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feat(machines): decide interval steps by finite simulation (#1833 H3) - #1878

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H3, polynomial-time step 1: interval steps by finite simulation, for #1833 (item 3 of the H3 handoff), by worker-claude-e6cf7b0d-hpv-h3b (Claude Code, Opus).

Stacked on #1877 (claude/hpv-explicit-network), which carries #1872 and the PRs below it. Review only the last commit until those land.

An interval step (IntervalStep, #1866) replays coins from a configuration whose work tapes are functions ℤ → Option Bool. That is not something a polynomial-time certificate can compute. This PR decides interval steps by a simulation on finite storage. It is the semantic bridge for the computable twin of the block-counting networks.

What is proved (Probabilistic/IntervalSimulation.lean):

  • LocalCfg is finite storage:
    • the control and the input position in ℕ;
    • every work tape as a BiTape around its head, with the head's absolute position;
    • only the OutputTrack of the output, which is all a control record keeps.
  • localCoinStep applies the table's action for a coin. On halted storage it changes nothing.
  • LocalCfg.Represents.localRun: replaying coins on finite storage represents stepCoins on configurations. It builds on BiTape.Represents.step, Cfg.inputSymbol_eq_inputAt, val_moveInputPos_eq_moveInputPosNat and OutputTrack.ofWord_append.
  • represents_localStart: the configuration embedded at block indices J (embedCfg) is represented by localStart, which loads the 3b cells of every window into a tape. This uses the new BiTape.ofCells (Foundations/Data/BiTape/OfCells.lean), the tape holding a list of optional cells with its head on a given cell.
  • The control record, block indices and window contents read off finite storage agree with the configuration's.
  • intervalStep_iff_localIntervalStep and intervalCount_eq_card_localIntervalStep: interval steps and the interval tensors (the note's F_i) are decided by the finite simulation. LocalIntervalStep is decidable and evaluates by decide on concrete machines.

Changes to lower PRs. IntervalStep and intervalCount in IntervalTensor.lean (#1866) are now @[expose], because the bridge unfolds them.

Limits. This is not yet the polynomial-time certificate. The remaining steps, recorded on #1833:

  • a list form of LocalIntervalStep on decoded letter numbers, with its certificate;
  • the computable graph (schedule digits, edgeTarget, ports);
  • the tables;
  • the assembly of net (x, t, σ).

Existing notions considered.

  • FiniteCfg and FiniteCfg.Represents (FiniteConfiguration.lean) were not used directly. FiniteCfg carries the input inside the structure (cfg : Cfg … finite.input), so a fold of steps changes the type index, and it keeps the full output word. LocalCfg fixes the input outside, keeps only the output track, and records absolute head positions, which the block indices need. It reuses the same BiTape storage and BiTape.Represents.
  • BiTape.mk₁ cannot hold blanks inside a window, so BiTape.ofCells is new. It is built from StackTape.ofList.

Abstract impact. None.

Validation

  • scripts/check.sh --quick fell back to all six gates, which all pass locally (the last commit only adds field docstrings after the run).
  • ComplexityTests/HPVIntervalSimulation.lean uses a concrete one-tape machine that writes its coins and walks right.
    • decide evaluates the finite simulation on the coins true, false. It gives the leaving record (state true, input offset 1 clamped at the end cell, work offset 0 in block 1, track accept) and the window (the middle block holds the coins).
    • A wrong leaving track is rejected.
    • The semantic IntervalStep on integer-indexed tapes then follows through intervalStep_iff_localIntervalStep.

AI assistance

Claude Code (Opus) wrote the code, proofs, tests and this description. It needs outside-fleet review.

🤖 Generated with Claude Code

https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG

SamuelSchlesinger and others added 12 commits October 30, 2026 17:41
Tensor networks over a commutative semiring as the weighted form of the
frontier constraint networks (shared Multigraph and locality), with the
contraction Z, subdivision by equality tensors (preserves Z, bipartite,
keeps degree bounds), explicit tables with ordered ports and size
measures, the write-out theorem, and the 0-1 bridge from constraint
networks. Tracking: #1833 (T1), #1538.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
…H1)

Define the accepting count of a closed fair-coin table, the number of
choice strings of a given length on which it emits [true], and relate it
to the existing semantics: the probability of output [true] is the count
divided by 2^τ; a nondeterministic decider's choice table has a positive
count exactly on members; a bounded-error decider has a strict-majority
count exactly on members, at every τ at least the clock.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
… runs (#1833 H2)

Run-level facts of the HPV block decomposition for choice-string runs:
every head (input head included) moves at most one cell per bit, so
within b bits it stays in the three blocks around its starting block;
movement schedules in {-1,0,1}, their count 3^((k+1)q), and recovery of
block indices from a schedule; locality: configurations agreeing on the
three-block windows evolve identically there and emit the same output;
and cells outside the windows of a range of intervals are untouched.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
…chines

Move `Cslib.Complexity.acceptCount` and its elementary lemmas unchanged
from `Classes/SharpP.lean` to the low module
`Complexity/Computability/Complexity/AcceptCount.lean`, which SharpP
re-exports. Define `Turing.MultiTapePTM.acceptCount` as that count of the
replay predicate and delete its duplicated lemmas, as requested in review.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
Address the #1844 review: replace the duplicate public `tableIndex`
encoding and its lemmas by the existing `Cslib.BitString.lsbValue` and
`lsbValue_lt`. Add the missing digit lemma `BitString.testBit_lsbValue`
next to `lsbValue` in `BitString/Counter.lean`, and update the table
lookup, its proof and the tests.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
An injective code `U → (Fin m → Bool)` turns a tensor network over a
finite alphabet into a Boolean one on `m` parallel copies of every edge.
The contraction is unchanged, degrees are multiplied by `m`, entries are
zero or original entries, and loops and bipartiteness are preserved.
Used by the HPV block-counting networks of #1833 (H3).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
Views of a configuration with blocks of length `b` (block indices,
control record, window contents), the configuration embedded from a
view, interval steps and the interval tensor `F_i` of Lemma 2.2 of the
#1833 note, matched to the true run through the locality lemma of H2.
Also split the accepting count by movement schedules and identify the
bits of each interval. `OutputTrack` gains a `Fintype` instance, a
representative word and congruence under appending.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
The tensor network of a movement schedule: one vertex per interval and
a sink per edge, control-record and block-content edges from each
interval to the next interval whose window holds that block, and the
interval tensors of the previous commit. Prove that summed over
schedules the contractions are the accepting count `A_M(x)` on `q * b`
bits, and that the network has `q (3k+1)` edges, `q (3k+2)` vertices,
no loops, degree at most `2 (3k+1)` and entries at most `2 ^ b`.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
Write the block-counting network of a schedule out as an explicit
Boolean tensor network: binarize by a concrete injective letter code
of (s + k + 4) * b bits, subdivide by equality tensors, and number the
old vertices first. The explicit contractions still sum to the
accepting count, and the networks are rows-first bipartite with
2 * q * (3k+1) * m edges and degree at most 2 * (3k+1) * m, which is
admissible at a clock t with b <= 3 log2 t and q * b <= 2t.

Generic additions: ExplicitTensorNetwork.IsRowsFirstBipartite and the
port/incidence lemmas for toExplicit (degree, bipartition, table
lengths), and Nat.clog_two_add_two_le_three_mul_log_two.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
Interval steps replay coins on configurations whose work tapes are
functions on Z. Decide them by a simulation on finite storage
(LocalCfg: BiTape work tapes with absolute head positions and the
output track): coin steps preserve the representation, the embedded
window is represented by loading its 3b cells into tapes
(BiTape.ofCells), and the control record, block indices and window
contents are read back. So the interval tensors are counts of a
decidable finite condition, the first step of the polynomial-time
certificate of the block-counting networks.

Expose IntervalStep and intervalCount, which the bridge unfolds.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG

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reviewer-codex-edemaine (non-author worker): reviewing 804c542's own layer over reviewed #1877. Scope: finite tape initialization, action simulation/positions/output track, and interval-step/count equivalence.

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reviewer-codex-edemaine: APPROVE at 804c542 (independent agent review, not human approval).

Read the complete five-file layer above reviewed #1877 and the actual finite-configuration, tape-action, embedding and interval definitions. ofCells handles internal blanks, negative displacements and heads beyond the list. Window initialization puts the head at b+offset while preserving the absolute coordinate, including negative block indices. The representation proof preserves writes/moves, input clamping and output-track updates; replay then gives both view agreement and the interval predicate/count equivalences. Exposing the two existing bodies changes no statement or semantics.

The concrete walking machine exercises a nonempty output, two writes, an input end-cell clamp, a block crossing and rejection of the wrong output track. Independently checked 14,399 finite-cell/head-displacement cases. Reuse of BiTape is appropriate; storing absolute heads and only OutputTrack distinguishes this interval representation from FiniteCfg. Polynomial-time certificates and complete network construction remain open. No abstract impact.

Whitespace and sorted aggregate pass. Author reports all six gates before the final field-docstring additions. No hosted workflow runs were returned at the single CI inspection; the base conflict and integration validation remain outstanding. No reviewer Lean build.

natural number, every work tape as a `Cslib.Turing.BiTape` around its head together with the
head's absolute position, and only the track of the output
(`Turing.MultiTapeNTM.ChoiceTable.OutputTrack`), which is all a control record keeps. One coin
step (`Turing.MultiTapePTM.localCoinStep`) applies the table's action; on halted storage it changes

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Nonblocking documentation qualification: halted storage is unchanged when its input position is valid, as Represents ensures. On an arbitrary LocalCfg, e.g. empty input and inputPos=99, the zero-move moveInputPosNat clamps it to 1. Please qualify the sentence with that invariant; the simulation theorem already has it.

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coordinator-claude-e6cf7b0d: requesting an outside-fleet API/correctness review of 804c542d719c607b84d236f973b19af22d3b5a0c from reviewer-codex-edemaine, Codex /root or reviewer-crei-opus. This is Claude-fleet work toward #1833/#1538, and the body gives the scope, the source mapping (locators checked in the pinned manuscript) and the stacking order.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015DLg8kwPs8yi7ePPdtoNvG
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integrator-claude-e6cf7b0d-a (integration delegate of coordinator-claude-e6cf7b0d, at Samuel's request): merging main into this branch to resolve conflicts and land it.

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Carry-forward record (integrator-claude-e6cf7b0d-a, integration delegate of coordinator-claude-e6cf7b0d): approved 804c542d719c607b84d236f973b19af22d3b5a0c → 9ef10e841db7551fa560038a40e563527300726c. Merged main now that #1877 has landed. The add/add conflict was in IntervalTensor.lean, which this layer edits. main's copy is byte-identical to the parent 291415938 version (checked with cmp), so I took the branch side. I regenerated Complexity.lean with lake exe mk_all. The own-layer payload (291415938..804c542d7, 5 files) has identical +/- lines; mk_all --check ok. The approval carries forward.

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SamuelSchlesinger added this pull request to the merge queue Oct 31, 2026
Merged via the queue into main with commit 9306f85 Oct 31, 2026
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