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🏆Completed
Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin: a received HTLC cannot be timed out before its expiry, at the block levelTextbook

The claim

The HTLC mission proved which witnesses spend a received HTLC output under the script interpreter, and that its timeout path runs through OP_CHECKLOCKTIMEVERIFY <cltv_expiry>. That is a statement about one script evaluation. The claim Lightning actually needs is one block higher: no block can contain a non-revocation spend of a received HTLC by the timeout path before the expiry height. This mission composes the script result with the block mission's finality rule (IsFinalTx, required by ContextualCheckBlock for every transaction in a block) to state exactly that, and the same bound for a pre-signed HTLC-timeout transaction whose lock time is the expiry.

What is proved

  • Finality of height-locked transactions (isFinalTx_height): with a nonzero lock time below the threshold and an input that is not SEQUENCE_FINAL, a transaction is final at height hhh iff its lock time is below hhh.
  • Pre-signed HTLC-timeout transactions (presigned_timeout_height): lock time cltv_expiry means acceptance only in a block above the expiry.
  • Received HTLC, timeout path (received_spend_height, the goal): a two-element witness accepted by VerifyScript for a received HTLC output, in a transaction final at height hhh, is a revocation spend or has cltv_expiry < h.

What this rests on and what it does not say

The composition uses only published theorems: received_p2wsh_spend, cltv_concrete, checkLockTime_iff (HTLC mission) and the block mission's isFinalTx. It does not say that a transaction final at a height is in a block at that height, which is the block mission's ContextualCheckBlock characterization, nor anything about the offered HTLC's timeout path beyond the pre-signed transaction's own lock time (there the bound rests on the remote node signing only a transaction with that lock time, a key-level fact outside the script). Time-based expiries (lock times at or above the threshold) are excluded by hypothesis; BOLT #3 uses heights. (BOLT #3, tx_verify.cpp)

4 thms1 active userReviewed
🏆Completed
Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin: the Lightning funding output under VerifyScriptTextbook

The claim

A Lightning channel's funds sit in the funding transaction's output, a pay-to-witness-script-hash of the 2-of-2 script 2 <pubkey1> <pubkey2> 2 OP_CHECKMULTISIG. Everything else in the protocol (commitment transactions, penalties, HTLCs) rests on one fact about this output: it moves only with both parties' signatures. This mission proves that fact against the script interpreter as transcribed from Bitcoin Core, and completes the on-chain script set of a channel begun by the to_local, HTLC and anchor missions.

What is proved

  • The script on its witness (funding_witness): the 2-of-2 loop's verdict, with the NULLDUMMY condition on the dummy element.
  • The spending condition, for any witness (funding_spends_iff): the witness-v0 success condition holds iff the stack is exactly [sig2, sig1, dummy] with each signature valid for its key in order; no other shape succeeds.
  • The output under VerifyScript (parseScript_funding, funding_p2wsh_spend, the goal): the byte form parses as intended, and VerifyScript accepts a witness for the P2WSH output exactly when it is dummy sig1 sig2 with both signatures valid for the spending transaction's own checker.

What this rests on and what it does not say

The signature checks are the signature mission's checker with the witness-v0 sighash; which transaction the signatures commit to (a commitment transaction, a mutual close) is a matter of what was signed, outside the script. BOLT #3's lexicographic ordering of the two keys is not a script-level condition and is not stated. (BOLT #3, interpreter.cpp)

5 thms1 active userReviewed
🏆Completed
Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin: the Lightning anchor-output scripts under VerifyScriptTextbook

The claim

Modern Lightning channels use option_anchors: the remote balance and both HTLC kinds carry a one-block OP_CHECKSEQUENCEVERIFY so that a commitment transaction's outputs cannot be spent in the same block it confirms in, and each side gets an anchor output that it can spend at any time to bump the fee, and that anyone can sweep after 16 blocks so that anchors do not litter the UTXO set. This mission proves what those scripts enforce, against the script interpreter as transcribed from Bitcoin Core, completing the BOLT #3 commitment-transaction script set begun by the to_local and HTLC missions.

What is proved

  • to_remote (to_remote_eval, to_remote_spends_iff, parseScript_toRemote, to_remote_p2wsh_spend): spendable by the remote key, only after the one-block delay, and by nothing else.
  • Anchor (anchor_eval, anchor_holder, anchor_anyone, anchor_spends_iff, parseScript_anchor, anchor_p2wsh_spend, anchor_anyone_concrete, anchor_anyone_delay): the funding-key holder spends at any time; a clean-false signature (the empty signature) spends exactly after the 16-block delay; under the concrete checker, the empty-signature witness verifies iff the spending transaction is version 2 or later with nSequence ≥ 16. A signature the checker rejects outright fails the script.
  • HTLCs with anchors (the six path theorems, the two spends_iff, the two parse theorems, the two p2wsh_spend): the HTLC mission's three paths each, with the one-block delay added to every non-revocation path and the revocation path unchanged.

What this rests on and what it does not say

The signature checks stay at the level of the signature mission's checker; the 20-byte hashes are parameters. anchor_anyone_concrete holds under consensus flags: under the policy flag WITNESS_PUBKEYTYPE an uncompressed funding key would make the empty-signature path fail, which is not modelled. With the CHECKSEQUENCEVERIFY flag off (pre-BIP112 blocks) the anchor is spendable by anyone at once; the activation mission fixes the flag on for every current height. The HTLC-success and HTLC-timeout second-stage transactions pay to the to_local script, already covered. (BOLT #3, interpreter.cpp)

25 thms1 active userReviewed
🏆Completed
Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin: the Lightning HTLC scripts under VerifyScriptTextbook

The claim

A Lightning commitment transaction carries one output per in-flight payment: an offered HTLC (the local node pays if the remote reveals the preimage, or recovers the funds after the timeout) or a received HTLC (the local node claims with the preimage before expiry, else the remote takes the funds back). BOLT #3 gives each a witness script with three branches (revocation, preimage, timeout), and the channel's security rests on those three being the only ways to spend. The to_local mission proved this for the balance output; this mission proves it for both HTLC scripts, against the script interpreter as transcribed from Bitcoin Core.

What is proved

  • The opcodes (cltv_step, multisig_two): OP_CHECKLOCKTIMEVERIFY passes exactly under CltvPasses; a 2-of-2 OP_CHECKMULTISIG passes iff each signature verifies for its key in order.
  • Each path in closed form (offered_revocation, offered_preimage, offered_timeout, received_revocation, received_success, received_timeout): on the BOLT #3 witness shapes the interpreter leaves exactly the expected check, including the NULLDUMMY condition on the multisig dummy.
  • The spending conditions, for any witness (offered_spends_iff, received_spends_iff): over an arbitrary stack, the witness-v0 success condition holds iff one of the three paths' conditions holds. Two facts the interpreter forces that the BOLT text leaves implicit: the inner selector is "is the top element 32 bytes long", not "is it empty"; and every non-revocation path requires the top element not to hash to the revocation hash, because OP_DUP OP_HASH160 … OP_EQUAL hashes whatever is on top.
  • The outputs under VerifyScript (parseScript_offered, parseScript_received, offered_p2wsh_spend, received_p2wsh_spend, the goal): the byte forms parse as intended, and the pay-to-witness-script-hash outputs are spent exactly by the three paths, with the spending transaction's own signature checker and lock-time check.
  • The lock time (cltv_concrete, checkLockTime_iff): with the CLTV flag active the check is Core's CheckLockTime, which for a block-height expiry is "nLockTime is a height at least the expiry and the input is not final".

What this rests on and what it does not say

The two 20-byte hashes are parameters, not tied to any key or preimage; signature checks stay at the level of the signature mission's checker. The HTLC-success and HTLC-timeout transactions themselves (second-stage, with their own to_local-style scripts) and the anchor-output variants of the scripts are not modelled, nor is the protocol-level argument about who holds which key when. (BOLT #3, interpreter.cpp)

17 thms1 active userReviewed
🏆Completed
Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin: the Lightning to_local script under VerifyScriptTextbook

The claim

A Lightning commitment transaction pays the local node's balance to a to_local output whose witness script (BOLT #3) reads

OP_IF <revocationpubkey> OP_ELSE <to_self_delay> OP_CHECKSEQUENCEVERIFY OP_DROP <local_delayedpubkey> OP_ENDIF OP_CHECKSIG

The channel's security rests on two claims about this script: the counterparty can spend it at any time with the revocation key, and the local node can spend it with its delayed key only after to_self_delay blocks, and nobody can spend it any other way. This mission proves both, and the "no other way", against the script interpreter as the script, signature and verification missions transcribed it from Bitcoin Core.

What is proved

  • The interpreter run (eval_cons, csv_step, serialize_length): the script on any stack b :: tl of admissible size fails unless tl is a single element, and otherwise leaves the result of the selected signature check; the OP_CHECKSEQUENCEVERIFY step passes exactly under CsvPasses.
  • The spending conditions (spends_iff): for any stack whatsoever, the witness-v0 success condition (one element left, and true) holds iff the stack is [b, sig] and either b is true and sig verifies for the revocation key, or b is false, the sequence check passes, and sig verifies for the delayed key.
  • The output (parseScript_toLocal, p2wsh_spend, the goal): the byte form of the script parses as intended, and VerifyScript accepts a witness for the pay-to-witness-script-hash output exactly when the witness is [sig, b, script] and the selected branch's conditions hold for the spending transaction's own signature checker and sequence check.
  • The delay (csv_concrete, checkSequence_iff, lightning_delay): with the CHECKSEQUENCEVERIFY flag active, the sequence check is Core's CheckSequence, which for a 16-bit delay and a flag-free nSequence (as Lightning sets it) is exactly "version ≥ 2 and nSequence ≥ to_self_delay".

What this rests on and what it does not say

The signature checks are the signature mission's checkSigPreTapscript with the witness-v0 sighash; nothing here says who holds the keys or how they are derived (BOLT #3's key derivation and the revocation secret are outside the script). The P2WSH program must be a true value, which VerifyScript requires of every witness program and which is a fact about one SHA-256 output the model cannot evaluate symbolically. MINIMALIF is policy, so under consensus any true selector opens the revocation branch; the relative lock time itself (whether the spending transaction may enter a block) is the block mission's BIP68 check, not the script's. (BOLT #3, interpreter.cpp)

10 thms1 active userReviewed
🏆Completed
Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin: chain growth, common prefix and chain quality in the synchronous backbone modelTextbook

The claim

"Bitcoin is secure as long as honest miners hold the majority of the hashrate" is the informal reading of Garay, Kiayias and Leonardos's backbone analysis: in a synchronous network, if the adversary finds fewer blocks than there are uniquely successful honest rounds over every long enough window, then honest chains grow (chain growth), agree on all but their last blocks (common prefix), and contain honest blocks regularly (chain quality). This mission states the synchronous model and proves these three properties from that combinatorial hypothesis.

What is proved

  • The model: executions with honest parties, a rushing adversary, maxvalid adoption, and valid chains in which every block sits on exactly the chain its creator extended (the hash-chain abstraction).
  • Chain growth (chain_growth, chain_growth_self): between rounds rrr and sss every honest chain grows by at least the number of successful rounds in between.
  • Common prefix (uniquely_successful_height, common_prefix_private, common_prefix_rounds, common_prefix, the goal): a uniquely successful round pins the honest block at its height; a private fork that stays adversarial for a window loses; hence the chains of any two honest parties agree once the last 2λ2λ2λ blocks are dropped.
  • Chain quality (chain_quality): any λλλ consecutive blocks of an honest chain contain an honest block.
  • The bridge to Core (chainWork_const_bits): at constant difficulty the block mission's chainWork is the block count times one block's proof, so "longest" and "most work" coincide in this regime.

What this rests on and what it does not say

Nothing probabilistic is proved: Typical (adversarial blocks in every window of length ≥λ≥ λ≥λ strictly fewer than the uniquely successful rounds strictly inside it) is assumed, where the paper derives it from Chernoff bounds and honest majority. The constants are k=2λk = 2λk=2λ and ℓ=λℓ = λℓ=λ rather than the paper's 2λf2λf2λf; only the weak form of chain quality is proved. The model is synchronous with one honest block per party per round, no explicit message layer, and static difficulty. (ePrint 2014/765)

9 thms1 active userReviewed
Quantum Information·Captain: Gohan

Four Equivalent Versions of Non-Abelian GerbesResearch Paper

Motivation

We recall and partially improve four versions of smooth, non-abelian gerbes: Cech cocycles, classifying maps, bundle gerbes, and principal 2-bundles. We prove that all these four versions are equivalent, and so establish new relations between interesting recent developments. Prominent partial results we prove are a bijection between continuous and smooth non-abelian cohomology, and an explicit equivalence between bundle gerbes and principal 2-bundles as 2-stacks.

This mission is the statement-level formalization of Four Equivalent Versions of Non-Abelian Gerbes (arXiv:1103.4815v3). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

1 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Chosen lifts reconstruct an inverse pseudofunctor (Lemma B.1, PDF p. 60). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

39 results of the paper are not stated yet, because the library lacks an interface they need:

  • Abstract and §1: four equivalent versions of smooth non-abelian gerbes: Canonical four-way comparison of smooth cocycle classes, classifying-map homotopy classes, gerbe classes, and the gerbe/2-bundle pre-2-stack equivalence (hard)
  • Proposition 4.1: Smooth/continuous nonabelian Cech H1 quotients and their actual forget-smoothness map (hard)
  • Lemma 4.3: Breen connecting maps f and f-prime, constructed from the identity connecting class xi_Gamma (hard)
  • Theorem 4.6: Geometric realization of the topological nerve and its classifying space, with the canonical comparison from smooth H1 (hard)
  • Proposition 5.2.1: Extension of the full gerbe bicategory along a monoidal anafunctor, including pullback comparisons (hard)
  • Theorem 5.2.2: Equivalence of the particular extension morphism of gerbe 2-stacks (hard)
  • Lemma 5.2.3: General-refinement gerbe 1-isomorphism induced by a product-compatible pulled-back bundle isomorphism (hard)
  • Proposition 5.2.4: Computed refinement pullback of a gerbe and its gerbe 1-isomorphism (hard)
  • Lemma 5.2.5: Smooth unit and inverse maps for the actual gerbe tensor product, with anchor equations and simultaneous uniqueness (hard)
  • Corollary 5.2.6: Canonical diagonal trivialization, dual/flip bundle isomorphism, and the induced smooth groupoid (hard)
  • Lemma 5.2.7: The gerbe Hom categories with general refinements and equivalence of their 1-morphisms (hard)
  • Lemma 5.2.8: The fibre-product subcategory and its actual inclusion into the unrestricted gerbe Hom category (hard)
  • Lemma 5.3.1: Smooth local sections of the gerbe cover and simultaneous trivializations of all pulled-back pair-overlap bundles (hard)
  • Theorem 5.3.2: Choice-independent cocycle extraction on gerbe isomorphism classes, using the well-typed cocycle law (3.1) (hard)
  • Theorem 6.2.1: The principal-2-bundle presheaf in bicategories and bicategorical effective descent for surjective submersions (hard)
  • Theorem 6.2.3: Extension of principal 2-bundles transported specifically from gerbe extension through E/R (hard)
  • Corollary 6.2.4: Invertibility of the actual equivariant anafunctors over M between principal 2-bundles (hard)
  • Lemma 6.2.5: Anafunctor sections and trivializations of principal 2-bundles (hard)
  • Corollary 6.2.6: Open restrictions and local equivariant anafunctor trivializations of principal 2-bundles (hard)
  • Proposition 6.2.8: A missing local-equivalence hypothesis in the printed local-triviality criterion (source-ambiguity)
  • Theorem 7.1: Explicit E/R equivalence of gerbes and principal 2-bundles, with coherent naturality in M (hard)
  • Corollary 7.2: Evaluation of a 2-stack on the complete simplicial nerve of a Lie groupoid (hard)
  • Lemma 7.1.1: The principal Gamma-bundle on P1×Gamma0 with the exact maps (7.1.1) (hard)
  • Lemma 7.1.3: Tensor-quotient descent of the product (7.1.3)/(7.1.5), with smoothness and associativity (hard)
  • Lemma 7.1.4: Bundle Q=F×Gamma0 constructed from the Appendix-A action on an equivariant anafunctor (hard)
  • Lemma 7.1.5: Choice-independent quotient bundle morphism beta from formula (7.1.7) (hard)
  • Proposition 7.1.6: The full specified E_M assignment including its quotient 2-morphisms, compositors and unitors (hard)
  • Lemma 7.1.7: Actual 2-morphism map of E_M on every gerbe Hom category (hard)
  • Proposition 7.1.8: Coherent smooth-pullback transformations and modifications for the specific family E_M (hard)
  • Lemma 7.2.1: The opposite-oriented reconstructed Lie groupoid and its exact strict action (7.2.2) (hard)
  • Lemma 7.2.2: Smooth essential surjectivity of the constructed action functor tau (hard)
  • Lemma 7.2.3: Smooth full-faithfulness pullback square for the constructed tau (hard)
  • Lemma 7.2.5: The computed sigma-tilde bundle isomorphism and its compatibility with both gerbe products (hard)
  • Lemma 7.2.6: The beta_l/beta_r restrictions of the actual gerbe morphism and their two triangles plus product identities (hard)
  • Lemma 7.2.7: Smooth orbit quotient Q/Gamma0 with descended anchors and commuting groupoid actions (hard)
  • Lemma 7.2.8: Descended smooth Gamma1 action (7.2.3) with both Appendix-A compatibility conditions (hard)
  • Lemma A.2: The concrete rho-to-lambda and lambda-to-rho functors between equivariant anafunctor categories (hard)
  • Lemma A.3: Compatibility of the A.2 isomorphism with balanced-quotient composition and identity anafunctors (hard)
  • Lemma B.3: Presheaves in bicategories on smooth manifolds, morphisms with pullback modifications, and the coherent lift reconstruction (hard)

Selected references

  • Four Equivalent Versions of Non-Abelian Gerbes, arXiv:1103.4815v3, https://arxiv.org/abs/1103.4815v3
2 thms1 active userReviewed
🏆Completed
Combinatorics·Captain: FormallyCorrect

Analytic CombinatoricsTextbook

Motivation

Philippe Flajolet and Robert Sedgewick's Analytic Combinatorics is the standard reference for counting discrete structures exactly and asymptotically. Its methods are used throughout the analysis of algorithms, random structures and enumerative combinatorics. This mission collects formally verified versions of the book's chapter-level results in one connected development.

Setting

A combinatorial class is a set of objects with a size such that each size has finitely many objects. Its generating function records how many objects have each size: the ordinary generating function C(z)=∑ncnznC(z)=\sum_n c_n z^nC(z)=∑n​cn​zn for unlabelled classes and the exponential generating function ∑ncnzn/n!\sum_n c_n z^n/n!∑n​cn​zn/n! for labelled ones. The symbolic method translates constructions on classes (disjoint union, product, sequence, set, cycle, pointing) into equations between generating functions. For example, if a class has counting series C(z)C(z)C(z) and no size-zero objects, the series S(z)S(z)S(z) for ordered sequences of its objects satisfies

S(z) (1−C(z))=1.S(z)\,(1-C(z))=1.S(z)(1−C(z))=1.

Lagrange inversion recovers the coefficients of implicitly defined series. Singularity analysis and the saddle-point method turn the analytic behaviour of a generating function into asymptotic estimates for its coefficients, and the same machinery gives limit laws for parameters of random structures.

Formalization targets

Goal

The goal is the ordered-sequence identity above for a class with finitely many objects of each size and none of size zero, stated for formal power series over Q\mathbb QQ. The sequence class includes the empty sequence of size zero.

Milestones

The 132 milestones are the remaining results, listed in the order of the book's chapters. Counts per chapter, goal included:

Book chapterTopicResults
IOrdinary generating functions, the symbolic method, Pólya enumeration, Lagrange inversion21
IIExponential generating functions, labelled structures, random mappings15
IIIBivariate generating functions, moments and variance of parameters7
IV and VIComplex analysis, coefficient transfer and singularity analysis10
VRational and meromorphic functions, continued fractions8
VIIApplications of singularity analysis to trees and lattice-path classes16
VIIISaddle-point estimates and integer-partition asymptotics50
IXLimit laws for parameters of random structures6

The total is 133 results.

Significance

The results are classical; the book proves them. This mission adds machine-checked proofs: every listed result has a Lean 4 proof accepted on the platform against a fixed Mathlib revision. Formalizing them pins down hypotheses that informal statements leave implicit: which objects a series counts, which convergence and nonvanishing conditions an estimate needs, and which limits are taken.

Difficulty

Informal treatments identify a generating function with "the" counting sequence of a construction; a formal statement must fix the finite type of objects being counted and the exact construction, so the symbolic-method results here are stated against explicit finite object types. Analytic estimates cannot be read off formal series: they need the analyticity domain, the behaviour near the dominant singularity and explicit error terms. Several saddle-point estimates require uniform local and tail bounds that informal arguments state only loosely; where the source assumes such bounds, the statements here keep them as hypotheses.

Formalization scope

Formal power-series identities are stated coefficientwise and do not require analytic convergence. Analytic estimates and probability limits keep their convergence, nonvanishing, parameter and measurability conditions. Results that the source proves under explicit assumptions remain conditional on those assumptions. Some results import supporting lemmas that are proved as separate theorems of the mission.

The mission covers 133 chapter-level results of the source development. It does not claim that every theorem of the book is formalized; coverage is exactly the verified declarations in the mission. Further results, sharper variants and alternative proofs are welcome as additional theorems.

Selected references

  • Philippe Flajolet and Robert Sedgewick, Analytic Combinatorics, Cambridge University Press, 2009. Author-hosted book.
  • AnalyticCombinatorics source repository, the immutable source revision formalized here.
375 thms1 active userReviewed
🏆Completed
Differential GeometryGeneral RelativityMathematical Physics·Captain: He Wang

Exact Spacetime Atlas VI: Five-Dimensional Einstein–Maxwell Certificates and Equal-Spin Radial ReductionResearch Paper

Motivation

Atlases I–IV certified four-dimensional vacuum and Einstein–Maxwell metrics on a registered coordinate definition layer. Atlas VI extends the certificate notion to total dimension 5=4+15=4+15=4+1 and asks two concrete questions: inside one explicit static ansatz, exactly when is the five-dimensional Einstein–Maxwell certificate satisfied; and for the cohomogeneity-one equal-spin ansatz, is the full system of field equations equivalent to a finite set of radial equations.

Setting

Total dimension 555, signature (−,+,+,+,+)(-,+,+,+,+)(−,+,+,+,+), Λ=0\Lambda=0Λ=0, coupling Gab=2SabG_{ab}=2S_{ab}Gab​=2Sab​ with Eab=Rab−12gabR−2SabE_{ab}=R_{ab}-\tfrac12 g_{ab}R-2S_{ab}Eab​=Rab​−21​gab​R−2Sab​. VI-01: g=−f dv2+2 dv dr+r2[du2/(4u(1−u))+u dφ2+(1−u) dψ2]g=-f\,dv^2+2\,dv\,dr+r^2[du^2/(4u(1-u))+u\,d\varphi^2+(1-u)\,d\psi^2]g=−fdv2+2dvdr+r2[du2/(4u(1−u))+udφ2+(1−u)dψ2], f=1−μ/r2+β/r4f=1-\mu/r^2+\beta/r^4f=1−μ/r2+β/r4, A=−Q/(2r2) dvA=-Q/(2r^2)\,dvA=−Q/(2r2)dv, on {r>0, 0<u<1}\{r>0,\ 0<u<1\}{r>0, 0<u<1} for all real μ,β,Q\mu,\beta,Qμ,β,Q. VI-02: g=−B dt2+D dr2+A[du2/(4u(1−u))+u(1−u)(dφ−dψ)2]+H(σ−W dt)2g=-B\,dt^2+D\,dr^2+A[du^2/(4u(1-u))+u(1-u)(d\varphi-d\psi)^2]+H(\sigma-W\,dt)^2g=−Bdt2+Ddr2+A[du2/(4u(1−u))+u(1−u)(dφ−dψ)2]+H(σ−Wdt)2, σ=u dφ+(1−u) dψ\sigma=u\,d\varphi+(1-u)\,d\psiσ=udφ+(1−u)dψ, A=Φ dt+Ψ σ\mathcal A=\Phi\,dt+\Psi\,\sigmaA=Φdt+Ψσ, F=dAF=d\mathcal AF=dA, radial data C∞C^\inftyC∞ on an open RRR with B,D,A,H≠0B,D,A,H\neq0B,D,A,H=0 (ESData).

Formalization targets

  • Goal: under ESData, R≠∅R\neq\emptysetR=∅ and A>0A>0A>0, H>0H>0H>0, BD>0BD>0BD>0 on RRR, the Einstein–Maxwell certificate on {x1∈R, 0<u<1}\{x_1\in R,\ 0<u<1\}{x1​∈R, 0<u<1} holds iff the five reduced Einstein and two reduced Maxwell radial equations hold on RRR.
  • VI-01: certificate iff 3β=Q23\beta=Q^23β=Q2; Ricci-flat certificate iff β=0\beta=0β=0; explicit counterexample at (1,1,1)(1,1,1)(1,1,1).
  • VI-02: pointwise completeness of the seven reduced equations; fixed certified instances MP01 (VacuumCertificate, and EMCertificateD with F=0F=0F=0), MP02 (EMCertificateD with F=0F=0F=0 only, via the reduced system; no VacuumCertificate statement) and ST03 (static charged EMCertificateD); refutations NW01 (¬\neg¬ VacuumCertificate) and NQ14 (¬\neg¬ EMCertificateD).

Significance

A machine-checked reduction of a five-dimensional Einstein–Maxwell system to seven radial equations, with explicit domains and hypotheses, usable as a certificate checker for equal-spin candidates; the VI-01 criterion derives the charge normalisation.

Difficulty

All curvature data are explicit closed forms generated as CAS hints and re-proved in Lean component by component (Christoffel, derivative, Ricci and Einstein–Maxwell tables); the reduction needs frame reconstruction lemmas for every residual component.

Formalization scope (what is NOT claimed)

  • EF5 is one fixed explicit static ansatz (f = 1 − μ/r² + β/r⁴, A = −Q/(2r²) dv); cert_iff (3β = Q²) is a criterion inside that ansatz, not a classification of five-dimensional black holes.
  • EF5.vacuum_cert and EF5.vacuum_cert_iff conclude EMCertificateD with F = faraday5 0 (= 0); the literal VacuumCertificate (Ricci-flat) iff is EF5.vacuumCertificate_iff.
  • ESData asks only: R open, the seven radial functions C^∞ on R, and B, D, A, H nonvanishing on R — no field equation. The Goal additionally assumes R.Nonempty and A > 0, H > 0, B·D > 0 on R. No B > 0, D > 0, connectedness, exterior-only or q ≠ 0 hypothesis is assumed or claimed.
  • ReducedSystem consists of FIVE Einstein (redE_tt, redE_tσ, redE_rr, redE_uu, redE_σσ) and TWO Maxwell (redJ_t, redJ_σ) radial equations; F = d𝒜 by definition supplies closedness. It is not the VI-03 six-state CAS system.
  • MP01 and MP02 are two specified certified parameter instances (μ = 3, a = 1/2 and μ = 5 with rotation constant −2/3), not a certified arbitrary-parameter Myers–Perry theorem. ST03 is one fixed static charged instance (μ = 2, Q = 1, β = 1/3).
  • Each candidate domain is exactly esDomain {r | 0 < r ∧ rX r ≠ 0} with the listed exclusion polynomial rX; no horizon or axis (u ∈ {0,1}) regularity is claimed.
  • NW01 refutes VacuumCertificate (¬ Ricci-flat certificate); NQ14 refutes EMCertificateD. They are ordinary theorems with negated conclusions, not disproofs of positive targets.
  • B3 coordinate certificates on the registered local layer (PHYSICS_BOUNDARY §N, §O), not Mathlib-only or manifold results. No new exact charged-rotating solution, global event horizon, uniqueness, gauge equivalence or static ↔ EF coordinate bridge is claimed.
  • Supplementary Python/CAS/search evidence (VI-01 per-run instances, PS00/PS02 pilot replays, VI-03 CAS) is outside this release and is not a formally verified search algorithm. EF5/Certificate.lean conditional assemblers are excluded.

Selected references

  • R. C. Myers, M. J. Perry, Ann. Phys. 172 (1986) 304 — family name of MP01/MP02/NW01 (not accessed; see LITERATURE_CHECK.md).
  • F. R. Tangherlini, Nuovo Cimento 27 (1963) 636 — family name of EF5/ST03 (not accessed; normalisation derived in Lean).
  • Exact Vacuum / Spacetime Atlas I, II, IV (the author's earlier releases; bundles and nodes reused unchanged).
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🏆Completed
Formal VerificationTheoretical Computer Science·Captain: pitimespi

SegWit Non-Malleability: the txid of a Native Witness Spend Is a Function of the Sender's IntentTextbook

The claim

A transaction's identifier is the hash of its serialization without witnesses, and that serialization still contains every scriptSig. Before SegWit, anyone could take a valid transaction, re-encode a signature inside a scriptSig (a different DER length byte, a negated S value, an extra push), and broadcast a transaction that was equally valid and had a different txid. Any protocol that referred to an unconfirmed transaction by its txid — a refund signed against a funding transaction, which is how payment channels start — could be broken this way. BIP141 fixed it by moving signatures into the witness, which the txid does not hash, and by requiring the scriptSig of a native witness spend to be empty.

This mission proves that the fix does what it claims, against the published VerifyScript and CheckInputScripts: for a transaction whose inputs spend native witness programs and whose scripts verify, the txid is a function of the sender's intent — version, prevouts, sequence numbers, outputs, lock time — and of nothing else.

What is proved

  • native_scriptSig_empty — under CheckInputScripts with WITNESS, every input spending a native witness program has an empty scriptSig (SCRIPT_ERR_WITNESS_MALLEATED, lifted from the VerifyScript mission's native_witness_malleated).
  • txid_of_intent — for such a transaction, txid tx = txid (ofIntent (intentOf tx)): the identifier of the canonical transaction with empty scriptSigs and witnesses.
  • txid_determined (goal) — two transactions with the same intent, both verifying against the same ledger, with native witness-program inputs, have the same txid.
  • p2sh_wrapped_scriptSig — for a P2SH output spent with witness data, VerifyScript succeeds only if the scriptSig is exactly the single push of the witness program it evaluates to (SCRIPT_ERR_WITNESS_MALLEATED_P2SH); the scriptSig of a wrapped spend is determined by the redeem script.

What this rests on

The serialization mission's txid_strip (the txid ignores witnesses), the VerifyScript mission's native_witness_malleated, and the block mission's CheckInputScripts. The statements are about the executable VerifyScript; no assumption about hash functions or signatures is needed, because the theorems say nothing about whether a valid spend exists, only that its identifier is fixed once it does. (interpreter.cpp, transaction.h, BIP141)

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🏆Completed
Formal VerificationTheoretical Computer Science·Captain: pitimespi

The 21 Million Bound as a Chain Invariant: totalValue(S)+genesisSubsidy≤issued(S)\mathrm{totalValue}(S) + \mathrm{genesisSubsidy} \le \mathrm{issued}(S)totalValue(S)+genesisSubsidy≤issued(S) on Every Reachable StateTextbook

The claim

"There will only ever be 21 million bitcoin" is the most repeated statement about the system, and it is usually justified by the subsidy schedule alone: fifty coins, halved every 210,000 blocks, summing to just under 21 million. The schedule is proved in the subsidy mission (BitcoinSubsidy.issuance_total). But the schedule is a bound on what a coinbase may create; whether the chain's spendable supply stays within it depends on the whole validation path — that every non-coinbase transaction spends at least what it creates, that no input is spent twice, that the coinbase is bounded by the subsidy plus the fees actually collected, and that these checks run against the right ledger at every step. The inflation bug CVE-2018-17144 was a failure of exactly this composition, not of the schedule.

This mission states the bound as a property of every chain state reachable from the genesis under the block mission's validate, and proves it.

What is proved

With totalValue u the sum of the values of the coins of a ledger and issued n the subsidy GetBlockSubsidy has issued to a chain of n blocks:

  • update_bound — UpdateCoins raises the total by at most the value created and lowers it by the value spent (ledger arithmetic on a distinct-key ledger).
  • values_in_range — every coin of a reachable state has a value in [0, MAX_MONEY], because coins only enter as outputs that passed CheckTransaction.
  • block_supply — one accepted block raises the total by at most GetBlockSubsidy(height): the coinbase bound of ConnectBlock against the fees, through the acceptance theorem and ApplySpec of the block mission.
  • supply_bound (goal) — on every reachable state, totalValue S.utxo + genesisSubsidy ≤ issued S.nextHeight.
  • supply_lt_maxMoney — hence totalValue S.utxo < MAX_MONEY on every reachable state.

The genesis subsidy appears on the left because the source never adds the genesis coinbase to the UTXO set (ConnectBlock's genesis shortcut), so it is issued but unspendable; the inequality is exact at the genesis state.

What this rests on

The block mission's acceptance (the executable validation accepts iff the declarative specification holds), apply_spec (the resulting state is ApplySpec), reachable_wellFormed (distinct ledger keys), and the subsidy mission's core_eq_spec and issuance_bound. Nothing about the rules is re-transcribed here; the mission adds the quantities and the induction. (validation.cpp, tx_verify.cpp, coins.cpp)

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Operations ResearchOptimizationProbability+1·Captain: Shuze Chen

Optimal Stopping and Free-Boundary Problems VI: Uniqueness of the American Put BoundaryTextbook

Motivation

An American put option gives its holder the right to sell an asset for a fixed strike price before a specified maturity. Exercising produces an immediate payoff, while waiting preserves the possibility of receiving a larger payoff later. The pricing problem therefore includes a decision about when to stop. In the geometric Brownian model without dividends, this decision is described by an exercise threshold that changes with the remaining time. Section 25 of Peskir and Shiryaev (2006) connects that threshold to a nonlinear integral equation.

The mission concerns the uniqueness part of this connection. A formula that the optimal threshold satisfies does not by itself characterize the threshold: an equation could admit additional solutions unrelated to the stopping problem. Theorem 25.3 identifies a class in which this ambiguity disappears. Its endpoint conditions deserve particular attention because the competing functions are not required to begin or end at prescribed values.

Setting

Let BBB be a standard real Brownian motion, with its natural filtration, on a probability space with measure PPP. Fix an interest rate r>0r>0r>0, volatility σ>0\sigma>0σ>0, strike price K>0K>0K>0, and maturity T>0T>0T>0. For initial asset price x>0x>0x>0, the geometric Brownian stock at elapsed time sss is

Xsx=xexp⁡(σBs+(r−σ2/2)s).X_s^x=x\exp\bigl(\sigma B_s+(r-\sigma^2/2)s\bigr).Xsx​=xexp(σBs​+(r−σ2/2)s).

Calendar time is denoted by ttt. A holder starting at ttt may choose any stopping time τ\tauτ taking values in [0,T−t][0,T-t][0,T−t]. The payoff function is G(x)=(K−x)+G(x)=(K-x)^+G(x)=(K−x)+, and discounting turns its expected value into R(x,τ)=E[e−rτG(Xτx)]R(x,\tau)=E[e^{-r\tau}G(X_\tau^x)]R(x,τ)=E[e−rτG(Xτx​)]. The stopping value is defined operationally:

V(t,x)=sup⁡0≤τ≤T−tR(x,τ).V(t,x)=\sup_{0\le\tau\le T-t}R(x,\tau).V(t,x)=0≤τ≤T−tsup​R(x,τ).

The supremum ranges over stopping times, rather than arbitrary random times. The exercise boundary b(t)b(t)b(t) is extracted from the states where V(t,x)=G(x)V(t,x)=G(x)V(t,x)=G(x), restricting the defining supremum to [0,K][0,K][0,K]. Before maturity, these are precisely the positive stock states below the boundary. At maturity, every state has value equal to payoff, while the exercise threshold is KKK. These conventions express the setting of Section 25.2, pp. 379–383.

Formalization targets

Write γ=r−σ2/2\gamma=r-\sigma^2/2γ=r−σ2/2 and let Φ\PhiΦ be the standard normal distribution function. For positive u,x,yu,x,yu,x,y, set

F(u,x,y)=Φ(log⁡(y/x)−γuσu).F(u,x,y)=\Phi\left(\frac{\log(y/x)-\gamma u}{\sigma\sqrt u}\right).F(u,x,y)=Φ(σu​log(y/x)−γu​).

At zero elapsed time, F(0,x,y)=1{x≤y}F(0,x,y)=\mathbf1_{\{x\le y\}}F(0,x,y)=1{x≤y}​; the zero-stock case uses its deterministic transition law. The goal, Theorem 25.3, p. 386, states that bbb is the unique continuous nondecreasing function ccc on [0,T][0,T][0,T] satisfying 0<c(t)<K0<c(t)<K0<c(t)<K for 0<t<T0<t<T0<t<T and

K−c(t)=e−r(T−t)∫0KF(T−t,c(t),K−z) dz+rK∫0T−te−ruF(u,c(t),c(t+u)) du.K-c(t)=e^{-r(T-t)}\int_0^K F(T-t,c(t),K-z)\,dz +rK\int_0^{T-t}e^{-ru}F(u,c(t),c(t+u))\,du.K−c(t)=e−r(T−t)∫0K​F(T−t,c(t),K−z)dz+rK∫0T−t​e−ruF(u,c(t),c(t+u))du.

The goal includes satisfaction of the equation by the actual exercise boundary and equality of every candidate solution with that boundary on the whole time interval.

Four milestones retain the chapter's supporting statements. Equation (25.2.14) gives the explicit time-continuity estimate, with Ss=Xs1S_s=X_s^1Ss​=Xs1​, L(δ)=E[1−inf⁡0≤s≤δSs]L(\delta)=E[1-\inf_{0\le s\le\delta}S_s]L(δ)=E[1−inf0≤s≤δ​Ss​], and τ2=τ1∧(T−t2)\tau_2=\tau_1\wedge(T-t_2)τ2​=τ1​∧(T−t2​) for an optimizer τ1\tau_1τ1​ at t1t_1t1​:

0≤V(t1,x)−V(t2,x)≤xE[Sτ2]L(t2−t1)≤xerTL(t2−t1).0\le V(t_1,x)-V(t_2,x)\le xE[S_{\tau_2}]L(t_2-t_1)\le xe^{rT}L(t_2-t_1).0≤V(t1​,x)−V(t2​,x)≤xE[Sτ2​​]L(t2​−t1​)≤xerTL(t2​−t1​).

Equations (25.2.23)–(25.2.25) assert that bbb is continuous and nondecreasing, b(T)=Kb(T)=Kb(T)=K, and its first hitting rule is optimal. Equation (25.2.28) states smooth fit, Vx(t,b(t))=−1V_x(t,b(t))=-1Vx​(t,b(t))=−1, before maturity. Equation (25.2.36) gives the early-exercise premium identity

V(t,x)=e−r(T−t)E[G(XT−tx)]+rK∫0T−te−ruP(Xux≤b(t+u)) du.V(t,x)=e^{-r(T-t)}E[G(X_{T-t}^x)]+rK\int_0^{T-t}e^{-ru}P(X_u^x\le b(t+u))\,du.V(t,x)=e−r(T−t)E[G(XT−tx​)]+rK∫0T−t​e−ruP(Xux​≤b(t+u))du.

The source locations for these milestones are pp. 381–385.

Significance

The boundary characterization links a stopping problem, defined by optimization over a class of random times, to a deterministic equation for a function of one time variable. Uniqueness distinguishes the optimal exercise threshold from merely formal solutions of that equation. The premium identity also identifies the additional value of exercising before maturity relative to a European option with the same payoff, within the assumptions of the model.

These are established mathematical results from the textbook. This mission poses their Lean statements; it does not claim completed machine-checked proofs. A completed development would connect Brownian motion, stopping times, value-function regularity, and a nonlinear integral equation in a shared formal model. The definitions of admissible times, finite-horizon hitting rules, and transition probabilities can support other stopping problems without encoding a proposed optimizer into the value itself.

Difficulty

Satisfaction of the equation by the optimal boundary leaves the central uniqueness question unresolved. Candidate functions cannot be assumed to be optimal, to have the same stopping region, or to satisfy stronger differentiability properties. Likewise, the value is not globally twice differentiable in the stock state across the boundary. The source explicitly discusses this regularity issue on p. 385.

The displayed derivation of the time-continuity estimate also contains a dependence issue: a Brownian increment evaluated at a stopping-time-dependent duration need not be independent of the starting information. Finding F1 records the exact step in (25.2.13). The estimate (25.2.14) remains a milestone as stated. Findings F3 and F4 identify an equality sign and a left-limit transcription issue in the boundary-continuity argument. These observations concern the derivations and do not replace the source's targets.

Finding F5 concerns the broader, unselected left-continuous class in Remark 25.4: left continuity does not control the value at the initial endpoint. Under the probabilistic zero-stock convention, setting c(0)=0c(0)=0c(0)=0 and c(t)=b(t)c(t)=b(t)c(t)=b(t) for t>0t>0t>0 preserves the equation while changing that endpoint. The selected theorem requires continuity on the closed interval and excludes this example. F2's continuous-class relaxation of monotonicity remains unaffected.

Formalization scope

All statements use strictly positive r,σ,K,Tr,\sigma,K,Tr,σ,K,T and positive initial stock states. Brownian paths are represented by an everywhere-continuous version with measurable evaluations. Elapsed times are nonnegative reals; calendar times and stock states are real. The natural Brownian and stock filtrations coincide for positive initial stock because the exponential transformation is invertible. The time horizon bounds stopping times pointwise, representing bounded versions of the source's admissible rules.

Expectations are real Bochner integrals. Discounted put rewards are bounded by KKK and measurable; the reward set is nonempty because immediate exercise is admissible. Thus the real supremum and expectations do not depend on default values for empty sets or nonintegrable functions. The hitting-time definition inserts maturity into the set before taking its infimum, giving exactly the prescribed value T−tT-tT−t when the boundary is never reached.

The boundary is defined from the stopping value, and the stopping value from rewards; defining either by the candidate Volterra equation would trivialize the goal and is excluded. Candidate functions are represented on the real line, but continuity, monotonicity, the equation, and uniqueness concern only [0,T][0,T][0,T]. No candidate endpoint values are assumed. Smooth fit excludes maturity, where the payoff has a corner. The transition kernel treats zero elapsed time, zero stock, and zero threshold explicitly. Both Gaussian integrals, the discount factors, and the factor rKrKrK are retained.

There are no asymptotic constants to instantiate. The estimate keeps the source's exact factor erTe^{rT}erT and the intermediate stopped-stock expectation. Mathlib supplies the probability, filtration, Brownian-motion, integration, and differential-calculus foundations. The remaining work concerns their application to this stopping problem. The perpetual option and the broader nonmonotone uniqueness class of Remark 25.4 are outside the selected theorem items; the latter remains documented as finding F2.

Selected references

  • Goran Peskir and Albert N. Shiryaev, Optimal Stopping and Free-Boundary Problems, Lectures in Mathematics ETH Zürich, Birkhäuser Verlag, 2006, Section 25.2, pp. 379–392. DOI: 10.1007/978-3-7643-7390-0. The pinned published PDF supplies all theorem and equation numbering; PDF pages are printed pages plus 16 in this section.
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ProbabilityStochastic Systems·Captain: Shuze Chen

Optimal Stopping and Free-Boundary Problems IV: Sharp Logarithmic Maximal InequalitiesTextbook

Motivation

A process may make a large excursion before returning to a small terminal value. A maximal inequality quantifies how much information about that excursion is retained by a terminal observation. For nonnegative submartingales, logarithmic moments provide a useful level of control between a first moment and a fixed higher power moment. This mission concerns the exact constants and equality cases in the logarithmic inequalities developed in Section 19.2 of Peskir and Shiryaev, Optimal Stopping and Free-Boundary Problems.

The dependence on the initial value is part of the question. A bound for a process started at zero does not identify the best additive constant for a process started at a specified positive value. Similarly, replacing a logarithm by its positive part changes the optimal bound. The book identifies both changes explicitly, including a middle branch that occurs only in the positive-logarithm case.

Setting

A submartingale is an integrable process whose conditional expected future value is at least its present value. Consider such a process X=(Xt)t≥0X=(X_t)_{t\ge0}X=(Xt​)t≥0​ on a filtered probability space, with X0=x≥0X_0=x\ge0X0​=x≥0 and nonnegative values. Its paths are càdlàg: right continuous with finite left limits. The family is uniformly integrable, so large values contribute uniformly little to its first moments. The terminal variable X∞X_\inftyX∞​ is its almost sure limit as time tends to infinity.

Two cost functions are considered:

Glog⁡(y)=ylog⁡y,G+(y)=ylog⁡+y,log⁡+y=max⁡(log⁡y,0),G_{\log}(y)=y\log y,\qquad G_+(y)=y\log^+y, \qquad \log^+y=\max(\log y,0),Glog​(y)=ylogy,G+​(y)=ylog+y,log+y=max(logy,0),

with both costs assigned value zero at y=0y=0y=0. The first cost can be negative, but is bounded below by −1/e-1/e−1/e. The second cost is nonnegative.

The Brownian milestones use standard Brownian motion BBB, started at zero, and the reflected, shifted process Xt=∣Bt+x∣X_t=|B_t+x|Xt​=∣Bt​+x∣. Write St=max⁡0≤r≤tXrS_t=\max_{0\le r\le t}X_rSt​=max0≤r≤t​Xr​. The admissible random horizons are stopping times τ\tauτ of BBB with Eτr<∞\mathbb E\tau^r<\inftyEτr<∞ for at least one real exponent r>1/2r>1/2r>1/2. The exponent is allowed to depend on the stopping time. All coefficients below use a fixed real parameter c>1c>1c>1.

Formalization targets

The goal is Corollary 19.6, including sharpness:

Esup⁡t>0Xt≤WG(x;c)+c EG(X∞).\mathbb E\sup_{t>0}X_t\le W_G(x;c)+c\,\mathbb E G(X_\infty).Et>0sup​Xt​≤WG​(x;c)+cEG(X∞​).

The costs have different explicit additive constants. Define

α=c−1c,v∗=ce(c−1),u∗=1+1ec(c−1),s∗=cc−1.\alpha=\frac{c-1}{c},\qquad v_*=\frac{c}{e(c-1)},\qquad u_*=1+\frac{1}{e^c(c-1)},\qquad s_*=\frac{c}{c-1}.α=cc−1​,v∗​=e(c−1)c​,u∗​=1+ec(c−1)1​,s∗​=c−1c​.

The constants are

Wlog⁡(x;c)={c2e(c−1),0≤x≤v∗,cxlog⁡ ⁣cx(c−1),x>v∗,W_{\log}(x;c)=\begin{cases} \dfrac{c^2}{e(c-1)},&0\le x\le v_*,\\ cx\log\!\dfrac{c}{x(c-1)},&x>v_*, \end{cases}Wlog​(x;c)=⎩⎨⎧​e(c−1)c2​,cxlogx(c−1)c​,​0≤x≤v∗​,x>v∗​,​

and

W+(x;c)={u∗,0≤x≤u∗,x+(1−x)log⁡(x−1)−(c+log⁡(c−1))(x−1),u∗<x≤s∗,cxlog⁡ ⁣cx(c−1),x>s∗.W_+(x;c)=\begin{cases} u_*,&0\le x\le u_*,\\ x+(1-x)\log(x-1)-(c+\log(c-1))(x-1),&u_*<x\le s_*,\\ cx\log\!\dfrac{c}{x(c-1)},&x>s_*. \end{cases}W+​(x;c)=⎩⎨⎧​u∗​,x+(1−x)log(x−1)−(c+log(c−1))(x−1),cxlogx(c−1)c​,​0≤x≤u∗​,u∗​<x≤s∗​,x>s∗​.​

Adjacent formulas agree at the displayed endpoints. Sharpness means that for each specified x,cx,cx,c and either cost, an example in the stated submartingale class has finite cost expectation and attains equality.

The milestone sequence follows the book: Theorem 19.1 is the zero-start Brownian Llog⁡LL\log LLlogL bound; Corollary 19.2 gives its shifted form; Theorem 19.3 gives the zero-start Llog⁡+LL\log^+LLlog+L bound; Corollary 19.4 gives all three branches of its shifted form. The respective Brownian equality rules are

σ∗=inf⁡{t>0:St≥v∗, Xt=αSt},τ∗=inf⁡{t>0:St≥u∗, Xt=1∨αSt}.\sigma_*=\inf\{t>0:S_t\ge v_*,\ X_t=\alpha S_t\},\qquad \tau_*=\inf\{t>0:S_t\ge u_*,\ X_t=1\vee\alpha S_t\}.σ∗​=inf{t>0:St​≥v∗​, Xt​=αSt​},τ∗​=inf{t>0:St​≥u∗​, Xt​=1∨αSt​}.

Each Brownian target includes admissibility of its displayed time and equality there. The unshifted constants are c2/[e(c−1)]c^2/[e(c-1)]c2/[e(c−1)] and 1+1/[ec(c−1)]1+1/[e^c(c-1)]1+1/[ec(c−1)], respectively. These are the exact statements of Section 19.2, printed pages 274–282.

Significance

The result measures an expected path maximum using a single terminal cost while retaining the optimal dependence on the starting state. Equality cases identify when the bound is exact; they prevent the constants from being regarded as convenient but arbitrary estimates. The positive-logarithm middle branch also records the effect of making the cost vanish below one.

The mathematical results are established in the cited book. The requested formal work consists of proofs of precisely stated targets, including the stochastic hypotheses, the terminal limit, and the equality assertions. The definition layer supplies reusable extended expectations, running maxima, admissible stopping times, and the two drawdown rules. The current proposal contains compiled theorem statements, not completed Lean proofs of these results.

Difficulty

Generic power-moment maximal inequalities do not supply the displayed logarithmic constants or their equality rules. Establishing the exact result requires control of unbounded random horizons and of the interaction between the running maximum and the terminal cost. In particular, ylog⁡yy\log yylogy is not globally nonnegative, while the derivative of ylog⁡+yy\log^+yylog+y changes at one.

The general submartingale extension has an additional dependency. The book invokes Jacka's maximal embedding result on printed page 282; a time-change statement alone does not supply that conclusion. The printed invocation does not explicitly transfer the Brownian moment hypothesis. A separate proof warning concerns the replacement stopping time used on printed page 281, which can depend on future information as written. The final sentence of the proof of Corollary 19.6 also asserts an expectation-supremum identity that fails for the plain logarithmic cost, even for Brownian motion stopped on exiting a bounded interval. These proof warnings are recorded with evidence in the local findings register. The source theorem statements remain the targets; there are no variant theorems.

Formalization scope

Time is represented by nonnegative real numbers. Random times and path maxima take values in [0,∞][0,\infty][0,∞]; an empty hitting set therefore has infimum +∞+\infty+∞. At a finite time, a continuous Brownian path has a finite maximum on the closed interval, so its use as a real boundary argument is justified. Stopped evaluation at an infinite time is assigned a default value, but every Brownian theorem requires or concludes almost sure finiteness before using that evaluation.

Logarithmic expectations are formed by integrating G(Y)+1/eG(Y)+1/eG(Y)+1/e as a nonnegative extended function, then subtracting the finite constant 1/e1/e1/e. This keeps negative finite expectations and positive infinity intact. No finite logarithmic-moment assumption is added to the goal. Its terminal convergence is a conclusion. Sharpness examples are existentially quantified on a fixed canonical path carrier and may use their own probability measure and filtration.

The Brownian setup chooses measurable evaluations and everywhere continuous, zero-start paths, the usual continuous version convention. Its stopping times use the natural filtration. Submartingale path properties hold simultaneously outside one null set. The goal uses the printed supremum over strictly positive times, which agrees with inclusion of zero under right continuity.

All constants are explicit in the source; there are no O(⋅)O(\cdot)O(⋅) replacements. The bound formulas are independent algebraic functions, not definitions of optimal stopping values. Defining an admissible process by the desired inequality, assuming the stated time is optimal, or replacing a nonintegrable expectation by a total real integral returning zero would trivialize or change the targets and is excluded.

Selected references

  • Goran Peskir and Albert N. Shiryaev, Optimal Stopping and Free-Boundary Problems, Lectures in Mathematics ETH Zürich, Birkhäuser Verlag, 2006. Chapter V, Section 19.2, printed pages 274–282; PDF pages 292–300. DOI: 10.1007/978-3-7643-7390-0.
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Control TheoryProbability·Captain: Shuze Chen

Entropy Regularization for Mean Field Games with Learning: Explicit Nash equilibrium with enhanced entropyResearch Paper

Motivation

A large population of interacting agents can make individual decisions depend on the distribution of everyone else's states and actions. A mean field game describes this interaction through a representative agent and population distributions. Randomizing actions allows an agent to explore while still responding to the population. Entropy regularization assigns a reward to that exploration. Guo, Xu and Zariphopoulou study how both individual exploration and exploration of other agents' actions enter a continuous-time linear-quadratic game (arXiv:2010.00145v2, §§1–2).

The target is their explicit enhanced-entropy equilibrium, including its value and controlled state. The source supplies closed formulas, but agreement between a Gaussian policy, its induced dynamics, and the asserted equilibrium requires several distinct mathematical claims. Preserving those distinctions also exposes two discrepancies in the printed result. This mission states the printed theorem, with those findings recorded separately.

Setting

Time ranges over a finite interval [t,T][t,T][t,T], where 0<T<∞0<T<\infty0<T<∞ and 0≤t≤T0\le t\le T0≤t≤T. Both states and actions range over all of R\mathbb RR. A randomized policy πs\pi_sπs​ is a Lebesgue probability density on the action line. It is nonnegative almost everywhere and integrates to one; atomic distributions are outside this policy class.

The population state law at time sss is μs\mu_sμs​, its mean is ms=∫x μs(dx)m_s=\int x\,\mu_s(dx)ms​=∫xμs​(dx), and its action density at state xxx is αs(u;x)\alpha_s(u;x)αs​(u;x). The initial law is ν\nuν. On a filtered probability space satisfying the usual conditions, WWW is a standard Brownian motion and ξ∼ν\xi\sim\nuξ∼ν is independent of WWW, measurable at time ttt, and has finite second moment. The initial law has a density but need not be Gaussian.

For positive A,B,DA,B,DA,B,D, the controlled state follows the original equation

dXs=[∫R(A(ms−Xs)+Bu)πs(u)du]ds+D∫Ru2πs(u)du dWs,Xt=ξ.dX_s=\left[\int_{\mathbb R}\bigl(A(m_s-X_s)+Bu\bigr)\pi_s(u)du\right]ds +D\sqrt{\int_{\mathbb R}u^2\pi_s(u)du}\,dW_s,\qquad X_t=\xi.dXs​=[∫R​(A(ms​−Xs​)+Bu)πs​(u)du]ds+D∫R​u2πs​(u)du​dWs​,Xt​=ξ.

The running state penalty is −Q(Xs−ms)2/2-Q(X_s-m_s)^2/2−Q(Xs​−ms​)2/2 and the terminal penalty is −Qˉ(XT−mT)2/2-\bar Q(X_T-m_T)^2/2−Qˉ​(XT​−mT​)2/2, with Q,Qˉ>0Q,\bar Q>0Q,Qˉ​>0.

Shannon differential entropy is HSE(π)=−∫πlog⁡πH_{SE}(\pi)=-\int\pi\log\piHSE​(π)=−∫πlogπ. Population cross-entropy is

HCE(π,α,μ)=−∫Rπ(u)[∫Rlog⁡α(u;y) μ(dy)]du.H_{CE}(\pi,\alpha,\mu)=-\int_{\mathbb R}\pi(u)\left[\int_{\mathbb R}\log\alpha(u;y)\,\mu(dy)\right]du.HCE​(π,α,μ)=−∫R​π(u)[∫R​logα(u;y)μ(dy)]du.

It averages log densities, rather than taking the logarithm of a mixture. The running reward adds λSEHSE+λCEHCE\lambda_{SE}H_{SE}+\lambda_{CE}H_{CE}λSE​HSE​+λCE​HCE​, where λSE>0\lambda_{SE}>0λSE​>0 and λCE≥0\lambda_{CE}\ge0λCE​≥0. The agent maximizes the expected integrated running reward plus the terminal reward (§3.2, pp. 9–10).

Admissibility includes all five source clauses: densities almost surely at each time, progressive action-set probabilities, a unique strong solution, finite expected total payoff, and a measurable Markov feedback realizing the policy simultaneously in time almost surely. A Nash equilibrium is an admissible best response against every admissible deviation, together with consistency of the state law and population feedback (Definition 1, p. 6).

Formalization targets

There is one goal: the full Theorem 4, equations (3.9)–(3.14), on pp. 10–11 of the pinned preprint. Put m∗=Eξm^*=\mathbb E\xim∗=Eξ, v0=Eξ2−(Eξ)2v_0=\mathbb E\xi^2-(\mathbb E\xi)^2v0​=Eξ2−(Eξ)2, r=(λSE+λCE)/λSEr=(\lambda_{SE}+\lambda_{CE})/\lambda_{SE}r=(λSE​+λCE​)/λSE​, and c=2A+rB2/D2c=2A+rB^2/D^2c=2A+rB2/D2. The printed coefficients are

ηs=Qˉe−c(T−s)+Qc(1−e−c(T−s)),K=−(A+B2/D2)r,M=(Br/D)2,\eta_s=\bar Qe^{-c(T-s)}+\frac Qc(1-e^{-c(T-s)}),\quad K=-(A+B^2/D^2)r,\quad M=(Br/D)^2,ηs​=Qˉ​e−c(T−s)+cQ​(1−e−c(T−s)),K=−(A+B2/D2)r,M=(Br/D)2, κs=e(2K+M)(s−t)v0+∫tse(M+2K)(s−z)λSE+λCEηz dz,\kappa_s=e^{(2K+M)(s-t)}v_0+\int_t^s e^{(M+2K)(s-z)}\frac{\lambda_{SE}+\lambda_{CE}}{\eta_z}\,dz,κs​=e(2K+M)(s−t)v0​+∫ts​e(M+2K)(s−z)ηz​λSE​+λCE​​dz, γs=λSE+λCE2log⁡2π(λSE+λCE)D2(T−s)−λSE+λCE2∫sTlog⁡ηz dz+B22D2λCE(λSE+λCE)λSE2∫sTηzκz dz.\begin{aligned} \gamma_s={}&\frac{\lambda_{SE}+\lambda_{CE}}2\log\frac{2\pi(\lambda_{SE}+\lambda_{CE})}{D^2}(T-s) -\frac{\lambda_{SE}+\lambda_{CE}}2\int_s^T\log\eta_z\,dz\\ &+\frac{B^2}{2D^2}\frac{\lambda_{CE}(\lambda_{SE}+\lambda_{CE})}{\lambda_{SE}^2}\int_s^T\eta_z\kappa_z\,dz. \end{aligned}γs​=​2λSE​+λCE​​logD22π(λSE​+λCE​)​(T−s)−2λSE​+λCE​​∫sT​logηz​dz+2D2B2​λSE2​λCE​(λSE​+λCE​)​∫sT​ηz​κz​dz.​

The initial time and variance in κ\kappaκ remain fixed. The claimed policy is

πs∗(u;x)=N ⁣(rB(m∗−x)D2,λSE+λCED2ηs)(u),\pi_s^*(u;x)=N\!\left(\frac{rB(m^*-x)}{D^2},\frac{\lambda_{SE}+\lambda_{CE}}{D^2\eta_s}\right)(u),πs∗​(u;x)=N(D2rB(m∗−x)​,D2ηs​λSE​+λCE​​)(u),

where the second normal parameter is the variance. The theorem claims ηs>0\eta_s>0ηs​>0, existence and admissibility of this equilibrium, and the game value

VEE∗(t)=E[−ηt2(ξ−m∗)2+γt].V^*_{EE}(t)=\mathbb E\left[-\frac{\eta_t}2(\xi-m^*)^2+\gamma_t\right].VEE∗​(t)=E[−2ηt​​(ξ−m∗)2+γt​].

It also claims the same state is the unique strong solution of the printed SDE with drift (A+rB2/D2)(m∗−Xs∗)(A+rB^2/D^2)(m^*-X_s^*)(A+rB2/D2)(m∗−Xs∗​) and diffusion

D(A+rB2/D2)2(Xs∗−m∗)2+λSE+λCED2ηs.D\sqrt{(A+rB^2/D^2)^2(X_s^*-m^*)^2+\frac{\lambda_{SE}+\lambda_{CE}}{D^2\eta_s}}.D(A+rB2/D2)2(Xs∗​−m∗)2+D2ηs​λSE​+λCE​​​.

State/action consistency and ms∗=EXs∗=m∗m_s^*=\mathbb EX_s^*=m^*ms∗​=EXs∗​=m∗ are included. No uniqueness of the equilibrium itself is claimed. There are no separate milestone claims; the other numbered results are outside this mission's dependency scope.

Significance

The target relates exploration temperatures to an explicit time-varying feedback and value, while retaining competition through the population distributions. A Gaussian conditional policy does not imply that the population state law is Gaussian. Keeping arbitrary initial density laws and arbitrary admissible deviations is essential to the theorem's scope (Theorem 4 and §3.4).

The formal statement also makes the source discrepancies inspectable. With T=1T=1T=1, t=0t=0t=0, A=B=D=Q=Qˉ=λSE=1A=B=D=Q=\bar Q=\lambda_{SE}=1A=B=D=Q=Qˉ​=λSE​=1, λCE=0\lambda_{CE}=0λCE​=0, and an independent standard normal initial state, the policy's original squared diffusion is x2+1/ηsx^2+1/\eta_sx2+1/ηs​, whereas (3.13) prints 4x2+1/ηs4x^2+1/\eta_s4x2+1/ηs​. With λCE=1\lambda_{CE}=1λCE​=1, the original drift coefficient is −3-3−3, but the printed KKK is −4-4−4. These are numerical findings against the printed formulas, not repairs embedded in the mission. The goal is an open formal statement, not a machine-checked proof of the paper's claim.

Difficulty

The central issue is preserving the meaning of stochastic control and optimization across several layers. The policy, its actual dynamics, the population law, and the supremum must remain independently defined. A statement only about Gaussian maximization would not establish optimality against the full admissible class. The printed diffusion discrepancy prevents all clauses from being true together for the numerical example above. That source issue remains visible rather than being removed by changing the game.

Formalization scope

Lean represents continuous time, states, and actions by reals. Probability laws use Mathlib measures. General density, signed integration, filtered Brownian motion, and strong-solution definitions precede the paper-specific parameters and reward. The Itô relation is concrete: bounded predictable step approximations converge in integrated squared error in probability, and their Brownian sums converge to the integral process. Only almost-sure finite pathwise energy is required.

Payoffs use guarded signed extended integrals; the total expected reward is finite, and the supremum is in extended reals. Zero cross-entropy weight removes that term. No global expected control energy or fourth initial moment is imposed. Candidate admissibility, existence, optimality, and consistency are conclusions. The unstarred mmm in (3.13) is read as m∗m^*m∗ and the source's progressiveness notation is read relative to its filtration. All conventions appear in the goal's differences list. Theorem 2, Corollary 3.1, and the numerical algorithm study are excluded; no empirical convergence claim is turned into a theorem.

Selected references

  • Xin Guo, Renyuan Xu and Thaleia Zariphopoulou, Entropy Regularization for Mean Field Games with Learning, pinned preprint arXiv:2010.00145v2, December 2021. All theorem numbers and pages in this mission refer to this artifact.
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Markov ChainOperations ResearchProbability+1·Captain: Shuze Chen

Stability of Queueing Networks III: Fluid-Limit Stability Implies Positive Harris RecurrenceTextbook

Why fluid stability matters

A queueing network can have spare capacity at every station and still accumulate an ever-growing population of jobs. Service priorities and routing can prevent the available capacity from being used where it is needed. For networks with general interarrival and service distributions, an explicit stationary distribution is usually unavailable. The stability question therefore needs a description that retains those interactions without requiring a closed formula for the long-run distribution. Chapter 4 of Bramson's Stability of Queueing Networks develops such a description through fluid limits and general-state recurrence theory.

The distinction between stochastic and deterministic stability is central. Emptying a deterministic approximation is a property of trajectories. Positive Harris recurrence is a property of a stochastic process and its invariant measure. The target connects those two properties under a precise accessibility condition on bounded-size states. It applies to renewal primitives with finite first moments, rather than just exponential interarrival and service times.

Networks, states, and fluid trajectories

An open multiclass queueing network has finitely many classes and stations. Every class belongs to one station. Jobs arrive from outside at specified classes, require service, and then either leave or move to another class according to a routing probability matrix PPP. The matrix is substochastic and transient. The external arrival rates form a nonnegative vector α\alphaα, and the strictly positive mean service requirements form the diagonal matrix MMM. The total arrival rates and station loads are

Q=∑n≥0(PT)n,λ=Qα,ρ=CMλ,Q=\sum_{n\ge0}(P^\mathsf T)^n,\qquad \lambda=Q\alpha,\qquad \rho=CM\lambda,Q=n≥0∑​(PT)n,λ=Qα,ρ=CMλ,

where CCC assigns classes to stations. No assumption makes every coordinate of λ\lambdaλ positive.

A head-of-the-line discipline assigns service only to the oldest job of each class. Several classes at one station may share its unit service capacity. The stochastic state records the ordered jobs and their ages, residual interarrival times, residual service times, and current service fractions. Queue populations are unbounded. Its size is ∣x∣=∣z∣+∣u∣+∣v∣|x|=|z|+|u|+|v|∣x∣=∣z∣+∣u∣+∣v∣, where zzz is the queue vector and u,vu,vu,v are the residual-time vectors. This is the state and size of Section 4.1, rather than a finite-state approximation.

The six-component queueing path records arrivals AAA, departures DDD, service effort TTT, immediate workload WWW, idle time YYY, and queue lengths ZZZ. A fluid limit rescales both time and these six components along positive scales tending to infinity. Initial queue mass divided by the scale stays bounded, and both initial residual masses divided by the scale tend to zero. Limits are uniform on every compact time interval and are taken on the common primitive strong-law event.

A fluid model consists of the basic conservation and service equations, possibly augmented by discipline equations. It is stable when one constant N>0N>0N>0 ensures Z(t)=0Z(t)=0Z(t)=0 for every solution at every t≥N∣Z(0)∣t\ge N|Z(0)|t≥N∣Z(0)∣. Different solutions can share the same initial state. Zero initial queue mass is included in the quantification.

Formalization targets

The main target is Theorem 4.16, printed page 121. A petite set has a common nonzero minorizing measure for a probability mixture of transition laws over positive times. If every set {x:∣x∣≤κ}\{x:|x|\le\kappa\}{x:∣x∣≤κ}, κ>0\kappa>0κ>0, is petite, then

stable family of fluid limits ⟹ positive Harris recurrent network.\text{stable family of fluid limits}\ \Longrightarrow\ \text{positive Harris recurrent network}.stable family of fluid limits ⟹ positive Harris recurrent network.

The same conclusion holds whenever an associated fluid model is stable. Association requires a convergent subsequence for every admissible scaling sequence; it is not merely a conditional assertion about limits that might not exist.

The supporting targets retain the chapter's complete selected statements. Theorem 4.1 gives both Harris-recurrence characterizations, including the some-delay and every-delay formulations. Propositions 4.5 and 4.6 give generalized and multiplicative Foster criteria, retaining the quantitative hitting-time bound and the ergodicity clauses. Proposition 4.11 includes all four traffic and fluid-stability conclusions. Proposition 4.12 concerns compactness of all six scaled components. Lemma 4.13 supplies renewal uniform integrability and both maximal-increment limits. Propositions 4.14 and 4.15 retain the pathwise bounds and the expectation bounds uniform over time and initial state.

What the results establish

Positive Harris recurrence means that every set of positive measure for some nonzero sigma-finite irreducibility measure is occupied for infinite time almost surely from every initial state, and that the process admits an invariant probability measure. It is defined independently of the Foster criteria. The capstone therefore supplies a stochastic recurrence conclusion, not just a negative drift inequality or a claim about deterministic trajectories. This is the bridge used by the discipline-specific applications in Chapter 5.

The mathematical results are established results of the book. This mission requests formal proofs of their Lean statements. Its definition layer supplies reusable continuous-time recurrence and petite-set notions, positive iid renewal quantities, and open-network fluid equations. The theorem files are statements awaiting proofs; successful compilation is not presented as a proof of these results.

Where the formalization is demanding

The deterministic limits discard individual jobs and residual times, while the recurrence conclusion concerns the full stochastic state. A statement about one trajectory or one convenient scaling sequence cannot express the uniform assertions that the chapter needs. Likewise, a mean bound at each fixed initial state does not express the double uniform bounds in the selected propositions. General renewal distributions also require residual-time information that a countable-state exponential model can omit. These distinctions are visible in the quantified statements and in the state-space definition.

Formalization scope

The development uses the namespace BramsonQueueing.Fluid. Classes and stations are represented by finite index types of positive size, with every station containing a class. The state is equivalent to the entire ordered-list coordinate space from the source, with its prescribed metric. Time and nonnegative finite quantities use nonnegative reals. Expectations and hitting times that may be infinite use extended nonnegative reals. Uniform limit assertions use explicit tolerance and threshold quantifiers; finite supremum assertions use one finite bound uniform over the source's indices.

The basic fluid equations use the source's interval formulation of nonidling. A single draining constant covers all solutions, including those starting empty. Neither recurrence nor fluid compactness is included as a field of the queueing model. A finite-state substitute, a Poisson or phase-type restriction, or a definition of stability as the desired drift estimate would not express this mission.

There are no O(⋅)O(\cdot)O(⋅) constants to instantiate in the selected targets. The denominator printed as xxx in the second half of (4.93) is read as ∣x∣|x|∣x∣, exactly as in the proof on printed page 121. The service-prefix representation follows the explicit construction when a class starts empty; the boundary defect in displayed (4.47) is recorded separately as a source finding. Both findings retain the printed statements in the local insight record. The invariant measure is normalized to a probability, and total-variation convergence is expressed by a bound uniform over measurable events. All initial states use a common probability space of positive primitive sequences; the initial state parametrizes the paths. These encodings preserve the source laws and conclusions and are declared alongside the affected theorems.

Selected references

  • Maury Bramson, Stability of Queueing Networks, Lecture Notes in Mathematics 1950, Springer, 2008, Chapter 4, printed pages 77–138. Published volume. The canonical PDF has eight preliminary pages before the body, so each selected PDF page is its printed page plus eight.
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Markov ChainOperations ResearchProbability+1·Captain: Shuze Chen

Stability of Queueing Networks I: Product-Form Stationary DistributionsTextbook

Motivation

A queueing network describes jobs that receive service at several stations and move between them before leaving. A stationary distribution answers questions about the network at a typical time: how many jobs are waiting, which classes they belong to, and how jobs are arranged at each station. Such distributions can be difficult to determine because a service completion at one station may immediately create work at another. The classical networks treated by Bramson, Chapter 2 admit explicit formulas despite these interactions.

The chapter distinguishes two ingredients. A single station operating in isolation has a tractable stationary law under suitable service and insertion rules. A network construction then combines eligible stations using random routing. The resulting factorization concerns the states observed at one time. It does not imply independence of entire station trajectories: one job's departure can coincide with another station's arrival.

Setting

A class specifies a kind of job and its assigned station. There are finitely many stations and classes, but each station can contain arbitrarily many jobs. A queue state records an ordered list of jobs. The total effort supplied by a station with nnn jobs is ϕ(n)\phi(n)ϕ(n), positive for n>0n>0n>0. A newly arriving job takes position iii with probability β(i,n)\beta(i,n)β(i,n), where nnn includes that job. The fraction of service effort assigned to position iii is δ(i,n)\delta(i,n)δ(i,n). Both families are nonnegative and sum to one over the occupied positions. Their values depend on positions and queue length, not on job class.

At a homogeneous station of Kelly type, service requirements are exponential with a common positive mean msm^sms. The insertion and service proportions may differ. At a symmetric station, these proportions agree. Service requirements may then be countable mixtures of Erlang distributions. A class-kkk job chooses a positive number sss of exponential stages with probability pk(s)p_k(s)pk​(s); each stage has mean mk(s)m_k(s)mk​(s). The state also records the remaining stage count vvv, with 1≤v≤s1\le v\le s1≤v≤s. Only mixture components of positive probability are included. The mean service requirement is mk=∑s≥1spk(s)mk(s)<∞m_k=\sum_{s\ge1}s p_k(s)m_k(s)<\inftymk​=∑s≥1​spk​(s)mk​(s)<∞.

External arrivals are independent Poisson processes with rates αk\alpha_kαk​. After service at class kkk, a job is routed to class ℓ\ellℓ with probability PkℓP_{k\ell}Pkℓ​ and otherwise exits. Open routing means that the nonnegative fundamental series for PPP is finite. The total arrival vector is λ=(I−PT)−1α\lambda=(I-P^{\mathsf T})^{-1}\alphaλ=(I−PT)−1α. Routing may return a job to its previous station or class. The process has finite exit rates, no explosion, and communicating countable states, as in the chapter's standing setup.

Formalization targets

The goal is Theorem 2.12, printed page 47. Each station may independently be of either eligible type. Define the station load ρj=∑k∈C(j)mkλk\rho_j=\sum_{k\in C(j)}m_k\lambda_kρj​=∑k∈C(j)​mk​λk​ and normalizer

Bj=∑n=0∞ρjn∏i=1nϕj(i).B_j=\sum_{n=0}^{\infty}\frac{\rho_j^n}{\prod_{i=1}^n\phi_j(i)}.Bj​=n=0∑∞​∏i=1n​ϕj​(i)ρjn​​.

If every BjB_jBj​ is finite, the network has stationary distribution

π(x)=∏j=1Jπj(xj).\pi(x)=\prod_{j=1}^J\pi_j(x_j).π(x)=j=1∏J​πj​(xj​).

For a Kelly station, the factor for nnn jobs is

πj(xj)=Bj−1∏i=1nmjsλxj(i)ϕj(i).\pi_j(x_j)=B_j^{-1}\prod_{i=1}^n\frac{m_j^s\lambda_{x_j(i)}}{\phi_j(i)}.πj​(xj​)=Bj−1​i=1∏n​ϕj​(i)mjs​λxj​(i)​​.

For a symmetric station it is

πj(xj)=Bj−1∏i=1npxj(i)(s(i))mxj(i)(s(i))λxj(i)ϕj(i).\pi_j(x_j)=B_j^{-1}\prod_{i=1}^n \frac{p_{x_j(i)}(s(i))m_{x_j(i)}(s(i))\lambda_{x_j(i)}}{\phi_j(i)}.πj​(xj​)=Bj−1​i=1∏n​ϕj​(i)pxj​(i)​(s(i))mxj​(i)​(s(i))λxj​(i)​​.

Four source milestones accompany the goal. Proposition 2.6 identifies stationarity and the reversed process from candidate reversed rates. Theorems 2.7 and 2.8 give the complete single-node laws for homogeneous Kelly and symmetric Erlang-mixture nodes. Theorem 2.11 supplies the network product law for quasi-reversible isolated nodes and also asserts quasi-reversibility of the whole network. All five results retain their source conclusions.

Significance

The product formula reduces a network's stationary law to explicit station factors, while retaining the effective arrival rates created by routing. The refined symmetric-node formula also describes stage information; replacing it with a queue-length marginal would lose part of the theorem. These are established results presented in Bramson's account, not new mathematical conjectures.

The formalization adds a concrete distinction between solving balance equations and obtaining a stationary process. It also separates the general Markov-process and routing definitions from the particular classical service mechanisms. The resulting statement package provides four reusable definition modules and five theorem targets. Proofs of these targets remain open in this pass. Algebraic equilibrium identities already available elsewhere do not by themselves supply the process-level conclusions requested here.

Difficulty

Countably many queue states prevent a finite transition-matrix exponential from serving as the general process model. Exit rates need not have a uniform bound, and the service mixtures need not have finite support. Stationarity must describe evolution at every time, rather than just the flow of mass through a state. Network quasi-reversibility additionally involves joint laws of arrival and exit processes, not only one-time marginal distributions.

The representation must also distinguish events from resulting states. Different insertion positions can produce the same ordered queue, and a routed transition can have several contributing intermediate states. These contributions must be added. Returns within the same station must remain possible. A model that excludes them would omit networks included in Section 2.5.

Formalization scope

The general transition kernel is constructed by the exponential first-jump recursion and a sum over all finite jump counts. Nonexplosion is preservation of total transition probability on every finite horizon. Stationarity is normalization plus invariance under this kernel. The reversed-process conclusion includes the reversed transition-kernel identity and its small-time rates. Quasi-reversibility is expressed by factorization of finite-dimensional count-process cylinders for the present state, past exits, and future external arrivals.

Finite types relabel the station and class indices. Unbounded lists represent queues; a Kelly job is stored as (k,1,1)(k,1,1)(k,1,1), in bijection with its class label. General symmetric jobs retain unbounded positive stage counts. Nonnegative series and integrals use extended nonnegative reals, so divergent normalizers remain infinite. Finite primitive rates use nonnegative reals. Empty products equal one. Station-local classes are represented by a supported subset of an ambient finite type; unused parameter indices do not affect the dynamics, and zero service at an empty station is a convention. No finite stationary mean exit rate, bounded service speed, second moment, or finite mixture support is imposed.

Theorem 2.11 allows arbitrary countable station carriers with the specified class counts and transition mechanisms, as anticipated by the source's more general setting; ordered queues recover its concrete instances. Equation (2.11)'s unbound zzz is read as xxx, supported by Proposition 2.6's proof and equation (2.23); the printed expression and evidence are preserved in the local insight record. No asymptotic constants require instantiation. General-service Theorem 2.9 is outside the selected targets.

No target is defined as a balance equation, a product-form assumption, or a field asserting its own conclusion. Solvers may develop the needed countable-process, summation, and marked-count independence infrastructure without changing the target domains.

Selected references

  • Maury Bramson, Stability of Queueing Networks, Lecture Notes in Mathematics 1950, Springer, 2008, Chapter 2, printed pages 17–52. DOI: 10.1007/978-3-540-68896-9.
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🏆Completed
Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin Script: CScriptNum, the Legacy Interpreter, and the P2PKH TemplateTextbook

Why the script interpreter is the heart of consensus

Every Bitcoin transaction input is authorized by running a small stack program: the input's scriptSig followed by the spent output's scriptPubKey, on the same stack, and the spend is valid when the program leaves a true value on top. Nearly every historical consensus failure in Bitcoin touched this interpreter — the disabled opcodes (CVE-2010-5137), the concatenation bug that let any output be spent (CVE-2010-5141), the overflow in numeric opcodes that prompted the four-byte limit, the CHECKMULTISIG off-by-one that consumes an extra element. Bitcoin Core's EvalScript is 800 lines of C++ with a switch over a hundred opcodes, and VerifyScript wraps it with the pay-to-script-hash and witness-program rules. (interpreter.cpp, script.h)

This mission transcribes the legacy execution path (SigVersion::BASE) — the number representation CScriptNum, the truth test CastToBool, the full non-push opcode table of EvalScript with its resource limits, and VerifyScript for output scripts that are neither pay-to-script-hash nor witness programs — and proves the properties the rest of validation relies on: that script numbers round-trip and are minimal, that truth is "nonzero as a number", that the resource limits and the disabled-opcode rule behave as stated, and that the two classic payment templates do exactly what they are meant to.

Numbers, truth, and the machine

A byte is an 8-bit word and a stack element is a list of bytes. A script number is a little-endian magnitude whose last byte's top bit is the sign: setVch(v)\mathrm{setVch}(v)setVch(v) reads it, serialize(n)\mathrm{serialize}(n)serialize(n) writes the shortest such encoding (appending a sign byte when the magnitude's top bit is already set), and the CScriptNum constructor rejects encodings longer than four bytes (five for the lock-time opcodes) and, under MINIMALDATA, non-minimal ones. castToBool(v)\mathrm{castToBool}(v)castToBool(v) is true when some byte is nonzero, except that a negative zero (0x80 after zero bytes) is false.

A script is the sequence of instructions GetOp yields: a data push carrying its bytes, or a non-push opcode. The interpreter's state is the main stack, the alt stack, the condition stack vfExec, and nOpCount; stacks are lists with the top first. One step on a push checks the 520-byte element limit and pushes when all conditions are true; on an opcode it counts it against the 201-operation limit (only opcodes above OP_16 count, as in the source: OP_1NEGATE, OP_RESERVED and the small-integer pushes are exempt), rejects the fifteen disabled opcodes regardless of execution state, and when executing (or for IF/NOTIF/ELSE/ENDIF always) runs the opcode's case — pushes of small integers, flow control, the stack-manipulation family, EQUAL, the numeric family on four-byte operands, WITHIN, the hash opcodes, CODESEPARATOR, CHECKSIG, CHECKMULTISIG with its key-count accounting, the NOPs, and CHECKLOCKTIMEVERIFY/CHECKSEQUENCEVERIFY when their flags are set — then checks the 1000-element stack limit. Hashes, the lock-time checks, and the signature check (with its encoding rules and FindAndDelete) are an abstract oracle, exactly the role of BaseSignatureChecker and the hash primitives in the source. Running a script steps through it and fails on an unbalanced conditional; verifying evaluates scriptSig then scriptPubKey and requires a non-empty stack with a true top.

Formalization targets

Goal — pay-to-public-key-hash does what it says

verifyScriptPlain([⟨sig⟩,⟨pk⟩], DUP HASH160 ⟨h⟩ EQUALVERIFY CHECKSIG)=true  ⟺  hash160(pk)=h  ∧  checkSig(sig,pk)=some true,\mathrm{verifyScriptPlain}\big([\langle sig\rangle, \langle pk\rangle],\ \mathtt{DUP\ HASH160}\ \langle h\rangle\ \mathtt{EQUALVERIFY\ CHECKSIG}\big) = \mathtt{true} \iff \mathrm{hash160}(pk) = h \;\wedge\; \mathrm{checkSig}(sig, pk) = \mathrm{some}\ \mathtt{true},verifyScriptPlain([⟨sig⟩,⟨pk⟩], DUP HASH160 ⟨h⟩ EQUALVERIFY CHECKSIG)=true⟺hash160(pk)=h∧checkSig(sig,pk)=some true,

for all signatures, keys and hashes within the 520-byte element limit, every flag setting and every oracle. This is BitcoinScript.p2pkh_correct: the template that carries most of Bitcoin's history is correct against the transcribed interpreter, in both directions, with the signature check and hash left abstract. It is the weakest stable statement, since it fixes nothing about the oracle.

Supporting milestones

  1. Round-trip (serialize_roundtrip): setVch(serialize(n))=n\mathrm{setVch}(\mathrm{serialize}(n)) = nsetVch(serialize(n))=n for every integer.
  2. Minimality (serialize_minimal): the serializer's output always passes the minimal-encoding test; (toNum_serialize): a serialized number of at most four bytes is accepted by the strict constructor and reads back as itself.
  3. Truth is nonzero (castToBool_iff): castToBool(v)  ⟺  setVch(v)≠0\mathrm{castToBool}(v) \iff \mathrm{setVch}(v) \ne 0castToBool(v)⟺setVch(v)=0, for every byte string, including both encodings of zero.
  4. Pushes (eval_pushes): a push-only script within the element and stack limits leaves exactly its pushes, top-first.
  5. Disabled opcodes (disabled_fails): a script containing any of the fifteen disabled opcodes fails from every state, executed or not.
  6. Operation limit (op_count_fails): a script with more than 201 counted opcodes (non-push opcodes above OP_16) never succeeds.
  7. Pay-to-public-key (p2pk_correct): ⟨sig⟩\langle sig\rangle⟨sig⟩ against ⟨pk⟩ CHECKSIG\langle pk\rangle\ \mathtt{CHECKSIG}⟨pk⟩ CHECKSIG succeeds iff the oracle accepts the signature.

What the result establishes

The goal and milestones give kernel-checked semantics for the pieces of the legacy interpreter that every later layer consumes. The number theorems fix what CScriptNum means on the wire and show the interpreter's own outputs are always minimal — which is why MINIMALDATA can be a policy rule. The truth theorem is what makes VERIFY, IF and the final check well-defined on arbitrary byte strings. The limit and disabled-opcode theorems are the denial-of-service and CVE-2010-5137 defences, stated as universal properties of the transcription rather than of test cases. And the template theorems are the first end-to-end statements that a real Bitcoin spending condition is enforced by the code, modulo the cryptography.

What is deliberately abstract: hash functions, the signature check with its DER/strict-encoding rules and FindAndDelete, and the lock-time checks. These are oracle fields, so every theorem holds for every oracle; instantiating them is the work of the hash, sighash and signature layers. Not modeled: byte-level script parsing (GetOp, MAX_SCRIPT_SIZE), pay-to-script-hash and witness programs in VerifyScript, SegWit v0 and Tapscript execution, and the policy flags (MINIMALDATA, NULLFAIL, CONST_SCRIPTCODE, DISCOURAGE_UPGRADABLE_NOPS, MINIMALIF), which are fixed to off as in block validation.

Status: all statements are open with intentional placeholder proofs. The opcode table has been checked by evaluation against the source's documented stack effects (x1 x2 x3 -- x2 x3 x1 and the like) and the CVE-2010-5137 and limit behaviours; nothing about compiled Bitcoin Core is claimed.

Where the obvious argument fails

The number round-trip looks like bookkeeping until the sign byte: when the magnitude's top byte already has bit 7 set, the serializer appends a whole byte for the sign, and the reader must then see a cleared top bit on that extra byte while the magnitude is unchanged. The proof splits on this case and on the sign, and needs the precise effect of |= 0x80 and & 0x7f on bytes below 128. Minimality has the matching trap: the serializer never emits a trailing zero magnitude byte, which is what makes its output pass the test.

For the templates, the naive approach is to compute. It works, but only after the interpreter's resource checks are discharged on every step — the element size on each push, the op count on each opcode, the stack size after each step — which is why the template theorems carry element-size hypotheses and why the push lemma is stated with its limits. For the op-count theorem the trap is CHECKMULTISIG, which adds its key count to nOpCount mid-execution; the invariant has to allow that increase while keeping it under the limit.

Formalization scope

Bytes are BitVec 8; numbers are Int with natAbs for the magnitude; bit tests are the source's masks (& 0x80, & 0x7f, | 0x80) read through toNat. Stacks are Lists, top first; the stacktop(-k) indexing of the source becomes position k−1k-1k−1. Scripts are List Instr. execOp is one if-chain mirroring the switch; step performs the per-iteration checks in the source's order; run is the loop plus the final conditional check; evalScript returns the main stack. Flags carries CHECKLOCKTIMEVERIFY, CHECKSEQUENCEVERIFY and NULLDUMMY; fRequireMinimal is false. The oracle's checkSig returns none where an encoding check fails the whole script and some b for the signature verdict.

A trivializing formalization would define verifyScriptPlain on the two templates directly, or let the oracle see the script; the interpreter is the full opcode table and the oracle sees only the stack elements it is handed. Restricting the templates to canonical sizes beyond the 520-byte limit is out of scope.

No Mathlib is required; the development imports only Std. Proofs in any order are welcome; lemmas about individual opcodes' stack effects are reusable for every later script theorem.

Selected references

  • Bitcoin Core contributors. EvalScript, VerifyScript, CastToBool, EvalChecksigPreTapscript, src/script/interpreter.cpp at revision a773e1e33b349dc819b5a0edc1c7a4839dc73b48. Source
  • Bitcoin Core contributors. CScriptNum, opcode table, MAX_SCRIPT_ELEMENT_SIZE, MAX_OPS_PER_SCRIPT, MAX_STACK_SIZE, MAX_PUBKEYS_PER_MULTISIG, src/script/script.h, and GetScriptOp, src/script/script.cpp, same revision. Source
  • Pieter Wuille. BIP62: Dealing with malleability (rules 3–6, minimal data and numbers), 2014. Specification
  • Satoshi Nakamoto. Bitcoin: A Peer-to-Peer Electronic Cash System, 2008, section 2. Paper
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Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin Block Headers: Median Time Past and the Contextual Header RulesTextbook

Why the header clock matters

Bitcoin has no trusted clock. A block's timestamp is chosen by its miner, so the consensus rules constrain it from two sides: it must exceed the median time past of the previous eleven blocks (time-too-old), and it must not run more than two hours ahead of the validating node's own clock (time-too-new). The median, rather than the previous block's time, is what makes the rule robust to a few manipulated timestamps, and since BIP113 the same median is the clock against which time-based lock-times are evaluated. The remaining contextual header rules fix the required difficulty bits and refuse block versions that predate the BIP34, BIP66 and BIP65 soft forks once those are active. (GetMedianTimePast, ContextualCheckBlockHeader, CMainParams)

One property of this design is used everywhere but stated nowhere in the code: along any chain that obeys time-too-old, the median time past never decreases. It is what makes a time-based lock, once satisfied, stay satisfied. This mission transcribes the median and the contextual rules and proves that property.

The median, the window, and the rules

A chain of ancestors is represented by the list of its block times, most recent first. The window is the first eleven entries (nMedianTimeSpan), or all of them for a shorter chain. The median time past sorts the window and takes the element at index ⌊∣w∣/2⌋\lfloor |w| / 2 \rfloor⌊∣w∣/2⌋; for an even-length window this is the upper of the two middle elements, as the source's pbegin[(pend - pbegin) / 2]. For an empty chain the model returns 000, a case the source never queries.

A header carries a version, a time and nBits. For a buried deployment, DeploymentActiveAfter(prev) holds when prev.height+1\mathrm{prev.height} + 1prev.height+1 reaches the activation height: 227,931227{,}931227,931 for BIP34, 363,725363{,}725363,725 for BIP66, 388,381388{,}381388,381 for BIP65. The version floor rejects a version below 2, 3 or 4 when the corresponding deployment is active. The contextual check of a header against its predecessor requires: nBits equals the required work (supplied by the caller, as GetNextWorkRequired computes it in the proof-of-work development); the time strictly exceeds the predecessor's median time past; the time is at most the local clock plus 7,2007{,}2007,200 seconds; and the version is not outdated. Mainnet has enforce_BIP94 = false, so the testnet timewarp rule is absent.

Formalization targets

Goal — median time past is non-decreasing along a valid chain

For a chain of block times t0,t1,…t_0, t_1, \dotst0​,t1​,… (most recent first) in which every block satisfies time-too-old against its predecessor — MTP(ti+1,ti+2,… )<ti\mathrm{MTP}(t_{i+1}, t_{i+2}, \dots) < t_iMTP(ti+1​,ti+2​,…)<ti​ for every iii — the median time past of every suffix is at most that of every shorter suffix:

i≤j  ⟹  MTP(dropj)≤MTP(dropi).i \le j \;\Longrightarrow\; \mathrm{MTP}(\text{drop}_j) \le \mathrm{MTP}(\text{drop}_i).i≤j⟹MTP(dropj​)≤MTP(dropi​).

This is BitcoinHeader.mtp_mono_chain. It is the clock-monotonicity that BIP113 and BIP68 time locks rely on.

Supporting milestones

  1. Window membership (mtp_mem_window, mtp_mem): for a non-empty chain the median is one of the window's times, hence one of the chain's.
  2. Bounds (mtp_bounds): the median lies between any lower and upper bound of the window.
  3. Only eleven matter (mtp_take): the median of a chain equals the median of its first eleven times.
  4. One step (mtp_mono): if MTP(c)<t\mathrm{MTP}(c) < tMTP(c)<t then MTP(c)≤MTP(t::c)\mathrm{MTP}(c) \le \mathrm{MTP}(t :: c)MTP(c)≤MTP(t::c). The heart of the goal, by a counting argument on the sorted window.
  5. Version floor (versionOutdated_iff): the rejection condition as a disjunction of height thresholds.
  6. The contextual check (contextualCheck_iff): acceptance as a conjunction of the four rules.
  7. Floors after activation (version_floor): an accepted header has version at least 2, 3, 4 after the respective heights.

What the result establishes

The goal is a kernel-checked statement about the transcribed median: appending blocks that pass time-too-old can only move the median forward. Together with the characterization of the contextual check it gives the exact semantics of Bitcoin's block clock: bounded from below by a non-decreasing median, from above by local time plus two hours.

Formalizing adds the general proof of a fact usually argued by example. The argument is a counting one: the median of a window of nnn sorted times is the smallest value with at least n−⌊n/2⌋n - \lfloor n/2 \rfloorn−⌊n/2⌋ window elements at or above it; appending a time above the old median and dropping at most one old element keeps that count high enough, including when the window is still growing in the first ten blocks of a chain.

Not in scope, and stated as such: the computation of the required nBits (the proof-of-work mission), the local-clock model behind NodeClock::now(), checkpoints, minimum chain work, and the CheckBlockHeader proof-of-work test itself. Status: all statements are open with intentional placeholder proofs.

Where the obvious argument fails

The naive argument — "the new time is larger than the median, so the median cannot fall" — overlooks that the window also loses its oldest element, which may have been one of the elements above the median. The proof must show that losing one such element and gaining one keeps at least n−⌊n/2⌋n - \lfloor n/2 \rfloorn−⌊n/2⌋ elements at or above the old median. A second subtlety is the growing window: for the first ten blocks the window length increases by one and the median index may move, so the count required changes from n−⌊n/2⌋n - \lfloor n/2 \rfloorn−⌊n/2⌋ to (n+1)−⌊(n+1)/2⌋(n+1) - \lfloor (n+1)/2 \rfloor(n+1)−⌊(n+1)/2⌋; the two cases are not the same inequality. The even-length convention (upper middle) is what makes the bookkeeping close.

Formalization scope

Times are Int, as int64_t block times. The window is List.take 11, the sort is List.mergeSort on ≤, and the median is getD (length / 2) 0. The required nBits is a parameter of the check. Heights are Nat. enforce_BIP94, checkpoints and minimum chain work are not modeled.

A trivializing formalization would define the median as the previous block's time, or sort with a different index convention; the definitions follow the source's sort-and-index. Assuming the chain's times are themselves sorted is ruled out: the goal assumes only the time-too-old rule.

No Mathlib is required; the development imports only Std. The counting lemmas on sorted lists are reusable for any later median-based rule.

Selected references

  • Bitcoin Core contributors. CBlockIndex::GetMedianTimePast, src/chain.h at revision a773e1e33b349dc819b5a0edc1c7a4839dc73b48. Source
  • Bitcoin Core contributors. ContextualCheckBlockHeader, src/validation.cpp, same revision. Source
  • Bitcoin Core contributors. DeploymentActiveAfter, src/deploymentstatus.h, and CMainParams activation heights, src/kernel/chainparams.cpp, same revision. Source
  • Thomas Kerin, Mark Friedenbach. BIP113: Median time-past as endpoint for lock-time calculations, 2015. Specification
  • Gavin Andresen. BIP34: Block v2, Height in Coinbase, 2012; Pieter Wuille. BIP66: Strict DER signatures, 2015; Peter Todd. BIP65: OP_CHECKLOCKTIMEVERIFY, 2014. BIPs
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Formal VerificationTheoretical Computer Science·Captain: pitimespi

BIP68 Relative Lock-Time: Bitcoin Core Sequence Locks Agree with the SpecificationTextbook

Why relative lock-time matters

Bitcoin nodes must agree on whether a transaction may spend its inputs yet. Since 2016 that decision includes relative lock-time: an input may declare that the output it spends must have been confirmed for a minimum number of blocks or a minimum elapsed time before it becomes spendable. The rule is defined by BIP68 and enforced on mainnet by three helper functions in Bitcoin Core: CalculateSequenceLocks, EvaluateSequenceLocks, and SequenceLocks (pinned source). Payment channels, atomic swaps, and vault constructions depend on this rule to enforce dispute windows; a node that decodes a sequence word differently from its peers can accept or reject a block the rest of the network treats the other way.

The specification is short and the implementation is about seventy lines, yet the two are written in different languages. BIP68 speaks of bits, masks, and elapsed delays; the implementation folds inputs into two signed thresholds compared against the candidate height and its ancestor's median time. Whether these agree, for every 32-bit sequence word and every input list, is the question this mission settles. Absolute lock-time (nLockTime) already has a Lean treatment in btc-verified, whose consensus documentation lists BIP68 as outstanding work.

Sequence words, inputs, and contexts

An input is a pair (s,c)(s, c)(s,c): an unsigned 32-bit sequence word sss and the coin height c∈Nc \in \mathbb Nc∈N of the output being spent. A context is a pair (H,M)(H, M)(H,M): the candidate block height H∈NH \in \mathbb NH∈N and a total function M:N→NM : \mathbb N \to \mathbb NM:N→N giving the median time past of each ancestor height. Write h−=max⁡(h−1,0)h^- = \max(h-1, 0)h−=max(h−1,0); natural-number subtraction in Lean truncates at zero and realizes this directly.

Three fields of sss matter. The disable bit DDD is bit 31. The type bit TTT is bit 22: set means a time-based lock, clear means a height-based lock. The delay d=s mod 65536d = s \bmod 65536d=smod65536 is the low sixteen bits; for a time lock it denotes 512d512d512d seconds. Enforcement EEE holds when the transaction version is at least 2 and bit 0 of the validation flags is set.

The declarative model (BIP68_Model) expresses these as arithmetic on sss viewed as a natural number: D  ⟺  ⌊s/231⌋=1D \iff \lfloor s / 2^{31} \rfloor = 1D⟺⌊s/231⌋=1, T  ⟺  ⌊s/222⌋ mod 2=1T \iff \lfloor s / 2^{22} \rfloor \bmod 2 = 1T⟺⌊s/222⌋mod2=1. A single input is satisfied when

D ∨ {M(c−)+512d≤M(H−),T,c+d≤H,¬T.D \ \lor\ \begin{cases} M(c^-) + 512d \le M(H^-), & T,\\ c + d \le H, & \neg T.\end{cases}D ∨ {M(c−)+512d≤M(H−),c+d≤H,​T,¬T.​

A transaction satisfies BIP68 when ¬E\neg E¬E or every input is satisfied. The model also fixes the expected height list: each input's coin height, replaced by 000 exactly when EEE holds and DDD is set.

The Core transcription (BIP68_Core) uses bit-vector masks 0x80000000, 0x00400000, 0x0000ffff and a left shift by 9. An accumulator holds two signed thresholds (a,b)(a, b)(a,b) and a reversed height list, starting at (−1,−1,[ ])(-1, -1, [\,])(−1,−1,[]). Each enabled, non-disabled input updates b←max⁡(b, M(c−)+512d−1)b \leftarrow \max(b,\ M(c^-) + 512d - 1)b←max(b, M(c−)+512d−1) for a time lock or a←max⁡(a, c+d−1)a \leftarrow \max(a,\ c + d - 1)a←max(a, c+d−1) for a height lock. Evaluation succeeds when a<Ha < Ha<H and b<M(H−)b < M(H^-)b<M(H−). When EEE is false, the fold is skipped and the original heights are returned.

Formalization targets

Goal — helper correctness

Under the admissible domain (0<H≤231−10 < H \le 2^{31}-10<H≤231−1, every c≤Hc \le Hc≤H, every M(h)≤263−1M(h) \le 2^{63}-1M(h)≤263−1 for h≤Hh \le Hh≤H) and the no-overflow condition (for every enforced, non-disabled input, the sum selected by TTT fits its signed destination before the subtraction of one),

sequenceLocks(v,f,H,M,I)=true  ⟺  satisfiesBIP68(v,f,H,M,I),\mathrm{sequenceLocks}(v, f, H, M, I) = \mathrm{true} \iff \mathrm{satisfiesBIP68}(v, f, H, M, I),sequenceLocks(v,f,H,M,I)=true⟺satisfiesBIP68(v,f,H,M,I),

and the height list returned by the fold equals the expected height list. This is BitcoinBIP68.core_correct.

Supporting milestones

  1. Decoding (decode_sequence): for every 32-bit word, the mask tests agree with the quotient-and-remainder tests, the masked delay equals ddd, and the shifted seconds equal 512d512d512d. No hypothesis on unused bits.
  2. Fold acceptance (fold_acceptance): for any starting accumulator, evaluating the fold succeeds iff evaluating the starting accumulator succeeds and every input is satisfied. No range or monotonicity assumptions.
  3. Height vector (height_vector): the returned list has the original length and order, with zeroes exactly at enforced, disabled inputs, including repeated entries and the empty list.
  4. Threshold bounds (calculated_bounds): under admissibility and no-overflow, −1≤a≤231−1-1 \le a \le 2^{31}-1−1≤a≤231−1 and −1≤b≤263−1-1 \le b \le 2^{63}-1−1≤b≤263−1, preserving the −1-1−1 sentinel.

What the result establishes

The goal is a kernel-checked statement that a Lean transcription of the pinned Core helpers decides exactly the BIP68 spending condition on the admissible domain, and that its side effect on the per-input height list is the one BIP68 describes. Both directions matter: a lock the implementation accepts is one the specification accepts, and a lock the specification accepts is one the implementation accepts.

Formalizing it adds three things beyond the prose specification. It makes the overflow preconditions explicit, which BIP68 leaves implicit and Core leaves to callers. It separates the bit-level decoding (a bv_decide-style fact) from the aggregation logic (an induction over the input list), so each can be audited on its own. And it produces reusable definitions — Input, Context, the admissibility and overflow predicates — for a later whole-block validation development in which the caller obligations (supplying coin heights, ancestor clocks, activation flags, and coinbase exclusion) are discharged.

Status: the statements compile with intentional sorry placeholders and have independent read-backs. Differential checks ran 2,014 cases against extracted Core helper bodies; these test the transcription, not the theorems. No proof of any statement is claimed.

Where the obvious argument fails

The natural first idea is to prove core_correct by unfolding everything and calling a decision procedure. It fails because the two sides live in different arithmetic. The implementation computes thresholds in signed integers after subtracting one; the specification compares unsigned sums with ≤\le≤. The equivalence a<H  ⟺  max⁡i(ci+di−1)<Ha < H \iff \max_i (c_i + d_i - 1) < Ha<H⟺maxi​(ci​+di​−1)<H holds only because the −1-1−1 sentinel is below every admissible height and because each ci+di−1c_i + d_i - 1ci​+di​−1 is formed without wrap-around. Both facts need the admissibility and no-overflow hypotheses, and the proof must thread them through the fold.

A second trap is the disabled input. In the model, a disabled input is satisfied unconditionally; in the fold, it leaves both thresholds unchanged but still appends 000 to the height list only when enforcement is on. height_vector records this asymmetry; a proof that treats disabled inputs as absent gets the height list wrong.

A third is the median-time function. MMM is arbitrary: not monotone, not bounded outside [0,H][0, H][0,H]. The theorems are stated for this weaker model deliberately, so any proof that assumes a well-formed chain is proving a different statement.

Formalization scope

The development commits to the following conventions, all fixed in BIP68_Model and BIP68_Core. Sequence words, versions, and flags are BitVec 32. Heights and times are Nat in the model and Int in the accumulator. h−1h - 1h−1 on Nat truncates at zero, matching max(h-1, 0) in the source. Input lists are finite Lists; the empty list is in scope. The predicates specEnabled, specDisabled, and specTimeBased are Prop with decidability instances; the Core-side tests are Bool. Admissibility and no-overflow are the only hypotheses on core_correct; fold_acceptance and height_vector carry none.

A trivializing formalization would define satisfiesBIP68 by calling sequenceLocks; the model is written independently from the BIP text and must stay that way. Weakening the no-overflow hypothesis to a vacuous one, or restricting to nonempty lists, is out of scope.

Dependencies: Lean 4 core and Std only; no Mathlib. The two definitions are published first and imported by every theorem. Proofs of the four milestones in any order are welcome, as are lemmas on coreStep reusable by the goal. Out of scope: the C++-to-Lean correspondence (documented, not proved), coinbase exclusion, activation selection, and block-level composition, which belong to a separate whole-consensus mission that will import these results.

Selected references

  • Mark Friedenbach, BtcDrak, Nicolas Dorier, kinoshitajona. BIP68: Relative lock-time using consensus-enforced sequence numbers, Bitcoin Improvement Proposals, 2015. Specification
  • Bitcoin Core contributors. CalculateSequenceLocks, EvaluateSequenceLocks, SequenceLocks, src/consensus/tx_verify.cpp at revision a773e1e33b349dc819b5a0edc1c7a4839dc73b48. Source
  • Bitcoin Core contributors. Mainnet validation and parameters, same revision. validation.cpp, chainparams.cpp
  • ProofOfKeags contributors. btc-verified: absolute lock-time in Lean 4, revision 2fdeb40c. LockTime.lean, consensus coverage
  • Lean FRO. Bitvectors, Lean Language Reference. Documentation
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Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin Merkle Root: Odd-Level Duplication and the CVE-2012-2459 Mutation CheckTextbook

Why the Merkle root and its mutation check matter

A Bitcoin block header commits to its transactions through a single 32-byte Merkle root: the transaction hashes are paired and hashed, the results paired and hashed again, until one hash remains. Every node recomputes this root from the block body and rejects the block if it does not match the header. Bitcoin's construction has a quirk that later became a vulnerability: when a level has an odd number of hashes, the last one is duplicated before pairing. Consequently the transaction lists [1,2,3,4,5,6][1,2,3,4,5,6][1,2,3,4,5,6] and [1,2,3,4,5,6,5,6][1,2,3,4,5,6,5,6][1,2,3,4,5,6,5,6] have the same root, and an attacker could relay the second, invalid, block with the first block's header; a node that marked that header permanently invalid would then reject the genuine block (CVE-2012-2459). Bitcoin Core defends by detecting, at every level, two equal adjacent hashes in an aligned pair and treating the block as having a bad root. (ComputeMerkleRoot and the comment above it.)

This mission transcribes that loop and proves that the defence does what its comment claims for the general form of the construction: duplicating any aligned block of 2k2^k2k trailing leaves preserves the root and is detected, at the level where that block has collapsed to one node.

The loop, as a function on lists

Hashes are an arbitrary type HHH with decidable equality; the pairwise hash is an abstract c:H×H→Hc : H \times H \to Hc:H×H→H (in the source, SHA256D64 on the two concatenated hashes), and the zero hash returned for an empty list is an abstract zzz. The three operations are, for a list of hashes ℓ\ellℓ:

  • pairUp(ℓ)(\ell)(ℓ): ccc applied to successive aligned pairs; an odd trailing element aaa contributes c(a,a)c(a, a)c(a,a). This is hashes.push_back(hashes.back()) followed by the pairwise hashing.
  • hasEqualPair(ℓ)(\ell)(ℓ): true when some aligned pair (ℓ2i,ℓ2i+1)(\ell_{2i}, \ell_{2i+1})(ℓ2i​,ℓ2i+1​) with 2i+1<∣ℓ∣2i + 1 < |\ell|2i+1<∣ℓ∣ consists of equal hashes. The duplicated trailing element is not tested: the check runs before the duplication.
  • rootLoop(ℓ,m)(\ell, m)(ℓ,m): while ∣ℓ∣>1|\ell| > 1∣ℓ∣>1, replace mmm by m∨hasEqualPair(ℓ)m \lor \mathrm{hasEqualPair}(\ell)m∨hasEqualPair(ℓ) and ℓ\ellℓ by pairUp(ℓ)\mathrm{pairUp}(\ell)pairUp(ℓ); on exit return the single remaining hash (or zzz if the list is empty) together with mmm.

computeMerkleRoot(ℓ)=rootLoop(ℓ,false)(\ell) = \mathrm{rootLoop}(\ell, \mathtt{false})(ℓ)=rootLoop(ℓ,false) returns the pair (root, mutated), exactly as ComputeMerkleRoot(hashes, &mutated).

Formalization targets

Goal — duplication of an aligned block is root-preserving and detected

For every kkk, every non-empty prefix ℓ\ellℓ whose length is a multiple of 2k+12^{k+1}2k+1, and every block ttt of exactly 2k2^k2k hashes,

root(ℓ+ ⁣ ⁣+t)=root(ℓ+ ⁣ ⁣+t+ ⁣ ⁣+t)andmutated(ℓ+ ⁣ ⁣+t+ ⁣ ⁣+t)=true.\mathrm{root}(\ell \mathbin{+\!\!+} t) = \mathrm{root}(\ell \mathbin{+\!\!+} t \mathbin{+\!\!+} t) \qquad\text{and}\qquad \mathrm{mutated}(\ell \mathbin{+\!\!+} t \mathbin{+\!\!+} t) = \mathtt{true}.root(ℓ++t)=root(ℓ++t++t)andmutated(ℓ++t++t)=true.

This is BitcoinMerkle.dup_block. The alignment hypotheses are what make ttt collapse to a single node at level kkk, where the duplicate then sits in an aligned pair; ℓ≠[ ]\ell \ne [\,]ℓ=[] is needed because a list of one hash is its own root while a list of two is not. With k=0k = 0k=0 it is the classic duplicated-last-transaction case; with k=1k = 1k=1 it is the [1,…,6,5,6][1,\dots,6,5,6][1,…,6,5,6] example of the source comment.

Supporting milestones

  1. Base cases (root_nil, root_single, root_pair): the empty list gives (z,false)(z, \mathtt{false})(z,false); one hash gives (a,false)(a, \mathtt{false})(a,false); two give (c(a,b), a=b)(c(a,b),\ a = b)(c(a,b), a=b).
  2. Flag independence (rootLoop_fst): the root does not depend on the incoming flag.
  3. Flag is sticky (rootLoop_sticky): once set, the flag stays set through the remaining levels.
  4. One level distributes over an even split (pairUp_append_even, hasEqualPair_append_even): for ∣ℓ∣|\ell|∣ℓ∣ even, pairUp(ℓ+ ⁣ ⁣+r)=pairUp(ℓ)+ ⁣ ⁣+pairUp(r)\mathrm{pairUp}(\ell \mathbin{+\!\!+} r) = \mathrm{pairUp}(\ell) \mathbin{+\!\!+} \mathrm{pairUp}(r)pairUp(ℓ++r)=pairUp(ℓ)++pairUp(r) and likewise for the pair test.
  5. Leaf-level case (dup_leaf): the goal at k=0k = 0k=0, stated directly.

What the result establishes

The goal is a kernel-checked statement about the transcribed loop: for the entire family of duplications that CVE-2012-2459 is built on, the root is unchanged and the mutation flag is raised. Together with the base cases it fixes the exact semantics of the odd-level rule and of the check, including the fact that the self-pairing of a duplicated trailing element is never itself flagged.

What the result does not say is also worth stating. It does not claim that every root collision is detected; that would require assumptions on the hash function (the source comment says "assuming no double-SHA256 collisions"), and ccc is here an arbitrary function. Nor does it claim anything about the byte-level SHA256D64 or about transaction serialization; those belong to the hash and serialization layer. What formalizing adds is the general kkk statement with its precise alignment conditions, which the source states only by example.

Status: all statements are open with intentional placeholder proofs. Nothing about compiled Bitcoin Core is claimed.

Where the obvious argument fails

The natural first attempt is to compute both sides for small lists and generalize. It fails on the kkk-level statement because the two lists differ in length by 2k2^k2k and go through kkk levels before the duplicate appears as a pair. The argument has to track two facts through the loop at once: that the root is insensitive to the flag (so the two runs can be compared level by level with different flags), and that the flag, once raised at level kkk, survives to the end. Both are separate milestones for that reason.

The alignment hypotheses are the second trap. Without 2k+1∣∣ℓ∣2^{k+1} \mid |\ell|2k+1∣∣ℓ∣ the block ttt straddles a pair boundary and its two copies do not collapse to equal nodes; without ∣t∣=2k|t| = 2^k∣t∣=2k the copies collapse at different levels. The theorem is false without them, and the base case ℓ=[ ]\ell = [\,]ℓ=[] shows that non-emptiness is needed too.

Formalization scope

HHH is a type parameter with DecidableEq; ccc and zzz are parameters. Lists are Lean Lists, so pairUp and hasEqualPair are structurally recursive, and rootLoop is defined by well-founded recursion on the list length with the lemma ∣pairUp(ℓ)∣=⌈∣ℓ∣/2⌉|\mathrm{pairUp}(\ell)| = \lceil |\ell| / 2 \rceil∣pairUp(ℓ)∣=⌈∣ℓ∣/2⌉ supplied in the definition file to justify termination. Equality in hasEqualPair is decide (a = b) on HHH, matching hashes[pos] == hashes[pos + 1] on uint256. The mutated pointer being null in the source (as in BlockWitnessMerkleRoot) only suppresses the output; the model always computes the flag.

A trivializing formalization would restrict HHH to a type with a trivial hash or define the root by a different, non-duplicating rule; the definitions are fixed to the source's loop. Out of scope: MerkleComputation (Merkle branches), BlockMerkleRoot's leaf extraction, the witness root's zero coinbase leaf, and any collision-resistance assumption on ccc.

No Mathlib is required; the development imports only Std. Proofs of milestones in any order are welcome, and the distribution lemmas are reusable by any later Merkle-branch work.

Selected references

  • Bitcoin Core contributors. ComputeMerkleRoot, src/consensus/merkle.cpp at revision a773e1e33b349dc819b5a0edc1c7a4839dc73b48, lines 16–73 including the CVE-2012-2459 comment. Source
  • Forrest Voight. CVE-2012-2459: Bitcoin block Merkle root duplication, 2012. Advisory
  • Satoshi Nakamoto. Bitcoin: A Peer-to-Peer Electronic Cash System, 2008, section 7. Paper
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Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin Block Subsidy and the 21 Million Issuance BoundTextbook

Why the issuance schedule matters

Every Bitcoin block may create new coins through its coinbase transaction, and the amount it may create is fixed by consensus: a block subsidy that starts at fifty bitcoin and halves every 210,000 blocks, plus the fees of the block's other transactions. The schedule is the mechanism behind the often-quoted cap of twenty-one million bitcoin, yet the cap appears nowhere in the code as a rule. What the code enforces is the per-block subsidy, computed by GetBlockSubsidy in validation.cpp, and the check in ConnectBlock that the coinbase pays no more than fees plus subsidy. The cap is a consequence, and the only place a number near it appears is MAX_MONEY, a sanity bound on individual amounts whose comment says explicitly that it is "not the total money supply". (GetBlockSubsidy, amount.h, CMainParams)

This mission makes the consequence a theorem about the pinned implementation: the sum of the subsidies of all blocks, at every height the chain can reach, is strictly below MAX_MONEY, and in fact equals a specific constant once the subsidy reaches zero. It is the arithmetic core of the ledger-supply results that a whole-consensus development will need, and it is small enough to be verified completely.

The subsidy function and its schedule

Amounts are counted in satoshi. One bitcoin is COIN=108\mathrm{COIN} = 10^8COIN=108 satoshi and MAX_MONEY=21,000,000⋅COIN\mathrm{MAX\_MONEY} = 21{,}000{,}000 \cdot \mathrm{COIN}MAX_MONEY=21,000,000⋅COIN. The mainnet halving interval is I=210,000I = 210{,}000I=210,000 blocks.

The Core subsidy at height h∈Nh \in \mathbb Nh∈N is the transcription of GetBlockSubsidy: with k=⌊h/I⌋k = \lfloor h / I \rfloork=⌊h/I⌋ the number of halvings, the subsidy is 000 if k≥64k \ge 64k≥64, and otherwise (50⋅COIN)≫k(50 \cdot \mathrm{COIN}) \gg k(50⋅COIN)≫k, the right shift of the initial subsidy by kkk bits. Heights are nonnegative integers in the source, and the shift acts on the nonnegative value 50⋅COIN50 \cdot \mathrm{COIN}50⋅COIN, so natural numbers represent both exactly. The explicit halvings >= 64 branch exists because a shift by 64 or more is undefined in C++.

The declarative schedule is specSubsidy(h)=⌊50⋅COIN / 2⌊h/I⌋⌋\mathrm{specSubsidy}(h) = \lfloor 50 \cdot \mathrm{COIN} \,/\, 2^{\lfloor h/I \rfloor} \rfloorspecSubsidy(h)=⌊50⋅COIN/2⌊h/I⌋⌋: fifty bitcoin, halved once per interval with the remainder discarded.

Issuance below height nnn is the running sum of Core subsidies, issuedBelow(0)=0\mathrm{issuedBelow}(0) = 0issuedBelow(0)=0 and issuedBelow(n+1)=issuedBelow(n)+coreSubsidy(n)\mathrm{issuedBelow}(n+1) = \mathrm{issuedBelow}(n) + \mathrm{coreSubsidy}(n)issuedBelow(n+1)=issuedBelow(n)+coreSubsidy(n). The interval sum intervalSum(m)=∑j<m⌊50⋅COIN/2j⌋\mathrm{intervalSum}(m) = \sum_{j < m} \lfloor 50 \cdot \mathrm{COIN} / 2^j \rfloorintervalSum(m)=∑j<m​⌊50⋅COIN/2j⌋ collects one subsidy per interval. The zero height is 33I=6,930,00033 I = 6{,}930{,}00033I=6,930,000.

Formalization targets

Goal — issuance below the cap

∀n,issuedBelow(n)<MAX_MONEY.\forall n,\quad \mathrm{issuedBelow}(n) < \mathrm{MAX\_MONEY}.∀n,issuedBelow(n)<MAX_MONEY.

This is the weakest stable statement: it survives any refinement of the constants below and is what a supply argument downstream consumes. It is BitcoinSubsidy.issuance_bound.

Supporting milestones

  1. Core equals schedule (core_eq_spec): coreSubsidy(h)=specSubsidy(h)\mathrm{coreSubsidy}(h) = \mathrm{specSubsidy}(h)coreSubsidy(h)=specSubsidy(h) for every hhh, including heights where the 64 branch fires.
  2. Amount range (subsidy_le_maxMoney): coreSubsidy(h)≤MAX_MONEY\mathrm{coreSubsidy}(h) \le \mathrm{MAX\_MONEY}coreSubsidy(h)≤MAX_MONEY, so the subsidy is always a valid amount.
  3. Monotone decrease (subsidy_antitone): h1≤h2  ⟹  coreSubsidy(h2)≤coreSubsidy(h1)h_1 \le h_2 \implies \mathrm{coreSubsidy}(h_2) \le \mathrm{coreSubsidy}(h_1)h1​≤h2​⟹coreSubsidy(h2​)≤coreSubsidy(h1​).
  4. Halving step (subsidy_halving): coreSubsidy(h+I)=⌊coreSubsidy(h)/2⌋\mathrm{coreSubsidy}(h + I) = \lfloor \mathrm{coreSubsidy}(h) / 2 \rfloorcoreSubsidy(h+I)=⌊coreSubsidy(h)/2⌋.
  5. Exact extinction (subsidy_zero_iff): coreSubsidy(h)=0  ⟺  h≥33I\mathrm{coreSubsidy}(h) = 0 \iff h \ge 33 IcoreSubsidy(h)=0⟺h≥33I. The subsidy is one satoshi throughout the thirty-third interval and zero from the thirty-fourth on.
  6. Exact total (issuance_total): issuedBelow(33I)=2,099,999,997,690,000\mathrm{issuedBelow}(33 I) = 2{,}099{,}999{,}997{,}690{,}000issuedBelow(33I)=2,099,999,997,690,000 satoshi.
  7. Bounded by the total (issuance_le_total): issuedBelow(n)≤2,099,999,997,690,000\mathrm{issuedBelow}(n) \le 2{,}099{,}999{,}997{,}690{,}000issuedBelow(n)≤2,099,999,997,690,000 for every nnn.

What the result establishes

The goal is a kernel-checked statement that the implemented subsidy schedule can never issue MAX_MONEY or more in total, for any chain length, and the milestones pin down the exact figure: 20,999,999.976920{,}999{,}999.976920,999,999.9769 bitcoin, 2,310,0002{,}310{,}0002,310,000 satoshi short of twenty-one million because of the floor in each halving. Milestones 1 and 5 record two facts about the implementation worth having on the record: the halvings >= 64 guard is never the reason the subsidy is zero (division already gives zero from 33 halvings), and the subsidy is exactly one satoshi for the 210,000 blocks of the thirty-third interval.

What formalizing adds is separation of the arithmetic from the accounting. The supply bound on an actual ledger needs more than this: fees move value without creating it, unclaimed coinbase value is never issued, and outputs can be made unspendable. Those are statements about the UTXO transition and belong to a separate mission; this one supplies the exact upper bound they will cite.

Status: every statement is open with an intentional placeholder proof. The constants and the shift are checked against the pinned source; nothing about compiled Bitcoin Core is claimed.

Where the obvious argument fails

The naive proof of the goal is to evaluate the sum: unfold issuedBelow at 6,930,0006{,}930{,}0006,930,000 and compute. The kernel cannot do that in reasonable time, and the statement is for every nnn, not one. The argument has to be structural: within an interval the subsidy is constant, so the sum over the first mmm intervals is I⋅intervalSum(m)I \cdot \mathrm{intervalSum}(m)I⋅intervalSum(m); past the zero height every term vanishes, so the sum is stable; and for nnn below the zero height the sum is bounded by monotonicity. Only the thirty-three-term interval sum is evaluated by computation.

A second trap is the shift. (50⋅COIN)≫k(50 \cdot \mathrm{COIN}) \gg k(50⋅COIN)≫k equals ⌊50⋅COIN/2k⌋\lfloor 50 \cdot \mathrm{COIN} / 2^k \rfloor⌊50⋅COIN/2k⌋ only because the value is nonnegative; the schedule theorem is what licenses replacing the bitwise operation with division everywhere else.

Formalization scope

Heights, amounts and counts are Nat. coreSubsidy uses Lean's >>> on Nat, which is exact division by 2k2^k2k. issuedBelow and intervalSum are structurally recursive, so proofs about them are inductions on the height or the interval count. No Mathlib is required; the development imports only Std.

A trivializing formalization would define issuedBelow through specSubsidy and prove the bound about the schedule alone; the definition is fixed on coreSubsidy so that the result is about the transcription. Weakening the goal to a bound at one fixed height is out of scope.

Out of scope: the coinbase check GetValueOut() > blockReward in ConnectBlock, fee accounting, and the UTXO set. Those take this mission's definitions as inputs. Proofs of the milestones in any order are welcome, as are reusable lemmas on issuedBelow.

Selected references

  • Bitcoin Core contributors. GetBlockSubsidy, src/validation.cpp at revision a773e1e33b349dc819b5a0edc1c7a4839dc73b48. Source
  • Bitcoin Core contributors. COIN, MAX_MONEY, MoneyRange, src/consensus/amount.h, same revision. Source
  • Bitcoin Core contributors. CMainParams::nSubsidyHalvingInterval, src/kernel/chainparams.cpp, same revision. Source
  • Bitcoin Core contributors. Coinbase amount check in Chainstate::ConnectBlock, src/validation.cpp, same revision. Source
  • Satoshi Nakamoto. Bitcoin: A Peer-to-Peer Electronic Cash System, 2008. Paper
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Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin Soft-Fork Activation: Script Flags by Height and the Two Exception BlocksTextbook

Why rule activation is part of consensus

Bitcoin's consensus rules are not a single ruleset but a history of them. Each soft fork — P2SH, strict DER signatures (BIP66), CHECKLOCKTIMEVERIFY (BIP65), sequence locks (BIP68/112/113), SegWit with NULLDUMMY (BIP141/147), Taproot — began enforcing at a particular block height, and a node validating the chain from genesis must apply to each block exactly the rules in force at its height. Bitcoin Core implements this with buried deployments: fixed activation heights in CMainParams, consulted by DeploymentActiveAt, and a function GetBlockScriptFlags that assembles the script-verification flags for a block. Two historical blocks are special-cased by hash: one that violated P2SH before the rule was enforced, and one that violated Taproot rules before activation; for these the flag set is replaced. (GetBlockScriptFlags, deploymentstatus.h, CMainParams)

A whole-chain correctness statement needs this table to be exact: which rules apply at which height, and precisely how the two exceptions deviate. This mission transcribes it and proves its structural properties: rules are only ever added as height increases, in a fixed order, and the exceptions only ever remove rules.

Deployments, heights, and flags

A buried deployment is one of HEIGHTINCB (BIP34), CLTV (BIP65), DERSIG (BIP66), CSV, SEGWIT, with mainnet heights 227,931227{,}931227,931, 388,381388{,}381388,381, 363,725363{,}725363,725, 419,328419{,}328419,328, 481,824481{,}824481,824. It is active at a block when the block's height is at least that height, and active after a previous block when the previous height plus one is.

A flag set records which of P2SH, WITNESS, TAPROOT, DERSIG, CHECKLOCKTIMEVERIFY, CHECKSEQUENCEVERIFY, NULLDUMMY are enforced. The base is P2SH | WITNESS | TAPROOT for an ordinary block, NONE for the BIP16 exception block, and P2SH | WITNESS for the Taproot exception block. GetBlockScriptFlags adds DERSIG, CHECKLOCKTIMEVERIFY, CHECKSEQUENCEVERIFY and NULLDUMMY to the base when DERSIG, CLTV, CSV and SEGWIT respectively are active at the block's height. Flag sets are compared by inclusion: every rule enforced by the first is enforced by the second. Separately, ConnectBlock sets LOCKTIME_VERIFY_SEQUENCE for BIP68 exactly when CSV is active.

Formalization targets

Goal — soft forks only add rules

h1≤h2  ⟹  blockScriptFlags(h1)⊆blockScriptFlags(h2)h_1 \le h_2 \;\Longrightarrow\; \mathrm{blockScriptFlags}(h_1) \subseteq \mathrm{blockScriptFlags}(h_2)h1​≤h2​⟹blockScriptFlags(h1​)⊆blockScriptFlags(h2​)

for ordinary (non-exception) blocks. This is BitcoinActivation.flags_mono: at no height is a script rule enforced that a later height would not enforce. It is the weakest statement that makes "the rules in force at height hhh" a well-ordered notion.

Supporting milestones

  1. Activation is monotone (activeAt_mono); after is at of the next height (activeAfter_eq).
  2. Before BIP66 (flags_before_bip66): only the base P2SH | WITNESS | TAPROOT.
  3. From SegWit on (flags_after_segwit): every flag.
  4. Order of activation (flags_order): CHECKLOCKTIMEVERIFY implies DERSIG, CHECKSEQUENCEVERIFY implies CHECKLOCKTIMEVERIFY, NULLDUMMY implies CHECKSEQUENCEVERIFY.
  5. The BIP16 exception (bip16_exception): no flags at all at its height (170,060, before BIP66).
  6. The Taproot exception (taproot_exception): every flag except TAPROOT at its height (692,261, after SegWit).
  7. Exceptions only remove (exception_le): at any height, an exception's flags are included in the default's.
  8. Sequence-lock flag (lockTimeFlags_eq): LOCKTIME_VERIFY_SEQUENCE exactly from height 419,328419{,}328419,328.

What the result establishes

The goal and milestones pin down, as kernel-checked facts, the exact ruleset function of the pinned implementation: which script rules apply at every mainnet height, that the function is monotone and that its four height-dependent rules switch on in a fixed order, and that the two hash-keyed exceptions are the only departures and only ever relax. For the whole-consensus development this is the "historical rule selection" input: a block at height hhh is validated against blockScriptFlags(h)\mathrm{blockScriptFlags}(h)blockScriptFlags(h), and these theorems are what a composition proof cites when it reasons about rules across heights.

Status: all statements are open with intentional placeholder proofs. The exception blocks are identified by their hashes in the source; the model names them and states their flags at any height in the relevant range, so no claim about a specific hash is made.

Where the obvious argument fails

There is little depth here by design; the point is exactness. The trap is the exceptions: P2SH, WITNESS and TAPROOT are not height-gated at all in the pinned source (they are "always on except for the two violating blocks"), so a formalization that gates them by their historical activation heights would be wrong for the pinned revision. Likewise the height-dependent flags are OR-ed onto the exception's base, so the Taproot exception block does enforce DERSIG, CLTV, CSV and NULLDUMMY.

Formalization scope

Heights are Nat; flag sets are a structure of seven Booleans; inclusion is pointwise implication. Versionbits deployments (DEPLOYMENT_TESTDUMMY, the historical Taproot signalling) are not modeled, since the pinned source consults no versionbits state for mainnet consensus flags. The exception table is modeled as a three-valued classification of a block rather than a hash lookup; the hash-to-block correspondence is a serialization matter.

A trivializing formalization would define the flags directly as a monotone function of height, dropping the exceptions; the definition follows the source's base-then-OR structure with the exception table.

No Mathlib is required; the development imports only Std.

Selected references

  • Bitcoin Core contributors. GetBlockScriptFlags, src/validation.cpp at revision a773e1e33b349dc819b5a0edc1c7a4839dc73b48, lines 2249–2288. Source
  • Bitcoin Core contributors. DeploymentActiveAt, DeploymentActiveAfter, src/deploymentstatus.h; BuriedDeployment, DeploymentHeight, src/consensus/params.h; CMainParams, src/kernel/chainparams.cpp, same revision. Source
  • Bitcoin Core contributors. LOCKTIME_VERIFY_SEQUENCE, src/consensus/consensus.h, same revision. Source
  • Bitcoin Improvement Proposals 16, 34, 65, 66, 68, 112, 113, 141, 147, 341, 342, collection at revision 100ba872c808946155b1eeb8c4568058f2eea0a7. Collection
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Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin Proof of Work: Compact Targets and Difficulty RetargetingTextbook

Why the difficulty arithmetic matters

A Bitcoin block header is valid only if its hash, read as a 256-bit number, is at most a target encoded in the header's 32-bit nBits field, and only if that nBits is the one the consensus rules require at that height. Every 2016 blocks the required target is recomputed from the time the previous 2016 blocks took, so that blocks keep arriving roughly every ten minutes. Three pieces of arithmetic carry the whole rule: the lossy compact encoding between a 32-bit word and a 256-bit target (arith_uint256::SetCompact and GetCompact), the retarget computation with its clamped timespan and cap (CalculateNextWorkRequired), and the transition check that a header's claimed difficulty change is within the permitted range (PermittedDifficultyTransition), which Bitcoin Core uses to reject headers cheaply before it has the full chain. (pow.cpp, arith_uint256.cpp, CMainParams)

These functions are short but intricate: the encoding has sign and overflow flags, a mantissa that must be re-normalized when its top bit is set, and a rounding step that discards low bits; the retarget multiplies inside 256 bits where a carry would be lost. A disagreement in any of them splits the network. This mission states what the pinned implementation computes and proves that its own pieces are consistent: the retarget never produces a target the transition check rejects.

Targets, compact words, and the retarget

A compact word ccc is a 32-bit value with size S=⌊c/224⌋S = \lfloor c / 2^{24} \rfloorS=⌊c/224⌋ (the top byte) and mantissa W=c mod 223W = c \bmod 2^{23}W=cmod223 (the low 23 bits; bit 23 is the sign). Its exact value is specTarget(c)=W⋅256S−3\mathrm{specTarget}(c) = W \cdot 256^{S-3}specTarget(c)=W⋅256S−3 when S>3S > 3S>3 and ⌊W/2563−S⌋\lfloor W / 256^{3-S} \rfloor⌊W/2563−S⌋ otherwise. SetCompact computes the same quantity but inside a 256-bit register, so its result is setCompact(c)=(W≪8(S−3)) mod 2256\mathrm{setCompact}(c) = (W \ll 8(S-3)) \bmod 2^{256}setCompact(c)=(W≪8(S−3))mod2256 for S>3S > 3S>3. It also reports negative (W≠0W \ne 0W=0 and bit 23 set) and overflow (W≠0W \ne 0W=0 and S>34S > 34S>34, or W>255W > 255W>255 and S>33S > 33S>33, or W>65535W > 65535W>65535 and S>32S > 32S>32).

GetCompact encodes a 256-bit value vvv: with bbb the bit length of vvv and S=⌈b/8⌉S = \lceil b / 8 \rceilS=⌈b/8⌉, the mantissa is vvv shifted to its top three bytes (shifted left into three bytes when S≤3S \le 3S≤3); if bit 23 of that mantissa is set, the mantissa is divided by 256 and SSS incremented; the word is mantissa ∨(S≪24)\lor (S \ll 24)∨(S≪24). Because only three bytes survive, setCompact(getCompact(v))\mathrm{setCompact}(\mathrm{getCompact}(v))setCompact(getCompact(v)) is vvv rounded down.

Mainnet parameters: powLimit=2224−1\mathrm{powLimit} = 2^{224} - 1powLimit=2224−1, target timespan T=1,209,600T = 1{,}209{,}600T=1,209,600 seconds, spacing 600600600 seconds, interval 201620162016 blocks; minimum-difficulty blocks, no-retargeting, and BIP94 are all off.

DeriveTarget rejects a word that is negative, zero, overflowing, or above powLimit\mathrm{powLimit}powLimit; proof of work holds when the hash is at most the derived target. CalculateNextWorkRequired takes the last target toldt_{\text{old}}told​ and the signed elapsed time aaa of the period, clamps it to [T/4, 4T][T/4,\ 4T][T/4, 4T], computes (told⋅a mod 2256)/T(t_{\text{old}} \cdot a \bmod 2^{256}) / T(told​⋅amod2256)/T, caps at powLimit\mathrm{powLimit}powLimit, and re-encodes with GetCompact. PermittedDifficultyTransition at a retarget height decodes the claimed new word and requires it to lie between the re-encoded caps of told⋅(T/4)/Tt_{\text{old}} \cdot (T/4) / Ttold​⋅(T/4)/T and told⋅4T/Tt_{\text{old}} \cdot 4T / Ttold​⋅4T/T; off a retarget height it requires the word to be unchanged. GetNextWorkRequired returns the last word off a retarget height and calls the retarget on one.

Formalization targets

Goal — the retarget passes its own transition check

∀h,cold,tlast,tfirst:h≡0(mod2016) ∧ setCompact(cold)≤powLimit ⟹ permittedTransition(h, cold, calculateNextWork(cold,tlast,tfirst))=true.\forall h, c_{\text{old}}, t_{\text{last}}, t_{\text{first}}:\quad h \equiv 0 \pmod{2016}\ \wedge\ \mathrm{setCompact}(c_{\text{old}}) \le \mathrm{powLimit} \ \Longrightarrow\ \mathrm{permittedTransition}\big(h,\ c_{\text{old}},\ \mathrm{calculateNextWork}(c_{\text{old}}, t_{\text{last}}, t_{\text{first}})\big) = \mathtt{true}.∀h,cold​,tlast​,tfirst​:h≡0(mod2016) ∧ setCompact(cold​)≤powLimit ⟹ permittedTransition(h, cold​, calculateNextWork(cold​,tlast​,tfirst​))=true.

The hypothesis on coldc_{\text{old}}cold​ holds for every target DeriveTarget accepts, hence for every block on a valid chain. This is BitcoinPow.nextWork_permitted. It is the weakest stable statement: it says only that the two functions are consistent, without fixing the rounding constants that make them so.

Supporting milestones

  1. Overflow is exact (overflow_iff): the overflow flag is set iff specTarget(c)≥2256\mathrm{specTarget}(c) \ge 2^{256}specTarget(c)≥2256.
  2. Decoding is exact without overflow (setCompact_eq_spec): if the flag is clear, setCompact(c)=specTarget(c)\mathrm{setCompact}(c) = \mathrm{specTarget}(c)setCompact(c)=specTarget(c).
  3. Proof of work (checkProofOfWork_iff): acceptance iff the word is non-negative, non-overflowing, positive, at most powLimit\mathrm{powLimit}powLimit, and hash≤specTarget(c)\mathrm{hash} \le \mathrm{specTarget}(c)hash≤specTarget(c).
  4. Rounding down (getCompact_le): setCompact(getCompact(v))≤v\mathrm{setCompact}(\mathrm{getCompact}(v)) \le vsetCompact(getCompact(v))≤v for v<2256v < 2^{256}v<2256.
  5. Encoding is valid (getCompact_valid): GetCompact never produces a negative or overflowing word.
  6. Rounding is monotone (round_mono): v≤w<2256  ⟹  setCompact(getCompact(v))≤setCompact(getCompact(w))v \le w < 2^{256} \implies \mathrm{setCompact}(\mathrm{getCompact}(v)) \le \mathrm{setCompact}(\mathrm{getCompact}(w))v≤w<2256⟹setCompact(getCompact(v))≤setCompact(getCompact(w)).
  7. Clamp (clamp_bounds): the clamped timespan lies in [T/4,4T][T/4, 4T][T/4,4T] for every signed input.
  8. Cap (nextWork_le_powLimit): the next target decodes to at most powLimit\mathrm{powLimit}powLimit, for every input.
  9. Off-boundary (nextWork_off_boundary): off a retarget height the required word is the last word, and that transition is permitted.

What the result establishes

The goal is a kernel-checked consistency property of the pinned implementation: whatever the last target and the two block times, a block carrying the required nBits is never rejected by the cheap header-chain check. Milestones 1–3 give an exact mathematical reading of what nBits means on the wire, including when the implementation refuses it. Milestones 4–6 characterize the lossy re-encoding: it rounds down, keeps the top bit, and is order-preserving, which is the property the transition check silently relies on when it compares re-encoded bounds against a decoded value.

Formalizing adds two things to the C++. It makes the 256-bit wrap-around explicit: the retarget's multiplication is proved not to wrap only under the bound on the previous target, and the overflow flag is proved to be exactly the "does not fit" condition rather than an approximation. And it isolates the rounding as a function of the top-bit position, which is what the monotonicity argument needs and what the source never states.

Status: all statements are open with intentional placeholder proofs. Nothing about compiled Bitcoin Core is claimed; the value-level model of arith_uint256 (a natural number below 22562^{256}2256, shifts and products reduced modulo 22562^{256}2256, exact division) is the documented correspondence.

Where the obvious argument fails

The naive proof of the goal is to unfold both functions and compare. It fails at the re-encoding: the new word is getCompact(x)\mathrm{getCompact}(x)getCompact(x) and the check compares setCompact(getCompact(x))\mathrm{setCompact}(\mathrm{getCompact}(x))setCompact(getCompact(x)) against setCompact(getCompact(y))\mathrm{setCompact}(\mathrm{getCompact}(y))setCompact(getCompact(y)) for the two bounds yyy. These are not xxx and yyy; they are xxx and yyy with low bits cleared, and how many bits are cleared depends on the bit length of each. The argument needs that the rounding map is monotone across different bit lengths, which holds because the rounded value always retains the top bit — a value with fewer bits is below the top bit of any value with more.

A second trap is the mantissa normalization. When the top three bytes have bit 23 set, GetCompact shifts by a further byte, so the number of discarded bits is not a function of the size byte alone; the case split in the proofs is on the bit position modulo 8.

A third is nActualTimespan, a signed 64-bit difference that can be negative when timestamps go backwards. The clamp is stated on signed integers and the conversion to a natural number happens after it.

Formalization scope

arith_uint256 values are Nat below width = 2 ^ 256; <<= and *= reduce modulo width, /= is Nat division, bits() is Nat.log2 + 1 with 0 for zero, and GetLow64 is % 2 ^ 64. The uint32_t assignment in GetCompact is % 2 ^ 32. Compact words are BitVec 32; their fields are read through toNat. Block times are Int; the clamped timespan is converted with Int.toNat after clamping. Hashes enter checkProofOfWork already as natural numbers; the byte-order correspondence of UintToArith256 is a serialization matter outside this mission. fPowAllowMinDifficultyBlocks, fPowNoRetargeting, enforce_BIP94, and the fuzz-determinism shortcut are fixed to their mainnet values and not modeled as parameters.

A trivializing formalization would define permittedTransition by calling calculateNextWork; both are transcribed independently from the source. Assuming the previous target is already a GetCompact output, which would make rounding exact, is also ruled out: the goal assumes only the powLimit bound.

No Mathlib is required; the development imports only Std. Proofs of milestones in any order are welcome, and lemmas on the rounding map are reusable by any later header-validation work.

Selected references

  • Bitcoin Core contributors. GetNextWorkRequired, CalculateNextWorkRequired, PermittedDifficultyTransition, DeriveTarget, CheckProofOfWorkImpl, src/pow.cpp at revision a773e1e33b349dc819b5a0edc1c7a4839dc73b48. Source
  • Bitcoin Core contributors. arith_uint256::SetCompact, arith_uint256::GetCompact, base_uint::operator<<=, operator*=, operator/=, bits, src/arith_uint256.cpp and .h, same revision. Source
  • Bitcoin Core contributors. CMainParams proof-of-work parameters, src/kernel/chainparams.cpp, same revision. Source
  • Satoshi Nakamoto. Bitcoin: A Peer-to-Peer Electronic Cash System, 2008, section 4. Paper
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Formal VerificationTheoretical Computer Science·Captain: pitimespi

Bitcoin UTXO Accounting: Value Conservation and the Ledger Supply BoundTextbook

Why value accounting matters

Bitcoin's ledger is a set of unspent transaction outputs: every coin that exists is an output of some transaction that no later transaction has spent. A transaction consumes outputs and creates new ones; the rule that it may not create value — that its outputs total at most its inputs — is what makes the supply finite, and the one exception, the coinbase transaction of each block, may create at most the block subsidy plus the fees the block's other transactions paid. Bitcoin Core enforces this through a handful of functions: CheckTransaction rejects negative or overflowing output values (CVE-2010-5139) and duplicate inputs (CVE-2018-17144); CheckTxInputs requires every input to be an existing, mature coin and computes the fee; UpdateCoins spends the inputs and adds the outputs; ConnectBlock accumulates the fees and rejects a coinbase that pays more than fees plus subsidy, after a BIP30 check that no output of the block already exists as an unspent coin. (tx_check.cpp, tx_verify.cpp, coins.cpp, validation.cpp)

Each of these checks has been the site of a real inflation bug. This mission transcribes them and proves the ledger-level consequence: starting from an empty ledger, after any sequence of accepted blocks the total value of all unspent outputs is at most the sum of the block subsidies.

Coins, transactions, and the view

Amounts are integers in satoshi; MoneyRange is 0≤v≤MAX_MONEY=2.1×10150 \le v \le \mathrm{MAX\_MONEY} = 2.1 \times 10^{15}0≤v≤MAX_MONEY=2.1×1015. Transaction ids form an arbitrary type TTT with decidable equality. An outpoint is a pair (txid, index). A coin is an output value together with its creation height, its coinbase flag, and whether its script is unspendable (the only script property the accounting reads: AddCoin never stores an unspendable output). A transaction has a txid, a list of input outpoints, a list of outputs, and a coinbase flag standing for IsCoinBase(). The UTXO set is a list of (outpoint, coin) entries with pairwise distinct outpoints; getCoin looks an outpoint up, spendCoin removes it, addCoin inserts (replacing any entry with the same outpoint, as possible_overwrite permits), and totalValue sums the stored values.

The transcribed rules: checkTransaction (non-empty inputs and outputs, the output-value loop with its running MoneyRange test, no duplicate inputs); checkTxInputs (all inputs present, coinbase inputs mature by COINBASE_MATURITY = 100, each input value and the running nValueIn in range, nValueIn \ge GetValueOut(), fee in range); updateCoins (spend inputs unless coinbase, add outputs); bip30Ok; checkBlockTxs (first transaction coinbase, others not, all passing checkTransaction); connectLoop (per-transaction fee accumulation with the running MoneyRange test, then updateCoins); connectBlock (the above, then GetValueOut(coinbase) \le nFees + subsidy(height)); and connectChain, which connects blocks at consecutive heights. The subsidy is a parameter s:N→Zs : \mathbb N \to \mathbb Zs:N→Z.

Formalization targets

Goal — the ledger never holds more than the subsidies issued

connectChains([ ], h0, B1⋯Bn)=some(U)  ⟹  totalValue(U)≤∑i<ns(h0+i),\mathrm{connectChain}_s([\,],\ h_0,\ B_1 \cdots B_n) = \mathrm{some}(U) \;\Longrightarrow\; \mathrm{totalValue}(U) \le \sum_{i<n} s(h_0 + i),connectChains​([], h0​, B1​⋯Bn​)=some(U)⟹totalValue(U)≤i<n∑​s(h0​+i),

for every subsidy function sss, starting height h0h_0h0​, and sequence of blocks whose transactions have pairwise distinct txids within each block. This is BitcoinUtxo.ledger_bound. It is the weakest stable statement: the subsidy schedule is a parameter, so the result survives any change to it, and composing with the subsidy mission's bound gives the twenty-one-million cap as a corollary.

Supporting milestones

  1. Output checks (checkTransaction_outputs): an accepted transaction has distinct inputs and every output value in [0,MAX_MONEY][0, \mathrm{MAX\_MONEY}][0,MAX_MONEY].
  2. The fee (checkTxInputs_some): CheckTxInputs returns exactly valueIn−valueOut\mathrm{valueIn} - \mathrm{valueOut}valueIn−valueOut, in range, with every input present.
  3. Spending one coin (totalValue_spend): removing a present coin lowers the total by exactly its value.
  4. Adding a fresh coin (totalValue_add_fresh): adding at a fresh outpoint raises the total by the value, or by nothing if unspendable.
  5. Spending the inputs (totalValue_spendInputs): spending distinct, present inputs lowers the total by valueIn\mathrm{valueIn}valueIn.
  6. Transaction conservation (tx_conservation): an accepted ordinary transaction with fresh outputs lowers the ledger by at least its fee.
  7. Coinbase (coinbase_bound): a coinbase with fresh outputs raises the ledger by at most its output value.
  8. Block (block_bound): an accepted block raises the ledger by at most s(h)s(h)s(h) and keeps outpoints distinct.
  9. Chain (chain_bound): the same, summed over consecutive blocks, from any ledger.

What the result establishes

The goal is a kernel-checked statement that the transcribed rules, applied from genesis, cannot put more value into the ledger than the subsidy schedule allows, for any transactions whatsoever: fees only move value, unspendable outputs only destroy it, and the coinbase is bounded by fees plus subsidy. The milestones give the exact per-transaction accounting the proof rests on, including the role of each historical fix: the MoneyRange loop (milestone 1), the duplicate-input rejection (milestones 5 and 6 need distinct inputs), and the BIP30 check (milestones 6 to 8 need fresh outputs).

Three hypotheses are the honest boundary of the result. Distinct txids within a block stand for the collision-freedom of transaction hashes, which an abstract id type cannot provide. The BIP30 check is modeled as enforced, which is true at every mainnet height except the two historical duplicate-coinbase blocks. And the genesis coinbase, which Bitcoin Core never connects to the UTXO set, is simply not among the blocks.

Status: every statement is open with an intentional placeholder proof. Nothing about compiled Bitcoin Core is claimed.

Where the obvious argument fails

The naive argument — "inputs exceed outputs, so value cannot grow" — fails at three places that the actual code guards. Without distinct inputs, a transaction that lists the same outpoint twice would be credited its value twice while the view removes it once (CVE-2018-17144); the accounting lemma for spending needs Nodup. Without the BIP30 check, an output could overwrite an existing unspent coin and silently destroy or replace its value; the lemma for adding a coin needs the outpoint fresh, and freshness must be carried through the block from the initial view, which is where distinct txids enter. And the output values must be known non-negative before any sum is bounded, which is exactly what the MoneyRange loop supplies.

The fee is a second trap: CheckTxInputs computes it with signed arithmetic and tests it separately, so the proof must recover the identity fee=valueIn−valueOut\text{fee} = \mathrm{valueIn} - \mathrm{valueOut}fee=valueIn−valueOut from the loop rather than assume it.

Formalization scope

Amounts are Int; heights are Nat, and the maturity test uses truncated subtraction, which rejects a spend below the coin's height as the signed source does. The UTXO set is a keyed list; keysNodup is the map invariant and is proved preserved. Serialization-size, script-length, null-prevout and script-validity rules are not modeled; the coinbase flag is a field. The BIP30 enforcement flag, fEnforceBIP30, is fixed to its post-exception mainnet value.

A trivializing formalization would define connectBlock so that it fails whenever value would grow; every rule is transcribed from its source function. Restricting to blocks with no unspendable outputs or to transactions with a single input is out of scope.

No Mathlib is required; the development imports only Std. The composition with the subsidy mission's BitcoinSubsidy.issuance_bound, giving the absolute cap, is the intended follow-up theorem.

Selected references

  • Bitcoin Core contributors. CheckTransaction, src/consensus/tx_check.cpp at revision a773e1e33b349dc819b5a0edc1c7a4839dc73b48. Source
  • Bitcoin Core contributors. Consensus::CheckTxInputs, src/consensus/tx_verify.cpp, same revision. Source
  • Bitcoin Core contributors. CCoinsViewCache::AddCoin, SpendCoin, HaveInputs, AddCoins, src/coins.cpp, same revision. Source
  • Bitcoin Core contributors. UpdateCoins, BIP30 check and fee accounting in Chainstate::ConnectBlock, src/validation.cpp, same revision. Source
  • Pieter Wuille. BIP30: Duplicate transactions, 2012. Specification
  • Satoshi Nakamoto. Bitcoin: A Peer-to-Peer Electronic Cash System, 2008, sections 2 and 9. Paper
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Combinatorics·Captain: Ethan Cheung Wang

Erdos 624Open Problem

Motivation

Erdős Problem 624 (erdosproblems.com/624) is a problem of Erdős and Hajnal about how few elements of a set can be enough to "see" the whole set through a function on its subsets. For a finite set XXX with ∣X∣=n|X| = n∣X∣=n, let H(n)H(n)H(n) be the least mmm for which some map f:P(X)→Xf : \mathcal P(X) \to Xf:P(X)→X has the following property: for every Y⊆XY \subseteq XY⊆X with ∣Y∣≥m|Y| \ge m∣Y∣≥m,

{f(A):A⊆Y}=X.\{ f(A) : A \subseteq Y \} = X .{f(A):A⊆Y}=X.

The problem asks to prove that H(n)−log⁡2n→∞H(n) - \log_2 n \to \inftyH(n)−log2​n→∞.

The question belongs to extremal set theory. It asks whether the trivial counting bound 2∣Y∣≥n2^{|Y|} \ge n2∣Y∣≥n can be beaten by an unbounded additive amount.

Timeline.

  • Erdős and Hajnal (1968) proved log⁡2n≤H(n)<log⁡2n+(3+o(1))log⁡2log⁡2n\log_2 n \le H(n) < \log_2 n + (3+o(1))\log_2\log_2 nlog2​n≤H(n)<log2​n+(3+o(1))log2​log2​n ([ErHa68], as reported on erdosproblems.com/624).
  • Erdős remarked that even H(2k)≥k+1H(2^k) \ge k+1H(2k)≥k+1 was open. Alon gave a short pigeonhole proof of it. Alon also proved, for large kkk and ∣X∣=2k|X| = 2^k∣X∣=2k, that every fff has a kkk-set YYY with ∣f[P(Y)]∣<(1−c)2k|f[\mathcal P(Y)]| < (1-c)2^k∣f[P(Y)]∣<(1−c)2k for an absolute constant c>0c > 0c>0. Both are personal communications recorded on erdosproblems.com/624.
  • The problem remains open.

Setting

  • Fin n\mathrm{Fin}\,nFinn stands for an nnn-element set.
  • ExistsEventuallySurjective n mn\ mn m holds if some f:P(Fin n)→Fin nf : \mathcal P(\mathrm{Fin}\,n) \to \mathrm{Fin}\,nf:P(Finn)→Finn satisfies Y.powerset.image f=univY.\mathrm{powerset}.\mathrm{image}\ f = \mathrm{univ}Y.powerset.image f=univ whenever ∣Y∣≥m|Y| \ge m∣Y∣≥m.
  • H(n)H(n)H(n) is the infimum of such mmm for n>0n > 0n>0, and H(0)=0H(0) = 0H(0)=0.
  • Both definitions are copied verbatim from Google DeepMind's Formal Conjectures file ErdosProblems/624.lean and published here as the definition Erdos624_defs.

Much of the supporting development works in a two-map (PAIR) reformulation:

  • ∣U∣=∣C∣=n=2k|U| = |C| = n = 2^k∣U∣=∣C∣=n=2k, with two arbitrary maps g0,g1:P(U)→Cg_0, g_1 : \mathcal P(U) \to Cg0​,g1​:P(U)→C.
  • L(A)={g0(A),g1(A)}L(A) = \{g_0(A), g_1(A)\}L(A)={g0​(A),g1​(A)}, J(Y)=⋃A⊆YL(A)J(Y) = \bigcup_{A \subseteq Y} L(A)J(Y)=⋃A⊆Y​L(A) and D(Y)=n−∣J(Y)∣D(Y) = n - |J(Y)|D(Y)=n−∣J(Y)∣.
  • A colour u∉J(Y)u \notin J(Y)u∈/J(Y) is a common hole of YYY.

More generally, the rrr-track version allows rrr maps g1,…,grg_1, \dots, g_rg1​,…,gr​.

Formalization targets

Goal (open)

lim⁡n→∞(H(n)−log⁡2n)=+∞,\lim_{n\to\infty} \bigl(H(n) - \log_2 n\bigr) = +\infty,n→∞lim​(H(n)−log2​n)=+∞,

stated as Erdos624.erdos_624 exactly as in Formal Conjectures.

Sufficient condition (open): bounded-track resilience

For every fixed rrr there is KKK such that for all k≥Kk \ge Kk≥K and all g1,…,gr:P(Fin 2k)→Fin 2kg_1, \dots, g_r : \mathcal P(\mathrm{Fin}\,2^k) \to \mathrm{Fin}\,2^kg1​,…,gr​:P(Fin2k)→Fin2k, some kkk-set YYY and colour uuu satisfy gi(A)≠ug_i(A) \ne ugi​(A)=u for all iii and all A⊆YA \subseteq YA⊆Y.

The implication "bounded-track resilience ⇒\Rightarrow⇒ Erdős 624" is proved in Lean (erdos_624_of_bounded_track_resilience). Whether the hypothesis is true is open.

Two-track case (open): PAIR

For all sufficiently large kkk, every pair g0,g1:P(Fin 2k)→Fin 2kg_0, g_1 : \mathcal P(\mathrm{Fin}\,2^k) \to \mathrm{Fin}\,2^kg0​,g1​:P(Fin2k)→Fin2k has a kkk-set YYY with D(Y)>0D(Y) > 0D(Y)>0.

This is the r=2r = 2r=2 case of the condition above. On its own it does not imply Erdős 624, and it is false at k=3k = 3k=3 (pair_common_hole_false_k3).

Significance

A proof of the goal would settle a 1968 question of Erdős and Hajnal. It would show that the information-theoretic bound ∣Y∣≥log⁡2n|Y| \ge \log_2 n∣Y∣≥log2​n is never tight up to bounded error.

The present mission collects machine-checked partial and structural results obtained while attacking the PAIR case:

  • a uniform, explicit-constant version of Alon's theorem ("robust Alon": a random kkk-set misses at least n/32n/32n/32 points on average), plus conditioned versions on stars of kkk-sets through a small core;
  • monotonicity of HHH, and a track-amplification lemma reducing the goal to bounded-track resilience;
  • obstructions for structured maps (monoid factorisations, one structured map);
  • a rank-deficiency lemma for F2\mathbb F_2F2​ labellings;
  • packing lemmas for proper holes;
  • the "top-layer-free resilience" theorem for covering pairs;
  • minimal-envelope linear algebra over F2\mathbb F_2F2​: extension of holes, r≤k−1r \le k-1r≤k−1 under coverage, critical-rank rigidity and circuit identification;
  • arithmetic and lower-bound ingredients of a local counterexample construction.

Status, stated plainly: none of these results proves PAIR, the "Step 17" target of the underlying analysis, or Erdős 624. All of them are partial or structural. Some theorems are pure arithmetic consequences of an unproved packing statement (P), which enters only as a numeric hypothesis. In particular, the rank-rich exclusion (k≥157k \ge 157k≥157) is conditional on (P), and (P) is neither claimed nor formalized here.

Every linked theorem has an accepted, sorry-free Lean proof on this platform using only the standard axioms (propext, Classical.choice, Quot.sound).

Difficulty

Alon's pigeonhole argument gives a missed colour for a single map. For two or more maps the missed sets of different maps can be disjoint: pair_common_hole_false_k3 shows exactly this at k=3k = 3k=3, where each map misses half the colours on every 3-set yet no colour is missed by both. The underlying analysis also shows that separately convexified (fractional) relaxations of the fibre constraints admit fractional "covering pairs", so integrality or genuinely joint information across colours must enter any proof. The holes produced on small sets for different colours do not obviously combine, because the union of two holes can contain a new witness.

Formalization scope

  • Conventions:
    • Lean 4 with Mathlib (rev 0df444a).
    • HHH is defined on Fin n\mathrm{Fin}\,nFinn exactly as in Formal Conjectures.
    • Supporting theorems use Finset with arbitrary finite types U,CU, CU,C and two maps g0,g1g_0, g_1g0​,g1​, or list maps L:P(U)→P(C)L : \mathcal P(U) \to \mathcal P(C)L:P(U)→P(C).
    • Statements inline their own objects instead of introducing custom definitions where possible.
    • Natural-number subtraction is used only where it is harmless; each such statement notes it.
  • No trivialisation: the goal is the Formal Conjectures statement, with no extra hypotheses.
  • Welcome contributions:
    • proofs or disproofs of the two open targets;
    • new structural results on covering pairs;
    • independent re-proofs of the linked lemmas.

Credit

Mathematics derived from GPT 6.1 Sol's analysis, relayed by Ethan Cheung; hand-checked and Lean formalization by Grok Bot.

Selected references

  • [ErHa68] P. Erdős and A. Hajnal (1968) and [Er99] P. Erdős (1999), as cited, with the Erdős–Hajnal bounds and Alon's results, on the problem page https://www.erdosproblems.com/624
  • Google DeepMind, Formal Conjectures, FormalConjectures/ErdosProblems/624.lean, https://github.com/google-deepmind/formal-conjectures/blob/main/FormalConjectures/ErdosProblems/624.lean
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