dyer and Randall in 2010: approximate counting of cell-bounded tables (equivalently, integral flows in bipartite networks with arbitrary capacities) without any fixed-dimension, large-capacity, density or balance assumption, with polynomial cost on every execution and no oracle. It contains the 0/1 case handled by Jerrum–Sinclair–Vigoda and the fixed-row results as special cases, and via self-reduction it gives approximate samplers for integer flows. A companion preprint of the same family treats exact uniform sampling of uncapped tables. The result is claimed in an OpenAI preprint that has not been peer reviewed; no machine-checked proof exists.

Difficulty

Two regimes clash. Large capacities cannot be expanded into that many binary choices without exponential blow-up, so geometric (polytope volume) methods are needed for them; but those methods require every cell to be large. Small capacities need combinatorial Markov chains, which in turn must preserve exactly one unit of mass per table despite the multiplicities created by encoding a bounded integer by binary choices. Arbitrary mixtures of tiny and huge caps, zero caps and variable dimensions defeat each approach used alone.

Formalization scope

  • Algorithms are MatchingFPRAS.RandomMachines: a single finite transition table on an 8-symbol alphabet, one random bit per tick; time is counted in ticks (polynomially equivalent to bit operations).
  • Inputs are delimited binary encodings of m,n,r,c,bm,n,r,c,bm,n,r,c,b and of ε,δ\varepsilon,\deltaε,δ as reduced fractions (encodeCountingInput); outputs are encoded rationals.
  • count r c b is Fintype.card of bounded tables.
  • CountingStatement: one machine and constants C>0C>0C>0, ddd; with t=C(∣input∣+⌈1/ε⌉+⌈log⁡2⌈1/δ⌉⌉+1)dt=C(|\text{input}|+\lceil1/\varepsilon\rceil+\lceil\log_2\lceil1/\delta\rceil\rceil+1)^dt=C(∣input∣+⌈1/ε⌉+⌈log2​⌈1/δ⌉⌉+1)d, every tape of length ttt halts with a rational q≥0q\ge0q≥0; if count = 0 every tape outputs 000; and the fraction of tapes with (1−ε)Z≤q≤(1+ε)Z(1-\varepsilon)Z\le q\le(1+\varepsilon)Z(1−ε)Z≤q≤(1+ε)Z is at least 1−δ1-\delta1−δ.
  • m,n≥1m,n\ge1m,n≥1 and equal margin totals are hypotheses, as in the paper.

Needed infrastructure: randomized Turing machines, Markov chain mixing and Poincaré inequalities, log-concavity (Prékopa–Leindler), and network-flow feasibility. The machine model and encodings are shared with the exact-sampling mission of this family. Contributions formalizing Proposition 2.2 (contamination bound), Lemma 3.3 (log-concave tree marginals), Proposition 6.1 (weight evaluation) or Proposition 7.2 are welcome.

Selected references

  • OpenAI, An FPRAS for Cell-Bounded Contingency Tables, preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/An-FPRAS-for-Cell-Bounded-Contingency-Tables-September-24-2026/main.pdf
  • M. Cryan, M. Dyer, D. Randall, Approximately counting integral flows and cell-bounded contingency tables, SIAM J. Comput., 2010. https://doi.org/10.1137/060650544
  • M. Jerrum, A. Sinclair, E. Vigoda, A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries, J. ACM, 2004. https://doi.org/10.1145/1008731.1008738
  • B. Morris, Improved bounds for sampling contingency tables, Random Structures Algorithms, 2002. https://doi.org/10.1002/rsa.10049
  • A. Barvinok, Asymptotic estimates for the number of contingency tables, integer flows, and volumes of transportation polytopes, IMRN, 2009. https://doi.org/10.1093/imrn/rnn133
  • A. Barvinok, Z. Luria, A. Samorodnitsky, A. Yong, An approximation algorithm for counting contingency tables, Random Structures Algorithms, 2010. https://doi.org/10.1002/rsa.20301
  • P. Brändén, J. Leake, I. Pak, Lower bounds for contingency tables via Lorentzian polynomials, Israel J. Math., 2023. https://doi.org/10.1007/s11856-022-2364-9
  • H. Guo, M. Jerrum, Counting vertices of integral polytopes defined by facets, Discrete Comput. Geom., 2023. https://doi.org/10.1007/s00454-022-00406-8
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Exact Uniform Sampling of Contingency Tables with Arbitrary MarginsResearch Paper

Motivation

A contingency table with margins r=(r1,…,rm)r=(r_1,\dots,r_m)r=(r1​,…,rm​) and c=(c1,…,cn)c=(c_1,\dots,c_n)c=(c1​,…,cn​) is a nonnegative integer matrix whose row sums are rrr and column sums are ccc. Such tables are the basic objects of categorical data analysis: conditional tests of independence (Fisher's exact test and its generalizations) compare an observed table with the uniform distribution on all tables with the same margins, and in practice this requires sampling uniformly from that set. The set is finite but can be exponentially large in the input size when margins are written in binary. Whether one can sample it (approximately or exactly) in time polynomial in both dimensions and in the bit length of the margins has been open since the 1990s.

Timeline

  • 1995. Diaconis and Gangolli survey enumeration and random generation of rectangular arrays with fixed margins (doi:10.1007/978-1-4612-0801-3_3).
  • 1997–2002. Dyer, Kannan and Mount sample dense tables via the transportation polytope; Morris improves the dense bounds (doi:10.1002/rsa.10049).
  • 2000. Dyer and Greenhill give polynomial-time approximate counting and sampling for two-rowed tables.
  • 2003. Kijima and Matsui give exact sampling for two rows by coupling from the past; Cryan and Dyer approximately count for a fixed number of rows (doi:10.1016/S0022-0000(03)00014-X); Dyer gives an exact dynamic-programming sampler for fixed row count.
  • 2006. Cryan, Dyer, Goldberg, Jerrum and Martin prove rapid mixing of the heat-bath chain for every fixed number of rows (doi:10.1137/S0097539703434243).
  • 2016. DeSalvo and Zhao give an exact divide-and-conquer sampler whose polynomial runtime is conditional on a conjecture (arXiv:1507.00070).
  • 2021. Arman, Gao and Wormald give linear-time exact generation for sparse margins with 5Δ4<N5\Delta^4<N5Δ4<N, and report that no polynomial-time approximately uniform sampler for arbitrary margins was known (arXiv:2104.09413).
  • 2024. Göbel, Liu, Manurangsi and Pappik turn sufficiently accurate samplers into perfect samplers (arXiv:2410.00882).

The source of this mission, an OpenAI preprint dated September 24, 2026, claims both an almost-uniform and an exactly uniform sampler for arbitrary margins.

Setting

For nonnegative integer vectors r∈Z≥0mr\in\mathbb Z_{\ge0}^mr∈Z≥0m​, c∈Z≥0nc\in\mathbb Z_{\ge0}^nc∈Z≥0n​ with common total N=∑iri=∑jcjN=\sum_ir_i=\sum_jc_jN=∑i​ri​=∑j​cj​, let

Ω(r,c)={X∈Z≥0m×n: ∑jXij=ri, ∑iXij=cj}.\Omega(r,c)=\Bigl\{X\in\mathbb Z_{\ge0}^{m\times n}:\ \textstyle\sum_jX_{ij}=r_i,\ \sum_iX_{ij}=c_j\Bigr\}.Ω(r,c)={X∈Z≥0m×n​: ∑j​Xij​=ri​, ∑i​Xij​=cj​}.

The input is (m,n,r,c)(m,n,r,c)(m,n,r,c) with margins in binary, so its length is polynomial in mmm, nnn and log⁡(N+1)\log(N+1)log(N+1). An algorithm uses unbiased random bits. For laws on a finite set, TV(μ,ν)=12∑x∣μ(x)−ν(x)∣\mathrm{TV}(\mu,\nu)=\tfrac12\sum_x|\mu(x)-\nu(x)|TV(μ,ν)=21​∑x​∣μ(x)−ν(x)∣.

Formalization targets

Milestone: Theorem 1.1(i) (almost-uniform, bounded time)

For each k≥1k\ge1k≥1, an algorithm halts on every execution in time poly(m,n,log⁡(N+1),k)\mathrm{poly}(m,n,\log(N+1),k)poly(m,n,log(N+1),k) and outputs X∈Ω(r,c)X\in\Omega(r,c)X∈Ω(r,c) with

TV(law(X), Unif Ω(r,c))≤2−k.\mathrm{TV}\bigl(\mathrm{law}(X),\ \mathrm{Unif}\,\Omega(r,c)\bigr)\le2^{-k}.TV(law(X), UnifΩ(r,c))≤2−k.

Goal: Theorem 1.1(ii) (exact uniform sampling)

An algorithm outputs X∈Ω(r,c)X\in\Omega(r,c)X∈Ω(r,c) with

Pr⁡[X=Y]=1∣Ω(r,c)∣(Y∈Ω(r,c)),\Pr[X=Y]=\frac1{|\Omega(r,c)|}\quad(Y\in\Omega(r,c)),Pr[X=Y]=∣Ω(r,c)∣1​(Y∈Ω(r,c)),

terminates almost surely, and has expected running time poly(m,n,log⁡(N+1))\mathrm{poly}(m,n,\log(N+1))poly(m,n,log(N+1)), with polynomials uniform over all margins. Lean: OAI.ContingencyTables.exactSampling, open on the platform.

Significance

The theorem would give the first unconditional polynomial-time exact uniform sampler for contingency tables with arbitrary dimensions and binary margins, with no positivity, sparsity, balance or fixed-dimension restriction, and with all random bits and integer arithmetic counted. It removes the fixed-row restriction of the Dyer–Greenhill and Cryan–Dyer line and the sparsity condition of Arman–Gao–Wormald. The exponents are deliberately large; the contribution is the existence of a polynomial algorithm. Exact time is polynomial in expectation, not on every run. A companion preprint of the same family gives an FPRAS for counting tables with cell bounds. The result is claimed in an OpenAI preprint that has not been peer reviewed; no machine-checked proof exists.

Difficulty

Markov-chain approaches mix rapidly for a fixed number of rows, but the bounds degrade with the row count. Geometric (polytope) methods need all margins to be large, and fail when some margins are tiny while others are huge, which is exactly the mixed regime that arbitrary binary margins allow. Making the sampler exact rather than approximate adds a separate difficulty: the residual-mixture correction requires that the actual output law of the approximate sampler can be computed, with cost polynomial in the accuracy bits.

Formalization scope

  • Algorithms are MatchingFPRAS.RandomMachines: a single finite transition table over an 8-symbol tape alphabet; each tick reads one random bit and either moves, writes or halts. Time is counted in ticks, polynomially equivalent to bit operations.
  • Inputs are binary encodings with delimiters (encodeNat, encodeMargins); outputs are row-major matrix encodings left on the tape (OutputsTable).
  • tableMass A input t X and haltMass A input t are the fractions of the 2t2^t2t random tapes of length ttt that halt with output XXX, respectively halt at all, within ttt ticks.
  • ExactSamplingStatement: one machine and constants C>0C>0C>0, ddd such that for all m,n,r,cm,n,r,cm,n,r,c with equal totals: every halted output is in Ω(r,c)\Omega(r,c)Ω(r,c); tableMass → 1/|Ω| for each table; haltMass → 1; and ∑t(1−haltMasst)\sum_t(1-\text{haltMass}_t)∑t​(1−haltMasst​), the expected running time, is at most C(m+n+⌈log⁡2(N+1)⌉+1)dC(m+n+\lceil\log_2(N+1)\rceil+1)^dC(m+n+⌈log2​(N+1)⌉+1)d.
  • BoundedSamplingStatement: every tape of length t=C(m+n+⌈log⁡2(N+1)⌉+k+1)dt=C(m+n+\lceil\log_2(N+1)\rceil+k+1)^dt=C(m+n+⌈log2​(N+1)⌉+k+1)d halts with a feasible table, and the TV distance to uniform is at most 2−k2^{-k}2−k.
  • Dimensions m,nm,nm,n vary freely (zero allowed); the machine is uniform, not one per size.

Needed infrastructure: randomized Turing machines and their output laws, Markov chain mixing (conductance, Poincaré inequalities), and log-concave sampling. Contributions formalizing Theorem 4.3 (graph Poincaré inequality), Theorem 5.1 (dense completion draw), Proposition 6.3 (law tabulation) or the Göbel–Liu–Manurangsi–Pappik correction are welcome.

Selected references

  • OpenAI, Exact Uniform Sampling of Contingency Tables with Arbitrary Margins, preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/main.pdf
  • P. Diaconis, A. Gangolli, Rectangular arrays with fixed margins, Discrete Probability and Algorithms, 1995. https://doi.org/10.1007/978-1-4612-0801-3_3
  • B. Morris, Improved bounds for sampling contingency tables, Random Structures Algorithms, 2002. https://doi.org/10.1002/rsa.10049
  • M. Cryan, M. Dyer, A polynomial-time algorithm to approximately count contingency tables when the number of rows is constant, J. Comput. System Sci., 2003. https://doi.org/10.1016/S0022-0000(03)00014-X
  • M. Cryan, M. Dyer, L. A. Goldberg, M. Jerrum, R. Martin, Rapidly mixing Markov chains for sampling contingency tables with a constant number of rows, SIAM J. Comput., 2006. https://doi.org/10.1137/S0097539703434243
  • S. DeSalvo, J. Y. Zhao, Random sampling of contingency tables via probabilistic divide-and-conquer, preprint, 2016. https://arxiv.org/abs/1507.00070
  • A. Arman, P. Gao, N. Wormald, Linear-time uniform generation of random sparse contingency tables with specified marginals, preprint, 2021. https://arxiv.org/abs/2104.09413
  • A. Göbel, J. Liu, P. Manurangsi, M. Pappik, Perfect sampling from rapidly mixing Markov chains, preprint, 2024. https://arxiv.org/abs/2410.00882
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Uniform computation of the squared-logarithmic k-server boundResearch Paper

Motivation

In the kkk-server problem, kkk labelled servers occupy points of a metric space, requests arrive online, and each request is served by moving one server onto it; the cost is the total distance moved, compared with the offline optimum through the competitive ratio. A companion OpenAI preprint claims that on every metric there exists a randomized policy with ratio O(log⁡2(k+1))O(\log^2(k+1))O(log2(k+1)) against oblivious requests, matching the known worst-case lower bound. That result is an existence statement: it gives no procedure for computing the policy's probabilities.

This mission asks whether the bound can be realized by one uniform algorithm with explicit bit complexity on all finite rational metrics, as stated in an OpenAI preprint dated September 24, 2026 (source). The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open.

Background

  • 1988–1990 — Manasse, McGeoch and Sleator introduce the kkk-server problem and prove the deterministic lower bound kkk (STOC 1988; J. Algorithms 1990).
  • 1991 — Paging (the uniform metric): Fiat, Karp, Luby, McGeoch, Sleator and Young give the randomized marking algorithm and the harmonic lower bound (J. Algorithms 1991); McGeoch and Sleator attain HkH_kHk​ exactly (Algorithmica 1991). This suggested the randomized kkk-server conjecture, an O(log⁡k)O(\log k)O(logk) ratio on every metric.
  • 1995 — Koutsoupias and Papadimitriou prove that the work function algorithm is (2k−1)(2k-1)(2k−1)-competitive (J. ACM 1995).
  • 1996–2004 — Probabilistic tree embeddings: Bartal (FOCS 1996) and Fakcharoenphol–Rao–Talwar with distortion O(log⁡n)O(\log n)O(logn) (JCSS 2004).
  • 2012–2015 — Bansal, Buchbinder and Naor give O(log⁡k)O(\log k)O(logk) for weighted paging (J. ACM 2012); Bansal, Buchbinder, Mądry and Naor give the first polylogarithmic bound O(log⁡2klog⁡3nlog⁡log⁡n)O(\log^2k\log^3n\log\log n)O(log2klog3nloglogn) on arbitrary finite metrics (J. ACM 2015).
  • 2018 — Bubeck, Cohen, Lee, Lee and Mądry achieve O(log⁡2k)O(\log^2 k)O(log2k) on hierarchically separated trees, hence O(log⁡2klog⁡n)O(\log^2k\log n)O(log2klogn) on nnn-point metrics (STOC 2018).
  • 2023 — Bubeck, Coester and Rabani disprove the randomized kkk-server conjecture: some (k+1)(k+1)(k+1)-point metrics need ratio Ω(log⁡2k)\Omega(\log^2k)Ω(log2k) (STOC 2023).
  • 2026 — Coester and Cosson make the tree and finite-metric bounds polynomial-time, with ratio O(log⁡nlog⁡2k)O(\log n\log^2k)O(lognlog2k) on general metrics (ICALP 2026).
  • September 2026 — Two OpenAI preprints claim the matching upper bound O(log⁡2(k+1))O(\log^2(k+1))O(log2(k+1)) on every metric space (source) and a uniform bit-level implementation on finite rational metrics (source).

Setting

Fix n≥3n\ge3n≥3 and 2≤k<n2\le k<n2≤k<n. A rational metric on X={1,…,n}X=\{1,\dots,n\}X={1,…,n} is a table d(x,y)∈Qd(x,y)\in\mathbb Qd(x,y)∈Q that is nonnegative, zero exactly on the diagonal, symmetric, and satisfies the triangle inequality. An initial labelled tuple s∈Xks\in X^ks∈Xk may have repetitions. For a request word σ\sigmaσ, costA,s(σ)\mathrm{cost}_{A,s}(\sigma)costA,s​(σ) is the algorithm's total movement and OPTs(σ)\mathrm{OPT}_s(\sigma)OPTs​(σ) the minimum over all choices of serving labels with knowledge of σ\sigmaσ. LLL denotes the total binary length of the encoding of (n,k,d,s)(n,k,d,s)(n,k,d,s).

The computational model is a bit machine: a finite control with an input tape, finitely many work tapes over {0,1,blank}\{0,1,\text{blank}\}{0,1,blank}, an output channel, and one fresh unbiased random bit per step. At each request the input tape is replaced by the encoded request, the internal state is retained, and the output after the step budget names the server to move.

Formalization targets

Goal: Theorem 1.1 (p. 1)

There are an absolute constant CCC and a polynomial ppp such that a single uniform randomized online algorithm, on every instance (n,k,d,s)(n,k,d,s)(n,k,d,s) as above,

  • preprocesses the instance in at most p(L)p(L)p(L) bit operations;
  • serves request rtr_trt​, for every reachable history and internal state, in at most p(L+⌈log⁡2(t+1)⌉)p\bigl(L+\lceil\log_2(t+1)\rceil\bigr)p(L+⌈log2​(t+1)⌉) further bit operations, outputting a valid server label and moving exactly that server;
  • for every fixed finite request sequence σ\sigmaσ independent of its random bits satisfies
E costA,s(σ) ≤ C (log⁡(k+1))2 OPTs(σ)+Bd,k,s,\mathbb E\,\mathrm{cost}_{A,s}(\sigma)\ \le\ C\,\bigl(\log(k+1)\bigr)^2\,\mathrm{OPT}_s(\sigma)+B_{d,k,s},EcostA,s​(σ) ≤ C(log(k+1))2OPTs​(σ)+Bd,k,s​,

with Bd,k,s<∞B_{d,k,s}<\inftyBd,k,s​<∞ independent of σ\sigmaσ and of its length.

Significance

The result itself. It converts the existence theorem into an algorithm whose ratio is independent of nnn, using only unbiased random bits and polynomial work per request in the input length and the bit length of the request counter. The price is an additive constant Bd,k,sB_{d,k,s}Bd,k,s​ with no size bound: the algorithm may serve a very long initial stretch with a fixed label while its constructor runs. Coester and Cosson obtain stronger time and randomness guarantees but with ratio O(log⁡nlog⁡2k)O(\log n\log^2k)O(lognlog2k) on general metrics; the two results are incomparable.

Formalizing it. The goal is stated in an explicit machine model, so a formal proof certifies both the competitive bound and the bit-complexity bound. It needs the companion existence theorem (Theorem 2.1 here, p. 4) as an input; a full development therefore also requires formalizing that theorem or taking it as a separate milestone. Exact rational linear algebra (Fourier–Motzkin elimination), dyadic rounding of probabilities, and step-counted machine simulation are reusable.

Difficulty

Knowing that a good policy exists does not give its probabilities, and a finite horizon alone does not control a long input on which the optimum barely moves. The preprint (pp. 3–4) solves a rational linear system for each horizon, then filters requests so that only those missed by some strongly lazy service with bounded moves are retained, which bounds the retained word length; a marking fallback pays for trajectories exceeding the move cap. The constructor's running time is uncontrolled, so the algorithm simulates it for only ⌊log⁡2(t+1)⌋\lfloor\log_2(t+1)\rfloor⌊log2​(t+1)⌋ steps at request ttt (Proposition 4.2, p. 14), and the delayed activation is absorbed into Bd,k,sB_{d,k,s}Bd,k,s​.

Formalization scope

  • RationalMetric n is a Fin n → Fin n → ℚ table with the metric axioms; configurations are Fin k → Fin n; offlineCost is the sInf over label histories matching the request word (a finite, nonempty set of costs).
  • BitMachine has finitely many controls and tapes and a transition consuming one coin per step. Preprocessing (boot) runs c(L+1)ec(L+1)^ec(L+1)e steps with all coins 000, so it is deterministic; this is satisfied by the source's deterministic constructor, and Theorem 1.1 does not require randomness there.
  • Each request runs exactly requestBudget c e L t =c(L+⌈log⁡2(t+1)⌉+1)e=c(L+\lceil\log_2(t+1)\rceil+1)^e=c(L+⌈log2​(t+1)⌉+1)e steps (Nat.clog 2 (t+1)) and must have yielded with output value <k<k<k for every reachable state and every coin string; requests are encoded by a fixed self-delimiting binary code.
  • machineExpectedCost averages over all coin strings of the step budget, i.e. uniformly random bits; the bound is required for every request list w, with B≥0B\ge0B≥0 chosen after (d,s)(d,s)(d,s) and before w.
  • The machine, the polynomial (c,e)(c,e)(c,e) and CCC are fixed before nnn, kkk, ddd, sss: the algorithm is uniform.

Selected references

  • OpenAI, Uniform computation of the squared-logarithmic k-server bound, OpenAI Math Release preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Uniform-computation-of-the-squared-logarithmic-k-server-bound-September-24-2026/Uniform-computation-of-the-squared-logarithmic-k-server-bound-September-24-2026.pdf
  • OpenAI, Squared-logarithmic randomized k-server on arbitrary metrics, OpenAI Math Release preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Squared-logarithmic-randomized-k-server-on-arbitrary-metrics-September-24-2026/Squared-logarithmic-randomized-k-server-on-arbitrary-metrics-September-24-2026.pdf
  • D. Komm, R. Královič, R. Královič, T. Mömke, Randomized online computation with high probability guarantees, Algorithmica (2022). https://doi.org/10.1007/s00453-022-00925-z
  • G. B. Dantzig, B. C. Eaves, Fourier–Motzkin elimination and its dual, J. Combin. Theory Ser. A 14 (1973). https://doi.org/10.1016/0097-3165(73)90004-6
  • M. S. Manasse, L. A. McGeoch, D. D. Sleator, Competitive algorithms for server problems, J. Algorithms 11 (1990). https://doi.org/10.1016/0196-6774(90)90003-W
  • E. Koutsoupias, C. H. Papadimitriou, On the k-server conjecture, J. ACM 42 (1995). https://doi.org/10.1145/210118.210128
  • A. Fiat, R. M. Karp, M. Luby, L. A. McGeoch, D. D. Sleator, N. E. Young, Competitive paging algorithms, J. Algorithms 12 (1991). https://doi.org/10.1016/0196-6774(91)90041-V
  • N. Bansal, N. Buchbinder, A. Mądry, J. Naor, A polylogarithmic-competitive algorithm for the k-server problem, J. ACM 62 (2015). https://doi.org/10.1145/2783434
  • S. Bubeck, M. B. Cohen, J. R. Lee, Y. T. Lee, A. Mądry, k-server via multiscale entropic regularization, STOC 2018. https://doi.org/10.1145/3188745.3188798
  • S. Bubeck, C. Coester, Y. Rabani, The randomized k-server conjecture is false!, STOC 2023. https://doi.org/10.1145/3564246.3585132
  • C. Coester, R. Cosson, Randomized k-server in polynomial time, ICALP 2026. https://doi.org/10.4230/LIPIcs.ICALP.2026.65
  • J. Fakcharoenphol, S. Rao, K. Talwar, A tight bound on approximating arbitrary metrics by tree metrics, JCSS 69 (2004). https://doi.org/10.1016/j.jcss.2004.04.011
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Complexity TheoryLinear algebraTheoretical Computer Science·Captain: wurtle

Staggered extraction for exact matrix multiplication over every fieldResearch Paper

Motivation: the matrix-multiplication exponent

Multiplying two n×nn\times nn×n matrices by the schoolbook method costs about n3n^3n3 operations. The matrix-multiplication exponent ωF\omega_FωF​ of a field FFF is the infimum of exponents τ\tauτ such that n×nn\times nn×n matrices over FFF can be multiplied with O(nτ+o(1))O(n^{\tau+o(1)})O(nτ+o(1)) additions, subtractions and multiplications. It governs the asymptotic cost of a central bilinear operation and of the many algorithms built from it (determinants, inversion, linear systems). Bounding ω\omegaω has driven algebraic complexity theory since 1969. In recent years the bounds have moved in the third and fourth decimal place, with each improvement coming from a finer analysis of the same tensor-power framework.

Background

  • 1969 — Strassen's seven-product algorithm gives ω≤log⁡27\omega\le\log_27ω≤log2​7 (Strassen 1969).
  • 1980–1987 — Approximate bilinear algorithms (Bini 1980), the asymptotic sum inequality (Schönhage 1981) and the laser method (Strassen 1987).
  • 1990 — Coppersmith and Winograd introduce the tensor family CWq\mathrm{CW}_qCWq​ and progression-free extraction (J. Symbolic Comput. 1990).
  • 2010–2014 — Higher tensor powers: Stothers (fourth power; with Davie, Proc. Roy. Soc. Edinburgh 2013), Vassilevska Williams (eighth power, STOC 2012), Le Gall (thirty-second power, ISSAC 2014).
  • 2023–2024 — The refined laser method gives ω<2.3728596\omega<2.3728596ω<2.3728596 (Alman–Vassilevska Williams, TheoretiCS 2024). Asymmetric hashing reduces combination loss (Duan–Wu–Zhou). Finer variable sharing follows (Vassilevska Williams–Xu–Xu–Zhou).
  • 2024–2026 — Successive asymmetric compatibility tests (Alman–Duan–Vassilevska Williams–Xu–Xu–Zhou, arXiv:2404.16349). Dupont et al. optimize the eighth-power instance of this framework to ω<2.371177\omega<2.371177ω<2.371177 (arXiv:2608.16884).
  • 2026 — An OpenAI preprint, Staggered extraction for exact matrix multiplication over every field (OpenAI Math Release, September 24, 2026), claims ωF<2.371054886006746\omega_F<2.371054886006746ωF​<2.371054886006746 for every fixed field FFF, including every positive characteristic. It has not been peer reviewed, and the claim has not been formally verified.

Setting

Fix a field FFF. An arithmetic program is a straight-line sequence of gates, each a constant from FFF, an input entry, or the sum, difference or product of two earlier gates. Its cost counts the addition, subtraction and multiplication gates. A program multiplies n×nn\times nn×n matrices if designated registers equal the entries of ABABAB for every pair of input matrices A,BA,BA,B over FFF. A real number τ\tauτ is admissible if for every ε>0\varepsilon>0ε>0 there is C>0C>0C>0 such that for every n≥1n\ge1n≥1 some correct program has cost at most C nτ+εC\,n^{\tau+\varepsilon}Cnτ+ε, and

ωF=inf⁡{τ∈R: τ admissible over F}.\omega_F=\inf\{\tau\in\mathbb R:\ \tau\text{ admissible over }F\}.ωF​=inf{τ∈R: τ admissible over F}.

In Lean this is MatrixAllFields.MatrixMultiplication.Arithmetic.omega F, defined for every F : Type u with [Field F].

Formalization targets

Goal: the exponent bound over every field (Theorem 1.1)

∀ F field:ωF<2.371054886006746.\forall\,F\ \text{field}:\qquad \omega_F<2.371054886006746 .∀F field:ωF​<2.371054886006746.

The Lean statement also contains the numerical comparison 2.371054886006746<2.3710562.371054886006746<2.3710562.371054886006746<2.371056, which carries no mathematical content (the source writes the comparison as <2.371054887<2.371054887<2.371054887). This is Theorem 1.1 of the source. The goal is published on the platform with status Open: no machine-checked proof exists yet.

Significance

The result itself. If the source's argument and certificate are correct, the theorem lowers the best stated bound on ω\omegaω from 2.3711772.3711772.371177 (Dupont et al.) to 2.3710552.3710552.371055, and it does so uniformly over all fields, with exact algorithms in every positive characteristic. Any improvement in ω\omegaω lowers the exponent of every algorithm reducible to matrix multiplication. The method — pooling directional capacities across extraction depths and staggering lots so that one lot sits at each splitting stage — shows that combination losses in the asymmetric framework can be traded across depths.

Formalizing it. Bounds of this type rest on long chains of tensor-power constructions, recovery arguments and large rational parameter tables, checked in the source by an accompanying verifier with interval certificates for logarithms. A machine-checked proof would certify the whole chain from the arithmetic model to the final rational inequality. It would also give Mathlib the asymptotic sum inequality and Coppersmith–Winograd machinery, which every future bound on ω\omegaω needs.

Difficulty

Every recent improvement fights extraction losses. Restricting a tensor power to a direct sum of independent matrix products wastes variables whenever whole blocks must be disjoint, while the final products only need disjoint subsets. Prescribed marginals may also allow several joint distributions. Earlier analyses take the minimum of three directional capacities separately at each depth, so a shortage on one side at one depth is lost. Using capacity across depths requires that variables deleted in earlier extractions (to resolve conflicts or impose type conditions) can still be recovered exactly, over the original field and with only subexponentially many extra copies. Positive characteristic rules out arguments that divide by small integers or rely on characteristic-zero identities.

Formalization scope

  • The model is division-free straight-line programs over an arbitrary field F : Type u, with free constants from FFF and unit cost for add, sub and mul. This matches the source's "scalar additions and multiplications".
  • AdmissibleExponent F τ asks, for each ε>0\varepsilon>0ε>0, for one constant C>0C>0C>0 that works for every n≥1n\ge1n≥1. omega F is the sInf of the admissible set, which is nonempty (schoolbook algorithm) and bounded below by the output size, so a junk value cannot make the strict inequality true.
  • The goal quantifies over every field, including finite fields, so a proof cannot use characteristic-zero arguments or embeddings into C\mathbb CC.
  • The second conjunct is a trivially true rational comparison and can be ignored.
  • Infrastructure needed: tensor rank and border rank over arbitrary fields, the asymptotic sum inequality, the Coppersmith–Winograd tensor and its powers, method-of-types counting, and verified rational/logarithm interval arithmetic for the parameter certificate. All of this is reusable for other matrix-multiplication results.

Selected references

  • V. Strassen, Gaussian elimination is not optimal, Numer. Math. 13 (1969), 354–356. https://doi.org/10.1007/BF02165411
  • D. Bini, Relations between exact and approximate bilinear algorithms. Applications, Calcolo 17 (1980), 87–97. https://doi.org/10.1007/BF02575865
  • A. Schönhage, Partial and total matrix multiplication, SIAM J. Comput. 10 (1981), 434–455. https://doi.org/10.1137/0210032
  • V. Strassen, Relative bilinear complexity and matrix multiplication, J. reine angew. Math. 375/376 (1987), 406–443. https://doi.org/10.1515/crll.1987.375-376.406
  • D. Coppersmith and S. Winograd, Matrix multiplication via arithmetic progressions, J. Symbolic Comput. 9 (1990), 251–280. https://doi.org/10.1016/S0747-7171(08)80013-2
  • A. M. Davie and A. J. Stothers, Improved bound for complexity of matrix multiplication, Proc. Roy. Soc. Edinburgh Sect. A 143 (2013), 351–369. https://doi.org/10.1017/S0308210511001648
  • V. Vassilevska Williams, Multiplying matrices faster than Coppersmith–Winograd, STOC 2012, 887–898. https://doi.org/10.1145/2213977.2214056
  • F. Le Gall, Powers of tensors and fast matrix multiplication, ISSAC 2014, 296–303. https://arxiv.org/abs/1401.7714v1
  • J. Alman and V. Vassilevska Williams, A refined laser method and faster matrix multiplication, TheoretiCS 3 (2024), Art. 21. https://doi.org/10.46298/theoretics.24.21
  • R. Duan, H. Wu and R. Zhou, Faster matrix multiplication via asymmetric hashing, arXiv:2210.10173. https://arxiv.org/abs/2210.10173v5
  • V. Vassilevska Williams, Y. Xu, Z. Xu and R. Zhou, New bounds for matrix multiplication: from alpha to omega, arXiv:2307.07970. https://arxiv.org/abs/2307.07970v2
  • J. Alman, R. Duan, V. Vassilevska Williams, Y. Xu, Z. Xu and R. Zhou, More asymmetry yields faster matrix multiplication, arXiv:2404.16349v3. https://arxiv.org/abs/2404.16349v3
  • E. Dupont et al., Improving the matrix multiplication exponent with modern optimization, arXiv:2608.16884 (2026). https://doi.org/10.48550/arXiv.2608.16884
  • OpenAI, Staggered extraction for exact matrix multiplication over every field, OpenAI Math Release preprint, September 24, 2026 (Theorem 1.1, p. 2). https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Staggered-extraction-for-exact-matrix-multiplication-over-every-field-September-24-2026/Staggered-extraction-for-exact-matrix-multiplication-over-every-field-September-24-2026.pdf
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Perfect completeness for 2-to-1 gamesResearch Paper

Motivation

A projection game (label cover) asks for labels on the two sides of a bipartite multigraph so that, on each edge, the right label is the image of the left label under a prescribed map. In a 2-to-1 game the left alphabet has 2q2q2q labels, the right alphabet qqq, and every edge map has exactly two preimages of each right label. Khot introduced the 2-to-1 and Unique Games conjectures in 2002 (STOC 2002) as hypotheses that yield tight hardness of approximation. The 2-to-1 Games Conjecture with perfect completeness asserts that, for every fixed δ>0\delta>0δ>0, it is NP-hard to tell satisfiable 2-to-1 games from games in which every labeling satisfies at most a δ\deltaδ fraction of edges. Perfect completeness matters for problems whose YES instances must be exactly satisfiable, such as graph coloring: hardness with completeness 1−ε1-\varepsilon1−ε does not imply it.

This mission asks for a formal proof of the conjecture, as stated in an OpenAI preprint dated September 23, 2026 (source). The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open.

Background

  • 1998 — The PCP theorem (Arora–Safra, JACM 1998; Arora–Lund–Motwani–Sudan–Szegedy, JACM 1998) and Raz's parallel repetition theorem (SICOMP 1998).
  • 2002 — Khot formulates the 2-to-1 and Unique Games conjectures.
  • 2011 — Rao's parallel repetition for projection games (SICOMP 2011).
  • 2014 — Austrin, O'Donnell, Tan and Wright prove perfect-completeness 2-to-1 label cover hardness with alphabets of sizes 666 and 333 and soundness 23/24+ε23/24+\varepsilon23/24+ε (ACM TOCT 2014).
  • 2017–2023 — The Grassmann/shortcode programme: Khot–Minzer–Safra (STOC 2017; ToC 2025), Dinur–Khot–Kindler–Minzer–Safra (STOC 2018), Barak–Kothari–Steurer (ITCS 2019), and the Grassmann expansion theorem of Khot–Minzer–Safra (Annals 2023), giving 2-to-1 hardness with completeness arbitrarily close to 111.
  • 2026 — Fei, Minzer and Wang prove perfect-completeness hardness for 4-to-1 games at arbitrarily small soundness (ECCC TR26-179).
  • September 2026 — The OpenAI preprint claims perfect completeness for 2-to-1 games (Theorem 1.1, p. 1).

Setting

For an integer q≥2q\ge2q≥2, a 2-to-1 instance has finite left and right vertex sets and a nonempty list of edge occurrences e=(ue,ve,πe)e=(u_e,v_e,\pi_e)e=(ue​,ve​,πe​), where πe:[2q]→[q]\pi_e:[2q]\to[q]πe​:[2q]→[q] is given by an explicit table with ∣πe−1(b)∣=2|\pi_e^{-1}(b)|=2∣πe−1​(b)∣=2 for every b∈[q]b\in[q]b∈[q]. Parallel occurrences, possibly with different maps, are allowed and counted with multiplicity; there are no weights. A labeling (a,b)(a,b)(a,b) assigns a(u)∈[2q]a(u)\in[2q]a(u)∈[2q] and b(v)∈[q]b(v)\in[q]b(v)∈[q] and satisfies eee when πe(a(ue))=b(ve)\pi_e(a(u_e))=b(v_e)πe​(a(ue​))=b(ve​). The value is the maximum, over labelings, of the fraction of satisfied occurrences. 3-SAT inputs are bit strings that decode to conjunctions of three-literal clauses (repetitions allowed); malformed inputs count as unsatisfiable.

Formalization targets

Goal: Theorem 1.1 (p. 1)

For every fixed rational δ∈(0,1)\delta\in(0,1)δ∈(0,1) there are an integer q=q(δ)≥2q=q(\delta)\ge2q=q(δ)≥2 and a deterministic polynomial-time reduction φ↦Gφ\varphi\mapsto G_\varphiφ↦Gφ​ from 3-SAT to 2-to-1 instances with alphabets [2q][2q][2q] and [q][q][q] such that

φ satisfiable ⇒ val⁡(Gφ)=1,φ unsatisfiable ⇒ val⁡(Gφ)≤δ.\varphi\ \text{satisfiable}\ \Rightarrow\ \operatorname{val}(G_\varphi)=1,\qquad \varphi\ \text{unsatisfiable}\ \Rightarrow\ \operatorname{val}(G_\varphi)\le\delta .φ satisfiable ⇒ val(Gφ​)=1,φ unsatisfiable ⇒ val(Gφ​)≤δ.

The running time is polynomial in the binary input length, with degree and constants depending only on δ\deltaδ.

Significance

The result itself. It removes the conditional hypothesis from perfect-completeness reductions. The preprint derives NP-hardness for maximum kkk-colorable subgraph at soundness 1−1k+Clog⁡kk21-\frac1k+C\frac{\log k}{k^2}1−k1​+Ck2logk​ through Guruswami and Sinop's reduction (Corollary 1.2, p. 3), and recovers satisfiable Not-Two and three-query PCP hardness (Corollary 1.3, p. 4), the latter already known unconditionally by Håstad (SICOMP 2014). A companion preprint uses it for hardness of finding large independent sets in three-colorable graphs.

Formalizing it. No PCP-style hardness result of this strength has been machine-checked. A complete development needs a perfect-completeness PCP gap (Theorem 2.1, p. 5), projection-game parallel repetition (Theorem 2.2, p. 6), the Grassmann expansion theorem (Theorem 2.3, p. 7) and inverse shortcode estimates (Theorem 2.6, p. 9), on top of a polynomial-time Turing-machine framework. These components are shared with the Unique Games formalization and would be reusable.

Difficulty

Near-perfect completeness does not give perfect completeness: the 1−ε1-\varepsilon1−ε reductions need not produce any finite satisfiable game. Amplifying a constant-soundness perfect-completeness game by parallel repetition destroys the 2-to-1 structure, since kkk-fold repetition gives 2k2^k2k preimages per right label. The preprint therefore builds a new tree-structured construction (Section 3) whose joint-partition questions keep two-element fibers and satisfy exact identities, and the soundness analysis must decode labelings through a rank test whose tested rows depend on the decoded form itself (Sections 5–6), before projection-game repetition gives the contradiction (Section 7).

Formalization scope

  • ProjectionTable q is a Vector (Fin q) (2*q) with exactly two preimages of every b : Fin q. Instance q lists edges as a List with a nonemptiness proof, so value = maxSatisfied / edges.length never divides by zero; maxSatisfied is a Finset.sup over all labelings.
  • BinaryGapReduction δ fixes alphabet ≥ 2, a construct map, a Turing.TM2ComputableInPolyTime witness from raw input bits (identity encoding) to the explicit bit encoding gameBits, finite tape alphabets, completeness value = 1 on encodings of satisfiable formulas, and soundness value ≤ δ on all other inputs.
  • The goal quantifies over rational δ\deltaδ with 0<δ<10<\delta<10<δ<1, as in the source; the alphabet and machine are fixed before the input.
  • No vacuous reading: the 3-SAT language is nontrivial and value = 1 requires a labeling satisfying every edge.

Selected references

  • OpenAI, Perfect completeness for 2-to-1 games, OpenAI Math Release preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Perfect-completeness-for-2-to-1-games-September-23-2026/paper.pdf
  • S. Khot, On the power of unique 2-prover 1-round games, STOC 2002. https://doi.org/10.1145/509907.510017
  • P. Austrin, R. O'Donnell, L.-Y. Tan, J. Wright, New NP-hardness results for 3-coloring and 2-to-1 label cover, ACM Trans. Comput. Theory 6 (2014).
  • S. Khot, D. Minzer, M. Safra, Pseudorandom sets in Grassmann graph have near-perfect expansion, Ann. of Math. 198 (2023). https://doi.org/10.4007/annals.2023.198.1.1
  • I. Dinur, S. Khot, G. Kindler, D. Minzer, M. Safra, Towards a proof of the 2-to-1 games conjecture?, STOC 2018. https://doi.org/10.1145/3188745.3188804
  • B. Barak, P. K. Kothari, D. Steurer, Small-set expansion in shortcode graph and the 2-to-2 conjecture, ITCS 2019. https://doi.org/10.4230/LIPIcs.ITCS.2019.9
  • Y. Fei, D. Minzer, S. Wang, On the hardness of 4-to-1 games with perfect completeness, ECCC TR26-179 (2026). https://eccc.weizmann.ac.il/report/2026/179/
  • A. Rao, Parallel repetition in projection games and a concentration bound, SIAM J. Comput. 40 (2011). https://doi.org/10.1137/080734042
  • R. Raz, A parallel repetition theorem, SIAM J. Comput. (1998). https://doi.org/10.1137/S0097539795280895
  • V. Guruswami, A. K. Sinop, Improved inapproximability results for maximum k-colorable subgraph, Theory Comput. 9 (2013). https://doi.org/10.4086/toc.2013.v009a011
  • M. Braverman, S. Khot, D. Minzer, On rich 2-to-1 games, ITCS 2021. https://doi.org/10.4230/LIPIcs.ITCS.2021.27
  • J. Håstad, On the NP-hardness of Max-Not-2, SIAM J. Comput. (2014). https://doi.org/10.1137/120882718
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Complexity TheoryGraph TheoryTheoretical Computer Science·Captain: wurtle

Constant-factor hardness of directed feedback vertex setResearch Paper

Motivation

A directed feedback vertex set of a finite digraph GGG is a set FFF of vertices meeting every directed cycle, equivalently one whose removal leaves G−FG-FG−F acyclic; DFVS(G)\mathrm{DFVS}(G)DFVS(G) is the minimum size of such a set. The problem arises wherever cyclic dependencies must be broken — deadlock resolution, circuit testing, scheduling — and a topological order of G−FG-FG−F certifies a solution in polynomial time.

The best general approximation guarantee is O(log⁡nlog⁡log⁡n)O(\log n\log\log n)O(lognloglogn), originating in Seymour's work on fractional packing of directed circuits (Combinatorica 1995) and made constructive by Even, Naor, Schieber and Sudan (Algorithmica 1998). On the hardness side, NP-hardness was known only for particular constants (via Vertex Cover), while ruling out every constant factor required the Unique Games Conjecture. Whether a constant-factor approximation exists under P ≠ NP alone was open.

This mission asks for a machine-checked proof that approximating DFVS within any fixed factor A≥1A\ge1A≥1 is NP-hard on unweighted digraphs, as claimed in an OpenAI preprint dated September 23, 2026 (source, Theorem 1.1, p. 2). The preprint has not been peer reviewed and its claim has not been independently verified; on the platform the Lean goal is open.

Timeline

  • 1995–1998 — Seymour's fractional packing bound (Combinatorica 1995) and the algorithm of Even–Naor–Schieber–Sudan (Algorithmica 1998) give O(log⁡nlog⁡log⁡n)O(\log n\log\log n)O(lognloglogn) approximation.
  • 2005 — Dinur and Safra prove Vertex Cover NP-hard below 105−21≈1.3610\sqrt5-21\approx1.36105​−21≈1.36 (Annals 2005); doubling each undirected edge transfers this to DFVS.
  • 2008–2011 — Guruswami, Manokaran and Raghavendra show every constant factor is Unique-Games-hard via maximum acyclic subgraph and feedback arc set (FOCS 2008); expanded with Håstad and Charikar (SICOMP 2011).
  • 2013 — Svensson's direct Unique-Games-based construction for vertex deletion problems (ToC 2013).
  • 2016 — Guruswami and Lee give a simpler UG-based proof (ToC 2016).
  • 2018–2025 — The imperfect-completeness 2-to-1 Games Theorem is completed (Dinur–Khot–Kindler–Minzer–Safra, ToC 2025; Barak–Kothari–Steurer, ITCS 2019; Khot–Minzer–Safra, ECCC TR18-006), giving Vertex Cover, hence DFVS, hardness below 2\sqrt22​.
  • 2026 — Ghorbani and Mnich record the gap for general digraphs (ICALP 2026); the OpenAI preprint claims NP-hardness for every constant factor.

Setting

A digraph is finite and loopless, given by a vertex count nnn and a duplicate-free list of arcs (u,v)(u,v)(u,v) with u≠vu\ne vu=v; opposite arcs (u,v),(v,u)(u,v),(v,u)(u,v),(v,u) are allowed. A directed cycle is a cyclic sequence of r+1≥2r+1\ge2r+1≥2 distinct vertices with every successive arc present, including the closing one. FFF is a feedback set if it meets every directed cycle, and DFVS(G)\mathrm{DFVS}(G)DFVS(G) is the least size of one.

A language L⊆{0,1}∗L\subseteq\{0,1\}^*L⊆{0,1}∗ is in NP if there are a polynomial ppp and a deterministic polynomial-time verifier VVV (a multi-stack Turing machine with finite alphabets) with x∈Lx\in Lx∈L iff some certificate www with ∣w∣≤p(∣x∣)|w|\le p(|x|)∣w∣≤p(∣x∣) makes V(x,w)V(x,w)V(x,w) accept.

Formalization targets

Goal: constant-factor hardness (Theorem 1.1, p. 2)

For every real A≥1A\ge1A≥1 and every language L∈NPL\in\mathrm{NP}L∈NP there is a deterministic polynomial-time map x↦(Gx,kx)x\mapsto(G_x,k_x)x↦(Gx​,kx​) with kx≥1k_x\ge1kx​≥1 such that

x∈L ⇒ DFVS(Gx)≤kx,x∉L ⇒ DFVS(Gx)>A kx.x\in L\ \Rightarrow\ \mathrm{DFVS}(G_x)\le k_x,\qquad x\notin L\ \Rightarrow\ \mathrm{DFVS}(G_x)>A\,k_x .x∈L ⇒ DFVS(Gx​)≤kx​,x∈/L ⇒ DFVS(Gx​)>Akx​.

In Lean this is OAI.DirectedFeedback.main : DirectedFeedback.MainStatement. The preprint states Theorem 1.1 as a reduction from an NP-hard promise problem (the 2-to-1 game gap of Theorem 2.1, p. 5) to (G,k)(G,k)(G,k); composing with that promise problem's NP-hardness gives the Lean form, which quantifies over all NP languages directly. A deterministic polynomial-time AAA-approximation would then decide every NP language (proof of Theorem 1.1, p. 28).

The preprint also proves Corollary 8.3 (p. 28) for directed feedback arc set; it is not formalized on the platform.

Significance

The result itself. Theorem 1.1 shows that a constant-factor approximation for DFVS exists if and only if P = NP, removing the Unique Games hypothesis from the Guruswami–Manokaran–Raghavendra and Svensson results. It does not determine the true growth of the optimal factor between constants and O(log⁡nlog⁡log⁡n)O(\log n\log\log n)O(lognloglogn).

Formalizing it. No machine-checked inapproximability result of this kind is known to exist. A complete development needs the 2-to-1 Games Theorem with imperfect completeness (Theorem 2.1, p. 5, external input), Friedgut's junta theorem in a dyadic fixed-bias form (Theorem 2.2 and Corollary 2.3, p. 6), finite Ramsey theory and von Neumann's minimax theorem, and the preprint's rank-graph construction (Definition 3.1, p. 8; Proposition 3.7, p. 12), pivotality budget (Definition 6.1, p. 20) and weighted soundness (Proposition 7.4, p. 25). An NP and Karp-reduction library on TM2 machines (including Cook–Levin) would be reusable across the whole family of hardness missions.

Difficulty

The central difficulty (Introduction, pp. 3–4) is extracting a game labeling from an arbitrary acyclic remainder: a topological order exists, but it can interleave the many vertices representing one local test arbitrarily. Comparisons in that order must first be made consistent and then read as Boolean functions; Friedgut-type junta bounds must hold at many biases simultaneously for the same random function, so a separate influence bound at each scale does not suffice. The coordinate lists must also be fixed before the tested game edge is drawn, or they do not define a game labeling. Gadget reductions from Vertex Cover cannot go beyond the Vertex Cover constants.

Formalization scope

  • DirectedFeedback.Digraph: n : ℕ, a Nodup list of arcs in Fin n × Fin n, loopless; cycles are injective maps Fin (r+1) → Fin n with all successive arcs (including the wrap-around) present, so 2-cycles count and 1-cycles cannot occur. dfvs is the least cardinality of a feedback Finset.
  • NP is defined from scratch by NPVerifier: a polynomial certificate bound and a polynomial-time TM2 verifier with finite alphabets on the unary-framed pair encoding pairBits; no SAT-specific definition is used.
  • GapReduction A L is a polynomial-time TM2 machine (finite alphabets, raw-bit input) producing a GapInstance (digraph plus k > 0), encoded explicitly in unary-framed form by GapInstance.bits; completeness and soundness are as displayed, with soundness compared in ℝ.
  • The statement quantifies over all NP languages, so its proof must include (or reprove) NP-hardness of the source promise problem in this machine model — e.g. a Cook–Levin theorem for TM2 verifiers plus the 2-to-1 Games reduction.
  • k > 0 and the strict inequality rule out a trivial reading (e.g. empty graphs with k = 0).

Welcome contributions: Friedgut's junta theorem, finite Ramsey and minimax in usable forms, the 2-to-1 Games Theorem, and Cook–Levin for Turing.TM2ComputableInPolyTime.

Selected references

  • OpenAI, Constant-factor hardness of directed feedback vertex set, OpenAI Math Release preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Constant-factor-hardness-of-directed-feedback-vertex-set-September-23-2026/paper.pdf
  • P. D. Seymour, Packing directed circuits fractionally, Combinatorica 15 (1995). https://doi.org/10.1007/BF01200760
  • G. Even, J. Naor, B. Schieber, M. Sudan, Approximating minimum feedback sets and multicuts in directed graphs, Algorithmica (1998). https://doi.org/10.1007/PL00009191
  • I. Dinur, S. Safra, On the hardness of approximating minimum vertex cover, Ann. of Math. 162 (2005). https://doi.org/10.4007/annals.2005.162.439
  • V. Guruswami, R. Manokaran, P. Raghavendra, Beating the random ordering is hard: inapproximability of maximum acyclic subgraph, FOCS 2008. https://doi.org/10.1109/FOCS.2008.51
  • O. Svensson, Hardness of vertex deletion and project scheduling, Theory of Computing 9 (2013). https://doi.org/10.4086/toc.2013.v009a024
  • V. Guruswami, E. Lee, Simple proof of hardness of feedback vertex set, Theory of Computing 12 (2016). https://doi.org/10.4086/toc.2016.v012a006
  • I. Dinur, S. Khot, G. Kindler, D. Minzer, M. Safra, Towards a proof of the 2-to-1 games conjecture?, Theory of Computing 21 (2025). https://doi.org/10.4086/toc.2025.v021a011
  • S. Khot, D. Minzer, M. Safra, Pseudorandom sets in Grassmann graph have near-perfect expansion, ECCC TR18-006 (2018). https://eccc.weizmann.ac.il/report/2018/006/revision/2/download
  • E. Friedgut, Boolean functions with low average sensitivity depend on few coordinates, Combinatorica (1998). https://doi.org/10.1007/PL00009809
  • F. P. Ramsey, On a problem of formal logic, Proc. London Math. Soc. (1930). https://doi.org/10.1112/plms/s2-30.1.264
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The Factor-Two Hardness Threshold for Vertex CoverResearch Paper

Motivation

A vertex cover of a graph is a set of vertices meeting every edge; τ(G)\tau(G)τ(G) denotes the minimum size of one. Computing τ(G)\tau(G)τ(G) exactly is NP-hard, but a two-line algorithm — take both endpoints of a maximal matching — always returns a cover of size at most 2τ(G)2\tau(G)2τ(G). Whether any polynomial-time algorithm achieves a fixed factor 2−ε2-\varepsilon2−ε is one of the best-known questions in approximation algorithms. Unconditional NP-hardness was known below 105−21≈1.3610\sqrt5-21\approx1.36105​−21≈1.36 and, after the 2-to-2 Games Theorem, below 2\sqrt22​; factor-two hardness was known only under Khot's Unique Games Conjecture.

This mission asks for a machine-checked proof that every fixed approximation factor below two is NP-hard, as claimed in an OpenAI preprint dated September 23, 2026 (source, Corollary 1.2, p. 1). The preprint's direct reduction starts from ordinary perfect-completeness Label Cover. It has not been peer reviewed and its claim has not been independently verified; on the platform the Lean goal is open.

Timeline

  • 1972 — Karp proves Node Cover NP-complete (Karp 1972); the maximal-matching algorithm gives factor 222.
  • 1992–1998 — Feige–Goldwasser–Lovász–Safra–Szegedy (JACM 1996) link proof checking to approximation; the PCP theorem (Arora–Safra, JACM 1998; Arora–Lund–Motwani–Sudan–Szegedy, JACM 1998) gives a constant gap; Raz's parallel repetition (SICOMP 1998) yields Label Cover with arbitrarily small soundness.
  • 2001 — Håstad: NP-hard below factor 7/67/67/6 (JACM 2001).
  • 2005 — Dinur–Safra: NP-hard below 105−21≈1.3606810\sqrt5-21\approx1.36068105​−21≈1.36068 (Annals 2005).
  • 2002–2008 — Khot's Unique Games Conjecture (STOC 2002); Khot–Regev prove factor-(2−ε)(2-\varepsilon)(2−ε) hardness assuming it (JCSS 2008).
  • 2009 — Karakostas's algorithm achieves 2−Θ(1/log⁡n)2-\Theta(1/\sqrt{\log n})2−Θ(1/logn​) (TALG 2009), a saving that vanishes with nnn.
  • 2017–2023 — The 2-to-2 Games programme (Khot–Minzer–Safra, ToC 2025; Dinur–Khot–Kindler–Minzer–Safra, ToC 2025; Barak–Kothari–Steurer, ITCS 2019; Khot–Minzer–Safra expansion, Annals 2023) gives NP-hardness below 2\sqrt22​.
  • 2026 — The OpenAI preprint claims NP-hardness for every fixed factor below 222.

Setting

A graph is finite, simple, undirected and unweighted, given explicitly by its vertex count nnn and a duplicate-free list of edges {u,v}\{u,v\}{u,v} with u<vu<vu<v. A set SSS of vertices is a cover if every edge has an endpoint in SSS, and τ(G)\tau(G)τ(G) is the least size of a cover. An α\alphaα-approximation algorithm is a deterministic polynomial-time machine that, on every graph, outputs a duplicate-free list of vertices that covers all edges and has length at most α τ(G)\alpha\,\tau(G)ατ(G).

3SAT inputs are bit strings decoding canonically to a conjunction of three-slot clauses; malformed strings are NO instances. A 3SAT decider is a deterministic polynomial-time machine answering membership correctly on every input.

Formalization targets

Milestone: the explicit cover gap (Theorem 1.1, p. 1)

For every integer m≥4m\ge4m≥4 there is a deterministic polynomial-time reduction φ↦Gφ\varphi\mapsto G_\varphiφ↦Gφ​ from 3SAT to explicit simple graphs with

φ satisfiable⇒τ(Gφ)<(12+1m)∣V(Gφ)∣,φ unsatisfiable⇒τ(Gφ)>(1−1m)∣V(Gφ)∣.\varphi\text{ satisfiable}\Rightarrow \tau(G_\varphi)<\Bigl(\tfrac12+\tfrac1m\Bigr)|V(G_\varphi)|,\qquad \varphi\text{ unsatisfiable}\Rightarrow \tau(G_\varphi)>\Bigl(1-\tfrac1m\Bigr)|V(G_\varphi)|.φ satisfiable⇒τ(Gφ​)<(21​+m1​)∣V(Gφ​)∣,φ unsatisfiable⇒τ(Gφ​)>(1−m1​)∣V(Gφ​)∣.

The ratio of the thresholds is 2−6/(m+2)→22-6/(m+2)\to22−6/(m+2)→2. In Lean: OAI.VertexCover.explicit_cover_gap.

Goal: factor-two threshold (Corollary 1.2, p. 1)

For every real α\alphaα with 1≤α<21\le\alpha<21≤α<2, an α\alphaα-approximation algorithm for Vertex Cover yields a polynomial-time 3SAT decider:

∀ α∈[1,2):Approximation(α)  ⟹  ThreeSATDecision.\forall\,\alpha\in[1,2):\quad \mathrm{Approximation}(\alpha)\;\Longrightarrow\;\mathrm{ThreeSATDecision}.∀α∈[1,2):Approximation(α)⟹ThreeSATDecision.

In Lean: OAI.VertexCover.every_fixed_factor_below_two. The preprint derives it from Theorem 1.1 in its final section (pp. 23–24) by choosing mmm from α\alphaα and comparing the returned cover with an exact rational threshold. Together with the matching algorithm, it identifies 222 as the approximation threshold unless P = NP.

Significance

The result itself. The statement removes the Unique Games hypothesis from the Khot–Regev theorem and closes the gap between the elementary factor-222 algorithm and hardness. The density gap of Theorem 1.1 is also a statement about independent sets: YES graphs have independent sets of density nearly 1/21/21/2, NO graphs have none of density above 1/m1/m1/m.

Formalizing it. No machine-checked proof of any super-constant Vertex Cover hardness bound is known to exist. A complete development requires the perfect-completeness Label Cover theorem (Theorem 2.1, p. 4; PCP theorem plus parallel repetition, used as an external input), the explicit graph construction and its polynomial-time implementation (Proposition 3.3, p. 7), and the preprint's analytic soundness argument. The Label Cover input and a library for polynomial-time gap reductions on TM2 machines are reusable for many other hardness missions.

Difficulty

Soundness requires extracting short lists of candidate labels from an arbitrary large independent set, where each list may depend only on its own position's query. If a list also sees the opposite endpoint of a constraint or its occurrence index, the pair-decoding argument against Label Cover soundness breaks down (Introduction, pp. 3–4). Classical approaches either need the stronger Unique Games source promise (Khot–Regev) or only reach densities near 1−1/21-1/\sqrt21−1/2​ (the 2-to-2 route), which gives factor 2\sqrt22​ rather than 222. Averaging to impose the information restriction can erase all variance; the analytic core is retaining it without any dependence on the label alphabet sizes.

Formalization scope

  • Graphs are VertexCover.ExplicitGraph: n : ℕ and a Nodup list of pairs in Fin n × Fin n with e.1 < e.2; coverNumber is the least cardinality of a covering Finset (always defined since univ covers).
  • Encodings: inputs are raw bits; graphs are encoded by ExplicitGraph.bits (vertex count, all vertex names, edge count, edge endpoints, each with prefix-free binary framing); an approximation's output list is encoded by natListBits.
  • Approximation α bundles a polynomial-time TM2 program on graph encodings with finite tape alphabets, whose output is duplicate-free, in range, covers every edge and has length at most α * coverNumber. ThreeSATDecision is a polynomial-time TM2 decider correct on every input.
  • The goal is an implication from an algorithm to a decider: no assumption P ≠ NP is built in, and the hypothesis 1≤α<21\le\alpha<21≤α<2 is satisfied by real algorithms only if 3SAT is easy, which is exactly the content. It cannot be discharged trivially because Approximation α must be correct on every graph.
  • The milestone's thresholds are compared in ℝ with strict inequalities as in the source.

Welcome contributions: Label Cover hardness (PCP theorem + parallel repetition), Efron–Stein decompositions and variance inequalities on product spaces, and general complexity-theoretic infrastructure for Turing.TM2ComputableInPolyTime.

Selected references

  • OpenAI, The Factor-Two Hardness Threshold for Vertex Cover, OpenAI Math Release preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/The-Factor-Two-Hardness-Threshold-for-Vertex-Cover-September-23-2026/paper.pdf
  • R. M. Karp, Reducibility among combinatorial problems, Complexity of Computer Computations, 1972. https://doi.org/10.1007/978-1-4684-2001-2_9
  • J. Håstad, Some optimal inapproximability results, J. ACM 48 (2001). https://doi.org/10.1145/502090.502098
  • I. Dinur, S. Safra, On the hardness of approximating minimum vertex cover, Ann. of Math. 162 (2005). https://doi.org/10.4007/annals.2005.162.439
  • S. Khot, O. Regev, Vertex cover might be hard to approximate to within 2−ε, J. Comput. Syst. Sci. 74 (2008). https://doi.org/10.1016/j.jcss.2007.06.019
  • G. Karakostas, A better approximation ratio for the Vertex Cover problem, ACM Trans. Algorithms (2009). https://doi.org/10.1145/1597036.1597045
  • R. Raz, A parallel repetition theorem, SIAM J. Comput. (1998). https://doi.org/10.1137/S0097539795280895
  • S. Khot, D. Minzer, M. Safra, Pseudorandom sets in Grassmann graph have near-perfect expansion, Ann. of Math. 198 (2023). https://doi.org/10.4007/annals.2023.198.1.1
  • I. Dinur, S. Khot, G. Kindler, D. Minzer, M. Safra, Towards a proof of the 2-to-1 games conjecture?, Theory of Computing 21 (2025). https://doi.org/10.4086/toc.2025.v021a011
  • S. Khot, On the power of unique 2-prover 1-round games, STOC 2002. https://doi.org/10.1145/509907.510017
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A Direct Proof of Optimal Max-Cut HardnessResearch Paper

Motivation

Max-Cut asks for a partition of the vertices of a graph that maximizes the number of edges crossing it. Its decision version is one of Karp's original NP-complete problems, so the natural question is how well it can be approximated in polynomial time. Goemans and Williamson's semidefinite-programming algorithm with random-hyperplane rounding achieves ratio approaching

αGW=min⁡−1≤ρ<12arccos⁡ρπ(1−ρ)=0.878567…\alpha_{\mathrm{GW}}=\min_{-1\le\rho<1}\frac{2\arccos\rho}{\pi(1-\rho)}=0.878567\ldotsαGW​=−1≤ρ<1min​π(1−ρ)2arccosρ​=0.878567…

(GW 1995). Whether any polynomial-time algorithm can beat this constant is a central question of approximation algorithms: unconditional NP-hardness was known only above 16/1716/1716/17, and optimality of αGW\alpha_{\mathrm{GW}}αGW​ was known only under Khot's Unique Games Conjecture.

This mission asks for a machine-checked proof that approximating Max-Cut within any fixed ratio α∈(αGW,1]\alpha\in(\alpha_{\mathrm{GW}},1]α∈(αGW​,1] is NP-hard, even on simple unweighted graphs, as claimed in an OpenAI preprint dated September 23, 2026 (source, Theorem 1.1, p. 1). The preprint gives a direct reduction from the 2-to-1 Games Theorem, not via the Unique Games Conjecture. It has not been peer reviewed and its claim has not been independently verified; on the platform the Lean goal is open.

Timeline

  • 1972 — Karp lists Max-Cut among the original NP-complete problems (Karp 1972).
  • 1995 — Goemans and Williamson give the SDP algorithm with ratio αGW\alpha_{\mathrm{GW}}αGW​ (JACM 1995).
  • 2000–2001 — Håstad's optimal inapproximability results (JACM 2001) combined with the gadgets of Trevisan, Sorkin, Sudan and Williamson (SICOMP 2000) rule out ratios above 16/1716/1716/17.
  • 2002 — Feige and Schechtman show the SDP integrality gap approaches αGW\alpha_{\mathrm{GW}}αGW​ (RSA 2002); Khot formulates the Unique Games Conjecture (STOC 2002).
  • 2007–2010 — Khot, Kindler, Mossel and O'Donnell prove that αGW\alpha_{\mathrm{GW}}αGW​ is optimal assuming the UGC, conditional on Majority Is Stablest (SICOMP 2007), which Mossel, O'Donnell and Oleszkiewicz prove (Annals 2010) via Borell's Gaussian noise-stability bound (1985). O'Donnell–Wu (STOC 2008) and Raghavendra (STOC 2008) extend the UG-based picture.
  • 2017–2025 — The Grassmann-graph programme establishes the 2-to-1 Games Theorem with imperfect completeness: Khot–Minzer–Safra (ToC 2025), Dinur–Khot–Kindler–Minzer–Safra (ToC 2025), Barak–Kothari–Steurer (ITCS 2019), Khot–Minzer–Safra expansion (Annals 2023).
  • 2026 — The OpenAI preprint claims unconditional NP-hardness at every ratio above αGW\alpha_{\mathrm{GW}}αGW​.

Setting

A simple unweighted graph on NNN vertices is given by a symmetric, loopless Boolean adjacency table. A cut is a map ccc from the vertices to {0,1}\{0,1\}{0,1}; its size is the number of unordered edges {u,v}\{u,v\}{u,v} with c(u)≠c(v)c(u)\ne c(v)c(u)=c(v), and MaxCut(G)\mathrm{MaxCut}(G)MaxCut(G) is the largest cut size. An algorithm is an α\alphaα-approximation if it always returns a cut of size at least α⋅MaxCut(G)\alpha\cdot\mathrm{MaxCut}(G)α⋅MaxCut(G).

3SAT inputs are bit strings decoding canonically to a conjunction of three-slot clauses (repeated variables and literals allowed); malformed strings are NO instances. A gap reduction for α\alphaα consists of rationals Y>0Y>0Y>0 and N≥0N\ge0N≥0 with N<αYN<\alpha YN<αY, and a deterministic polynomial-time map φ↦(Gφ,Qφ)\varphi\mapsto(G_\varphi,Q_\varphi)φ↦(Gφ​,Qφ​) outputting a graph together with a positive integer scale QφQ_\varphiQφ​ such that

φ∈3SAT⇒MaxCut(Gφ)Qφ≥Y,φ∉3SAT⇒MaxCut(Gφ)Qφ≤N.\varphi\in\mathrm{3SAT}\Rightarrow \frac{\mathrm{MaxCut}(G_\varphi)}{Q_\varphi}\ge Y,\qquad \varphi\notin\mathrm{3SAT}\Rightarrow \frac{\mathrm{MaxCut}(G_\varphi)}{Q_\varphi}\le N.φ∈3SAT⇒Qφ​MaxCut(Gφ​)​≥Y,φ∈/3SAT⇒Qφ​MaxCut(Gφ​)​≤N.

Given such a reduction, an α\alphaα-approximation algorithm would decide 3SAT by comparing its output with NQφN Q_\varphiNQφ​.

Formalization targets

Goal: optimal Max-Cut hardness (Theorem 1.1, p. 1)

For every real α\alphaα with αGW<α≤1\alpha_{\mathrm{GW}}<\alpha\le1αGW​<α≤1, a gap reduction for α\alphaα from 3SAT to simple unweighted Max-Cut exists:

∀ α∈(αGW,1]:GapReduction(α)≠∅.\forall\,\alpha\in(\alpha_{\mathrm{GW}},1]:\quad \mathrm{GapReduction}(\alpha)\ne\varnothing.∀α∈(αGW​,1]:GapReduction(α)=∅.

In Lean this is OAI.OptimalMaxCut.main : OptimalMaxCut.MainStatement. The preprint proves it from a sharper weighted gap (Theorem 1.2, p. 2): for rational t∈(0,1)t\in(0,1)t∈(0,1), B(t)=2πarcsin⁡tB(t)=\tfrac{2}{\pi}\arcsin tB(t)=π2​arcsint and 0<ε<(t−B(t))/40<\varepsilon<(t-B(t))/40<ε<(t−B(t))/4, it is NP-hard to distinguish Val≥1+t2−ε\mathrm{Val}\ge\frac{1+t}{2}-\varepsilonVal≥21+t​−ε from Val≤1+B(t)2+ε\mathrm{Val}\le\frac{1+B(t)}{2}+\varepsilonVal≤21+B(t)​+ε on graphs with a rational edge distribution; Appendix B (p. 35) converts weighted graphs to simple unweighted ones.

Significance

The result itself. Theorem 1.1 shows that the Goemans–Williamson algorithm is optimal among polynomial-time algorithms unless P = NP, removing the Unique Games hypothesis from the result of Khot, Kindler, Mossel and O'Donnell. It is the most prominent instance of an SDP-tight threshold, and the gap form (Theorem 1.2) gives a whole curve of completeness/soundness pairs.

Formalizing it. No machine-checked proof of any optimal inapproximability result for Max-Cut exists. A full development requires the 2-to-1 Games Theorem in the affine form stated as Proposition 2.2 (p. 6) and derived in Appendix A, the Majority Is Stablest theorem (Lemma 2.1, p. 5), and the paper's new ingredients: tree-code Fourier analysis (Section 5), a vector-valued affine hashing lemma (Lemma 6.1, p. 14), dimension-independent extraction (Proposition 7.1, p. 18) and decoding (Proposition 8.4, p. 24). Several of these — Majority Is Stablest, Boolean Fourier analysis, polynomial-time gap reductions on Turing machines — are reusable libraries in their own right.

Difficulty

The classical long-code test for Max-Cut, analysed via Majority Is Stablest, identifies an influential coordinate of an arbitrary cut. Under the Unique Games Conjecture that coordinate is directly a label of the source game. Starting instead from 2-to-1 games, the influential coordinate is indexed by a gate input of the test, not by a source-game label, and the two endpoints of a source edge cannot in general reconstruct a common context without knowing their projection. The decoding loss must also be independent of the source alphabet, because the alphabet dimension is chosen after the target soundness; a dimension-dependent loss would make the parameter choice circular (Introduction, pp. 3–4).

Formalization scope

  • Graphs are OptimalMaxCut.Graph: a vertex count with a symmetric loopless Bool adjacency function; maxCut maximizes cutSize over all Fin n → Bool, counting each unordered crossing edge once.
  • The output is a ScaledGraph with an explicit positive integer scale; the YES/NO bounds compare maxCut / scale in ℝ, and 0 < scale rules out division by zero.
  • alphaGW is defined as the sInf of 2arccos⁡ρ/(π(1−ρ))2\arccos\rho/(\pi(1-\rho))2arccosρ/(π(1−ρ)) over ρ∈[−1,1)\rho\in[-1,1)ρ∈[−1,1); this set is nonempty and bounded below, so the infimum is the true constant.
  • The reduction is a Turing.TM2ComputableInPolyTime machine with finite tape alphabets, from raw input bits (identity encoding) to ScaledGraph.bits (delimited vertex count and scale, then the full adjacency table); the rational bounds and the machine are fixed before the input.
  • The hypothesis αGW<α≤1\alpha_{\mathrm{GW}}<\alpha\le1αGW​<α≤1 is satisfiable, and the conclusion requires an actual strict gap noBound < α * yesBound with 0 < yesBound, so there is no trivial reading. No assumption P ≠ NP is built in.

Welcome contributions: Boolean and Gaussian Fourier analysis (noise stability, influences, Borell's theorem), Majority Is Stablest, the 2-to-1 Games Theorem, and a reusable library for polynomial-time gap reductions on TM2 machines.

Selected references

  • OpenAI, A Direct Proof of Optimal Max-Cut Hardness, OpenAI Math Release preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/A-Direct-Proof-of-Optimal-Max-Cut-Hardness-September-23-2026/paper.pdf
  • M. X. Goemans, D. P. Williamson, Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming, J. ACM 42 (1995). https://doi.org/10.1145/227683.227684
  • J. Håstad, Some optimal inapproximability results, J. ACM 48 (2001). https://doi.org/10.1145/502090.502098
  • L. Trevisan, G. Sorkin, M. Sudan, D. Williamson, Gadgets, approximation, and linear programming, SIAM J. Comput. 29 (2000). https://doi.org/10.1137/S0097539797328847
  • S. Khot, G. Kindler, E. Mossel, R. O'Donnell, Optimal inapproximability results for MAX-CUT and other 2-variable CSPs?, SIAM J. Comput. 37 (2007). https://doi.org/10.1137/S0097539705447372
  • E. Mossel, R. O'Donnell, K. Oleszkiewicz, Noise stability of functions with low influences: invariance and optimality, Ann. of Math. 171 (2010). https://doi.org/10.4007/annals.2010.171.295
  • U. Feige, G. Schechtman, On the optimality of the random hyperplane rounding technique for MAX CUT, Random Structures Algorithms 20 (2002). https://doi.org/10.1002/rsa.10036
  • I. Dinur, S. Khot, G. Kindler, D. Minzer, M. Safra, Towards a proof of the 2-to-1 games conjecture?, Theory of Computing 21 (2025). https://doi.org/10.4086/toc.2025.v021a011
  • S. Khot, D. Minzer, M. Safra, Pseudorandom sets in Grassmann graph have near-perfect expansion, Ann. of Math. 198 (2023). https://doi.org/10.4007/annals.2023.198.1.1
  • S. Khot, On the power of unique 2-prover 1-round games, STOC 2002. https://doi.org/10.1145/509907.510017
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The Unique Games TheoremResearch Paper

Motivation

A Unique Game is a system of constraints between pairs of variables, each constraint saying that the label of one endpoint is a fixed permutation of the label of the other. Deciding whether all constraints can be satisfied is easy (propagate labels along a spanning tree), so the interesting question is approximate: given an instance in which almost all constraints can be satisfied, can a polynomial-time algorithm find a labeling that satisfies even a small fraction?

Khot's Unique Games Conjecture (UGC, 2002) asserts that it cannot, unless P = NP. The conjecture matters because many optimal approximation thresholds have been proved conditionally on it: the Goemans–Williamson ratio for Max-Cut (KKMO 2007), the factor 222 for Vertex Cover (Khot–Regev 2008), the basic-SDP threshold for every constraint satisfaction problem (Raghavendra 2008), and hardness for ordering, multicut/sparsest-cut and correlation-clustering problems. An unconditional proof would turn all of these into NP-hardness results.

This mission asks for a machine-checked proof of the Unique Games Theorem as stated in an OpenAI preprint dated September 23, 2026 (source), which claims a positive resolution of the conjecture. The preprint has not been peer reviewed, and its claim has not been independently verified; on the platform the Lean goal below is open.

Timeline

  • 2001 — Håstad proves optimal inapproximability for linear equations mod 2 with near-perfect completeness (JACM 2001); this gap is the hardness input used by the preprint.
  • 2002 — Khot introduces Unique Games and formulates the conjecture (STOC 2002).
  • 2005–2008 — Conditional optimal hardness for Max-Cut (Khot, Kindler, Mossel, O'Donnell, SICOMP 2007), Vertex Cover (Khot–Regev, JCSS 2008) and every CSP (Raghavendra, STOC 2008). Khot–Vishnoi build SDP integrality gaps (JACM 2015, FOCS 2005).
  • 2005–2010 — Algorithms that delimit the conjecture's quantifiers: Trevisan (ToC 2008), Charikar–Makarychev–Makarychev with value 1−O(ϵlog⁡q)1-O(\sqrt{\epsilon\log q})1−O(ϵlogq​) (STOC 2006), and the subexponential algorithm of Arora–Barak–Steurer (FOCS 2010).
  • 2017–2018 — The 2-to-2 Games programme: Khot–Minzer–Safra (STOC 2017; ToC 2025), Dinur–Khot–Kindler–Minzer–Safra (STOC 2018; ToC 2025), Barak–Kothari–Steurer's shortcode formulation (ITCS 2019), and the Grassmann expansion theorem of Khot–Minzer–Safra (Annals 2023). Together these give NP-hardness of Unique Games with completeness near 1/21/21/2 and arbitrarily small soundness.
  • 2026 — The OpenAI preprint claims completeness arbitrarily close to 111, i.e. the full conjecture (Theorem 1.1, p. 2).

Setting

Fix a finite alphabet KKK. An instance GGG has a finite vertex set and a nonempty list of oriented constraints e=(ue,ve,πe)e=(u_e,v_e,\pi_e)e=(ue​,ve​,πe​) with πe\pi_eπe​ a permutation of KKK; repeated list entries count with multiplicity, and no weights are allowed. A labeling aaa assigns a label to every vertex and satisfies eee when a(ve)=πe(a(ue))a(v_e)=\pi_e(a(u_e))a(ve​)=πe​(a(ue​)). The value of aaa is the fraction of satisfied constraints and val(G)\mathrm{val}(G)val(G) is the maximum over labelings.

An instance is simple bipartite if its vertices split into two sides, every constraint goes from the first side to the second, and no two list entries join the same ordered pair. It is a translation instance over K=F2sK=\mathbb F_2^sK=F2s​ if every constraint has the form a(ve)=a(ue)+cea(v_e)=a(u_e)+c_ea(ve​)=a(ue​)+ce​.

3SAT inputs are bit strings decoding to a conjunction of clauses with exactly three literal slots (repeated variables and literals allowed); the input is a YES instance when it decodes to a satisfiable formula.

Formalization targets

Goal: the Unique Games Theorem (Theorem 1.1, p. 2)

For every ε,δ∈(0,1/2)\varepsilon,\delta\in(0,1/2)ε,δ∈(0,1/2) there exist s≥1s\ge1s≥1 and a deterministic polynomial-time map φ↦Gφ\varphi\mapsto G_\varphiφ↦Gφ​ to simple bipartite translation instances over F2s\mathbb F_2^sF2s​ such that

φ∈3SAT  ⟹  val(Gφ)≥1−ε,φ∉3SAT  ⟹  val(Gφ)≤δ.\varphi\in\mathrm{3SAT}\;\Longrightarrow\;\mathrm{val}(G_\varphi)\ge 1-\varepsilon,\qquad \varphi\notin\mathrm{3SAT}\;\Longrightarrow\;\mathrm{val}(G_\varphi)\le \delta .φ∈3SAT⟹val(Gφ​)≥1−ε,φ∈/3SAT⟹val(Gφ​)≤δ.

The alphabet, the program and the running-time polynomial depend only on (ε,δ)(\varepsilon,\delta)(ε,δ); the two errors are chosen independently, before the alphabet. In Lean this is OAI.UniqueGamesTheorem.theorem11, which asserts that the structure BinaryGapReduction ε δ is inhabited.

Significance

The result itself. The conjecture is the hypothesis behind a large body of tight approximation thresholds; the preprint's Section 8 (pp. 39–43) records how Theorem 1.1 feeds the earlier reductions for Max-Cut, Vertex Cover, Raghavendra's CSP theorem, ordering CSPs, multicut/sparsest cut and correlation clustering. Those reductions and thresholds are due to their original authors. The statement also fixes the conjecture's quantifier order (errors first, then alphabet), which is exactly what the known algorithms above do not contradict.

Formalizing it. No machine-checked proof of the UGC, or of the 2-to-2 theorem it builds on, is known to exist. A complete development would have to formalize Håstad's parity gap, classical parallel repetition for projection games, the Khot–Minzer–Safra Grassmann/shortcode inverse theorem (used as an external input via Theorem 5.1, p. 22 of the preprint), and the preprint's new latent-alphabet gadget and matrix test. Each of these is reusable well beyond this mission.

Difficulty

The central obstacle, identified in the preprint's introduction (pp. 2–4), is completeness: in a unique constraint every label permits exactly one reply, so the natural codeword tests lose a constant fraction of constraints even on honest proofs. For example, on the rank-one matrix shortcode the evaluation M↦MzM\mapsto MzM↦Mz is preserved by a random rank-one update only with probability about 1/21/21/2. The 2-to-2 theorem gives soundness but only near-1/21/21/2 completeness, and pushing completeness to 1−ε1-\varepsilon1−ε while keeping soundness δ\deltaδ independent of the alphabet is a separate problem that the earlier programme left open.

Formalization scope

  • Instances are Foundations.Target.Instance q over alphabet Fin q, with constraints given by explicit forward/inverse permutation tables and a nonemptiness proof; the value is countSatisfied / constraints.length in ℝ, so division by zero cannot occur.
  • The reduction is a Turing.TM2ComputableInPolyTime program from raw input bits (identity encoding, so runtime is measured in the raw input length including sparse variable names) to the bit encoding gameBits; its tape alphabets must be finite.
  • coordinates : Fin alphabet ≃ (Fin s → ZMod 2) fixes the F2s\mathbb F_2^sF2s​ structure, and translations requires every constraint to be a translation in these coordinates; simpleBipartite gives the orientation and the no-parallel-edge condition on list positions.
  • The 3SAT language is BinaryLanguage.language: inputs that decode (canonically) to a satisfiable three-slot formula. Malformed inputs are NO instances and must be mapped to low-value games.
  • The hypotheses 0<ε,δ<1/20<\varepsilon,\delta<1/20<ε,δ<1/2 are satisfiable and the conclusion is a data-carrying structure, so there is no vacuous reading; the completeness clause requires an actual labeling, and soundness quantifies over all labelings.

Welcome contributions: a reusable Lean library for PCP-style gap reductions on TM2 machines, Fourier analysis over F2n\mathbb F_2^nF2n​, parallel repetition for projection games, and the Grassmann expansion theorem.

Selected references

  • OpenAI, The Unique Games Theorem, OpenAI Math Release preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/The-Unique-Games-Theorem-September-23-2026/paper.pdf
  • S. Khot, On the power of unique 2-prover 1-round games, STOC 2002. https://doi.org/10.1145/509907.510017
  • S. Khot, On the Unique Games Conjecture, CCC 2010. https://doi.org/10.1109/CCC.2010.19
  • S. Khot, G. Kindler, E. Mossel, R. O'Donnell, Optimal inapproximability results for MAX-CUT and other 2-variable CSPs?, SIAM J. Comput. 37 (2007). https://doi.org/10.1137/S0097539705447372
  • S. Khot, O. Regev, Vertex cover might be hard to approximate to within 2−ε, J. Comput. Syst. Sci. 74 (2008). https://doi.org/10.1016/j.jcss.2007.06.019
  • P. Raghavendra, Optimal algorithms and inapproximability results for every CSP?, STOC 2008. https://doi.org/10.1145/1374376.1374414
  • J. Håstad, Some optimal inapproximability results, J. ACM 48 (2001). https://doi.org/10.1145/502090.502098
  • B. Barak, P. Kothari, D. Steurer, Small-set expansion in shortcode graph and the 2-to-2 conjecture, ITCS 2019. https://doi.org/10.4230/LIPIcs.ITCS.2019.9
  • S. Khot, D. Minzer, M. Safra, Pseudorandom sets in Grassmann graph have near-perfect expansion, Ann. of Math. 198 (2023). https://doi.org/10.4007/annals.2023.198.1.1
  • S. Arora, B. Barak, D. Steurer, Subexponential algorithms for Unique Games and related problems, FOCS 2010. https://doi.org/10.1109/FOCS.2010.59
2 thms1 active userReviewed
Discrete GeometryHarmonic Analysis·Captain: wurtle

A sharp Fourier certificate for planar circle packingResearch Paper

Motivation

How densely can equal disks be packed in the plane? The hexagonal arrangement, with centers on the triangular lattice, covers a fraction π/(23)≈0.9069\pi/(2\sqrt3)\approx0.9069π/(23​)≈0.9069 of the plane, and Thue's theorem says no packing does better. In 2003 Cohn and Elkies introduced a linear programming bound for sphere packing: a single auxiliary function whose values are nonpositive outside a ball and whose Fourier transform is nonnegative certifies an upper bound on packing density. Viazovska (dimension 8) and Cohn, Kumar, Miller, Radchenko and Viazovska (dimension 24) found auxiliary functions for which the bound is sharp, solving sphere packing in those dimensions. Cohn and Elkies conjectured that a sharp function exists also in dimension 2 (Conjecture 7.3 of their paper). The planar packing problem itself is classical; the question here is whether the Fourier method certifies it exactly.

Timeline

  • 1890s–1940s. Thue's theorem on optimality of the hexagonal circle packing; an elementary proof based on an idea of Rogers is presented by Hales (2000).
  • 2003. Cohn and Elkies introduce the linear programming bound and conjecture sharp auxiliary functions in dimensions 2, 8 and 24 (doi:10.4007/annals.2003.157.689).
  • 2017. Viazovska constructs the sharp function in dimension 8 (doi:10.4007/annals.2017.185.3.7); Cohn, Kumar, Miller, Radchenko and Viazovska do so in dimension 24 (doi:10.4007/annals.2017.185.3.8).
  • 2019. Radchenko and Viazovska prove Fourier interpolation on the real line (doi:10.1007/s10240-018-0101-z).
  • 2021. Sardari shows that values and first derivatives on triangular-lattice shells do not determine a planar radial Schwartz function (arXiv:2102.08753).
  • 2022. Cohn, Kumar, Miller, Radchenko and Viazovska prove universal optimality in dimensions 8 and 24 via interpolation formulas (doi:10.4007/annals.2022.196.3.3).

The source of this mission, an OpenAI preprint dated September 23, 2026, claims an explicit sharp planar certificate.

Setting

Scale disks to radius 1/21/21/2, so a packing is a set of centers C⊂R2\mathcal C\subset\mathbb R^2C⊂R2 with ∣x−y∣≥1|x-y|\ge1∣x−y∣≥1 for distinct centers. The Fourier transform is

f^(ξ)=∫R2f(x) e−2πi x⋅ξ dx.\widehat f(\xi)=\int_{\mathbb R^2}f(x)\,e^{-2\pi i\,x\cdot\xi}\,dx.f​(ξ)=∫R2​f(x)e−2πix⋅ξdx.

A Schwartz function is smooth with all derivatives decaying faster than any inverse power of ∣x∣|x|∣x∣. A function is radial if f(x)f(x)f(x) depends only on ∣x∣|x|∣x∣; for real radial integrable fff, f^\widehat ff​ is real and radial. The Cohn–Elkies theorem says that if fff is admissible, f(x)≤0f(x)\le0f(x)≤0 for ∣x∣≥1|x|\ge1∣x∣≥1 and f^≥0\widehat f\ge0f​≥0, then every packing of radius-1/21/21/2 disks has density at most vol⁡B(0,1/2)⋅f(0)/f^(0)\operatorname{vol}B(0,1/2)\cdot f(0)/\widehat f(0)volB(0,1/2)⋅f(0)/f​(0).

Formalization targets

Goal: Theorem 1.1 (sharp Fourier certificate)

There is a real radial Schwartz function f:R2→Rf:\mathbb R^2\to\mathbb Rf:R2→R with

f^(0)=1,f(0)=23,f^(ξ)≥0  (ξ∈R2),f(x)≤0  (∣x∣≥1).\widehat f(0)=1,\qquad f(0)=\frac{2}{\sqrt3},\qquad \widehat f(\xi)\ge0\ \ (\xi\in\mathbb R^2),\qquad f(x)\le0\ \ (|x|\ge1).f​(0)=1,f(0)=3​2​,f​(ξ)≥0  (ξ∈R2),f(x)≤0  (∣x∣≥1).

Lean: OAI.sharp_fourier_certificate, open on the platform.

Significance

With f(0)/f^(0)=2/3f(0)/\widehat f(0)=2/\sqrt3f(0)/f​(0)=2/3​ the Cohn–Elkies bound equals π4⋅23=π23\frac{\pi}{4}\cdot\frac{2}{\sqrt3}=\frac{\pi}{2\sqrt3}4π​⋅3​2​=23​π​, the hexagonal density, so the theorem answers the two-dimensional case of Cohn–Elkies Conjecture 7.3 affirmatively and gives a Fourier-analytic proof of Thue's theorem for arbitrary (not only periodic) packings. Its zero structure also recovers uniqueness of the triangular lattice among periodic optimal packings. The certificate does not satisfy the additional zero-set requirement of Cohn–Elkies Conjecture 8.1, which remains a separate question. The construction methods feed into the companion work on universal optimality of the triangular lattice in the same family. The result is claimed in an OpenAI preprint that has not been peer reviewed; no machine-checked proof exists. By contrast, Viazovska's dimension-8 theorem has been the subject of formalization efforts, so a planar certificate would be a natural low-dimensional companion.

Difficulty

In dimensions 8 and 24 the magic functions come from modular forms, whose symmetries control a function and its Fourier transform at once. No such modular construction is known to produce the planar certificate, and Sardari's nonuniqueness theorem shows that prescribing values and derivatives on the lattice shells does not pin a function down. Numerical Laguerre–Gaussian searches (Cohn–Elkies) approach the bound but do not attain it. The hard part is an exact construction together with rigorous sign control of both fff (for ∣x∣≥1|x|\ge1∣x∣≥1) and f^\widehat ff​ (everywhere), including between interpolation nodes and in the tails.

Formalization scope

  • The plane is EuclideanSpace ℝ (Fin 2); the function is a SchwartzMap Plane ℝ.
  • SharpPlanar.fourier f ξ is the Bochner integral of f(x)exp⁡(−2πi⟨x,ξ⟩)f(x)\exp(-2\pi i\langle x,\xi\rangle)f(x)exp(−2πi⟨x,ξ⟩); for a Schwartz function it is the usual transform.
  • SharpCertificate f: Radial f, fourier f 0 = 1, f 0 = 2 / √3, for every ξ the transform has zero imaginary part and nonnegative real part, and f x ≤ 0 when 1 ≤ ‖x‖.
  • The goal is pure existence; any certificate, explicit or not, proves it.

A complete development needs Fourier transforms of radial functions in the plane (Hankel transforms, Gaussian pairings) and rigorous interval or Bernstein-polynomial sign certification. Formalizing the Cohn–Elkies bound itself (certificate implies density bound) would be a valuable reusable companion. Contributions formalizing Proposition 2.2, Proposition 3.4 or Proposition 4.3 are welcome.

Selected references

  • OpenAI, A sharp Fourier certificate for planar circle packing, preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/A-sharp-Fourier-certificate-for-planar-circle-packing-September-23-2026/paper.pdf
  • H. Cohn, N. Elkies, New upper bounds on sphere packings I, Ann. of Math., 2003. https://doi.org/10.4007/annals.2003.157.689
  • T. C. Hales, Cannonballs and honeycombs, Notices Amer. Math. Soc., 2000.
  • M. S. Viazovska, The sphere packing problem in dimension 8, Ann. of Math., 2017. https://doi.org/10.4007/annals.2017.185.3.7
  • H. Cohn, A. Kumar, S. D. Miller, D. Radchenko, M. Viazovska, The sphere packing problem in dimension 24, Ann. of Math., 2017. https://doi.org/10.4007/annals.2017.185.3.8
  • D. Radchenko, M. Viazovska, Fourier interpolation on the real line, Publ. Math. IHÉS, 2019. https://doi.org/10.1007/s10240-018-0101-z
  • H. Cohn, A. Kumar, S. D. Miller, D. Radchenko, M. Viazovska, Universal optimality of the E8 and Leech lattices and interpolation formulas, Ann. of Math., 2022. https://doi.org/10.4007/annals.2022.196.3.3
  • N. Talebizadeh Sardari, Higher Fourier interpolation on the plane, preprint, 2021. https://arxiv.org/abs/2102.08753
2 thms1 active userReviewed
Discrete GeometryHarmonic AnalysisMathematical Physics·Captain: wurtle

An atomic certificate for triangular-lattice universal optimalityResearch Paper

Motivation

Which arrangement of points in the plane minimizes interaction energy? For many repulsive interactions the expected answer is the triangular (hexagonal) lattice. Universal optimality, formulated by Cohn and Kumar (2007, doi:10.1090/S0894-0347-06-00546-7), asks for a single configuration that minimizes energy simultaneously for every completely monotone potential of squared distance, which includes all Gaussians and all inverse powers. It was proved in dimensions 8 and 24 for E8E_8E8​ and the Leech lattice; in dimension two it was a conjecture. The question matters in mathematical physics (crystallization, vortex lattices, Coulomb and Riesz gases) and in the theory of Fourier interpolation.

Timeline

  • 1953–1964. Rankin, Cassels, Ennola and Diananda show that the triangular lattice minimizes the Epstein zeta function among planar lattices (doi:10.1017/S2040618500035668, doi:10.1017/S2040618500033906).
  • 1988. Montgomery proves the Gaussian (theta-function) minimum among planar lattices of fixed covolume for every parameter (doi:10.1017/S0017089500007047).
  • 2003. Cohn and Elkies introduce Fourier linear-programming bounds for sphere packing (doi:10.4007/annals.2003.157.689).
  • 2007. Cohn and Kumar formulate the Euclidean universal-optimality program (Conjecture 9.4).
  • 2018. Cohn and de Courcy-Ireland extend the Fourier comparison to density-based competitor classes (doi:10.1215/00127094-2018-0018).
  • 2019. Radchenko and Viazovska develop Fourier interpolation on the line (doi:10.1007/s10240-018-0101-z).
  • 2022. Cohn, Kumar, Miller, Radchenko and Viazovska prove universal optimality of E8E_8E8​ and the Leech lattice (doi:10.4007/annals.2022.196.3.3).
  • 2024–2025. Partial planar results: comparisons with periodic classes (Faulhuber–Shafkulovska–Zlotnikov, doi:10.1090/bproc/247), small periodic configurations (Hardin–Tenpas, doi:10.19086/da.144978), and local optimality under small displacements (Leblé, arXiv:2511.03353).

The source of this mission, an OpenAI preprint dated September 26, 2026, claims the full planar statement against all locally finite configurations of density one. A companion OpenAI preprint reaches the same energy conclusion by a different (modulo-12) interpolation construction.

Setting

Let b=3/2b=\sqrt3/2b=3​/2 and A=b−1/2{m(1,0)+n(1/2,b):m,n∈Z}A=b^{-1/2}\{m(1,0)+n(1/2,b):m,n\in\mathbb Z\}A=b−1/2{m(1,0)+n(1/2,b):m,n∈Z}, the triangular lattice of covolume one. Let BRB_RBR​ be the closed disk of radius RRR about the origin. A set C⊂R2\mathcal C\subset\mathbb R^2C⊂R2 is locally finite if each C∩BR\mathcal C\cap B_RC∩BR​ is finite, and has centered disk density one if NR/(πR2)→1N_R/(\pi R^2)\to1NR​/(πR2)→1, where NR=#(C∩BR)N_R=\#(\mathcal C\cap B_R)NR​=#(C∩BR​).

A smooth g:(0,∞)→[0,∞)g:(0,\infty)\to[0,\infty)g:(0,∞)→[0,∞) is completely monotone if (−1)jg(j)(t)≥0(-1)^jg^{(j)}(t)\ge0(−1)jg(j)(t)≥0 for all j≥0j\ge0j≥0, t>0t>0t>0. The lower energy per particle is

Eg(C)=lim inf⁡R→∞1NR∑x,y∈C∩BRx≠yg(∣x−y∣2)∈[0,∞],E_g(\mathcal C)=\liminf_{R\to\infty}\frac1{N_R}\sum_{\substack{x,y\in\mathcal C\cap B_R\\ x\ne y}}g(|x-y|^2)\in[0,\infty],Eg​(C)=R→∞liminf​NR​1​x,y∈C∩BR​x=y​∑​g(∣x−y∣2)∈[0,∞],

summing over ordered pairs. The Fourier transform is f^(ξ)=∫f(x)e−2πix⋅ξ dx\widehat f(\xi)=\int f(x)e^{-2\pi i x\cdot\xi}\,dxf​(ξ)=∫f(x)e−2πix⋅ξdx and A∗={ξ:ξ⋅a∈Z ∀a∈A}A^*=\{\xi:\xi\cdot a\in\mathbb Z\ \forall a\in A\}A∗={ξ:ξ⋅a∈Z ∀a∈A} is the dual lattice.

Formalization targets

Milestone: Theorem 1.2 (sharp Gaussian minorants)

For every α>0\alpha>0α>0 there is a real radial Schwartz fαf_\alphafα​ with

fα≤e−πα∣x∣2,f^α≥0,fα(a)=e−πα∣a∣2 (a∈A∖{0}),f^α(w)=0 (w∈A∗∖{0}),f_\alpha\le e^{-\pi\alpha|x|^2},\quad \widehat f_\alpha\ge0,\quad f_\alpha(a)=e^{-\pi\alpha|a|^2}\ (a\in A\setminus\{0\}),\quad \widehat f_\alpha(w)=0\ (w\in A^*\setminus\{0\}),fα​≤e−πα∣x∣2,f​α​≥0,fα​(a)=e−πα∣a∣2 (a∈A∖{0}),f​α​(w)=0 (w∈A∗∖{0}),

given for α≥1\alpha\ge1α≥1 by the paper's explicit atomic interpolation construction.

Goal: Theorem 1.1 (universal energy minimum)

For every smooth nonnegative completely monotone ggg and every locally finite C\mathcal CC of centered disk density one,

Eg(C) ≥ ∑a∈A∖{0}g(∣a∣2) = Eg(A)in [0,∞].E_g(\mathcal C)\ \ge\ \sum_{a\in A\setminus\{0\}}g(|a|^2)\ =\ E_g(A)\qquad\text{in }[0,\infty].Eg​(C) ≥ a∈A∖{0}∑​g(∣a∣2) = Eg​(A)in [0,∞].

Lean: OAI.AtomicTriangular.universal_energy_minimum, open on the platform.

Significance

Theorem 1.1 would settle the planar case of the Cohn–Kumar universal-optimality conjecture in a density-based competitor class with no periodicity or perturbation assumption, for every completely monotone potential including those (such as t−pt^{-p}t−p with p≤1p\le1p≤1) whose lattice sums diverge. By Bernstein's theorem such potentials are mixtures of Gaussians, so the sharp Gaussian certificates of Theorem 1.2 are the analytic core. The theorem identifies the minimum value; it does not classify minimizers. The result is claimed in an OpenAI preprint that has not been peer reviewed; no machine-checked proof exists. A formalization would also produce reusable Fourier-analytic tools in Lean (Poisson summation for lattices, linear-programming energy bounds).

Difficulty

Lattice-restricted results (Rankin, Montgomery) compare only lattices; a competitor here may be nonperiodic, have arbitrarily close pairs, and have large local clustering. The Fourier linear-programming method handles general competitors but needs, for each Gaussian, an auxiliary function that is simultaneously a pointwise minorant, has nonnegative Fourier transform, and is sharp: it must touch the Gaussian on every lattice shell and its transform must vanish on every dual shell. Constructing such functions for all α>0\alpha>0α>0 at once, with rigorous sign control between the interpolation nodes, is the central obstacle. A second issue is passing from the density hypothesis alone (no averaging over translates) to the energy inequality.

Formalization scope

  • Points live in EuclideanSpace ℝ (Fin 2); A is Set.range triangularPoint.
  • AdmissiblePotential g: ContDiffOn ℝ ⊤ g (Ioi 0), g≥0g\ge0g≥0 and (−1)r(-1)^r(−1)r iteratedDeriv r g t ≥0\ge0≥0 for t>0t>0t>0.
  • energy g C is the liminf over R→∞R\to\inftyR→∞ of diskEnergy, valued in ℝ≥0∞; latticeEnergy g is a tsum in ℝ≥0∞, so divergent sums are allowed and no summability hypothesis is needed.
  • The goal returns both the inequality for every admissible C and the equality latticeEnergy g = energy g A.
  • For Theorem 1.2, realFourier is Mathlib's 𝓕 (kernel e−2πi⟨x,ξ⟩e^{-2\pi i\langle x,\xi\rangle}e−2πi⟨x,ξ⟩) applied to the complexified function; the α≥1\alpha\ge1α≥1 clause names the paper's explicit coefficients (R, R0, Yf, Uplus, Uminus) and error bounds.

Contributions formalizing Lemma 2.1 (reciprocal Gaussian parameters), Proposition 2.3 (density-only linear programming), Lemma 4.1 (finite certificate) and Proposition 5.1 (exact interpolation and approximation) are welcome.

Selected references

  • OpenAI, An atomic certificate for triangular-lattice universal optimality, preprint, September 26, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/An-atomic-certificate-for-triangular-lattice-universal-optimality-September-26-2026/paper.pdf
  • H. Cohn, A. Kumar, Universally optimal distribution of points on spheres, J. Amer. Math. Soc., 2007. https://doi.org/10.1090/S0894-0347-06-00546-7
  • H. Cohn, A. Kumar, S. D. Miller, D. Radchenko, M. Viazovska, Universal optimality of the E8 and Leech lattices and interpolation formulas, Ann. of Math., 2022. https://doi.org/10.4007/annals.2022.196.3.3
  • H. Cohn, N. Elkies, New upper bounds on sphere packings I, Ann. of Math., 2003. https://doi.org/10.4007/annals.2003.157.689
  • H. Cohn, M. de Courcy-Ireland, The Gaussian core model in high dimensions, Duke Math. J., 2018. https://doi.org/10.1215/00127094-2018-0018
  • H. L. Montgomery, Minimal theta functions, Glasgow Math. J., 1988. https://doi.org/10.1017/S0017089500007047
  • R. A. Rankin, A minimum problem for the Epstein zeta-function, 1953. https://doi.org/10.1017/S2040618500035668
  • D. Radchenko, M. Viazovska, Fourier interpolation on the real line, Publ. Math. IHÉS, 2019. https://doi.org/10.1007/s10240-018-0101-z
  • T. Leblé, The hexagonal lattice is universally locally optimal, preprint, 2025. https://arxiv.org/abs/2511.03353
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