Source-Bound Reproduction, Deterministic Builds, and Availability

The core Stage 0–6 results in sec. 5 use vendored inputs and local computation. The release source repository is Institute release source repository. Their ordinary recorders support deterministic offline replay once the pinned environment and generated prerequisites are prepared. The one-shot replication of sec. 10.6 is deliberately outside that replay inventory. Its spent-population refusal and the separate descriptive replay are explained below. The external-model reference frame is the stated exception. Re-measuring it requires a local Ollama server serving the named local probes. The stipulated synthetic and in-context probes score through the hosted OpenRouter transport, which requires an exported OPENROUTER_API_KEY kept out of versioned artifacts. Eligible cached replies avoid repeated generation under their recorded transport identity. Local Ollama identity is content-bound; hosted OpenRouter identity records logical model slugs and does not authenticate immutable serving weights. The historical component corpora are vendored as committed count artifacts (tbl. 20). Their upstream use terms, including Brown’s non-commercial restriction and the Czech artifact’s CC BY-NC-SA terms, remain in LICENSE and THIRD_PARTY_NOTICES.md.

Availability. The versioned release source is the Institute repository’s release tag. The corresponding Zenodo deposition is unpublished. Its reserved DOI identifies the intended archive and does not yet resolve to a public record. The citation, package metadata and title page identify the same intended archive and release destination. Public source and archival availability do not establish independent reproduction. No independent reproduction outside the project is recorded in the evidence available here. A reproducibility claim becomes a result only when someone other than its authors acts on it.

Pinned environment and interpreter identity

The project pins inferactively-pymdp 1.0.4, which runs on JAX (Bradbury et al. 2018), and is installed with uv sync --locked from pyproject.toml and the committed uv.lock. All project commands then address .venv/bin/python explicitly. The quick release profile checks this base dependency set. The full profile requires the declared gnn-interop extra, installed with uv sync --locked --extra gnn-interop, and a real upstream GNN checkout selected by AILM_GNN_UPSTREAM or the validated default discovery path. Its interoperability checks fail on missing prerequisites instead of skipping. Returning to quick requires uv sync --locked to restore the base set; neither release profile modifies the environment it checks. This sidecar is not a member of the surrounding template workspace, so uv run can bind the template environment and a bare pytest can select a shim; either can produce a green run against the wrong Python or pymdp. Coverage on src/ailm is gated at 90% with branch coverage (fail_under in pyproject.toml, recorded into the ledger by the Stage 0 recorder so this sentence cannot silently disagree with the gate).

Vendored corpus artifacts and their population boundaries

The historical component corpora are reduced to committed count artifacts, so core corpus evaluation and figure generation do not require downloading those corpora again. The connected GUM evaluation additionally uses a vendored raw CoNLL-U test file and a frozen document/exclusion manifest, rather than only sufficient statistics.

Full provenance for every artifact, including each derivation choice that biases the counts, is in docs/provenance/sources.md: the text normalization applied to Brown (Francis and Kučera 1979; Bird et al. 2009), and for CMUdict (Carnegie Mellon University Speech Group, n.d.) the use of the primary pronunciation only, the stripping of stress digits to the canonical 39-phone inventory, the length-matching restriction, and the shipped sorted-index interleave. That seedless word-level split is reproducible but permits lexical-family overlap; the separately recorded family-disjoint arm keeps each prefix family on one side and reports the covariate cost of doing so. The accompanying docs/reference/data_statement.md describes language varieties, curation, filtering, sampling units, and limits on generalization. It covers the English corpora and authored Stage 1 fixture, the exploratory Czech morphology arm, and the stipulated letter and phone controls separately. For English EWT it distinguishes internal document partitions of upstream dev from the upstream test population subsequently used under the registered replication protocol.

The connected evaluation uses vendor/en_gum-ud-test.conllu, data/connected_gum/protocol.json and data/connected_gum/input_manifest.json. scripts/record_connected_gum_claims.py authenticates the frozen inputs, excludes the documents already inspected during bounded development, and records paired per-document losses with its execution binding. Replaying those same documents checks reproducibility; it does not create another independent replication. The GUM file retains its upstream CC BY-NC-SA 4.0 terms (Zeldes 2017). Only its raw text comments enter inference under the normalization specified in sec. 5.1; annotation columns remain withheld.

Reproduction order and offline, local, and hosted dependencies

tbl. 21 lists the reproduction path from the project root. Set and export TEMPLATE_REPO_ROOT to the existing template checkout’s path before running the table. The render and validation commands use that variable to change directory in a subshell. Core level verdicts are independent of the model server. Re-measuring the external reference frame of sec. 13, including the stipulated controls, uses the network claim phase and requires a local Ollama server with all registered probes available and, for the two hosted scored-call recorders, an exported OPENROUTER_API_KEY. A missing probe or key refuses the comparison rather than silently shrinking its declared family. Environment installation and count regeneration from upstream sources may also require downloads. The committed counts support offline core evaluation.

That network phase stores calls individually, keyed by the request, endpoint, protocol and transport-specific identity. Local Ollama calls require a server-supplied content digest or a validated content blob in a stable build manifest. Hosted OpenRouter calls instead hash requested and returned model slugs; a hash of a logical slug is not a hash of serving weights. Capped hosted runs additionally bind the selected provider and price constraints in both the exact cached payload and the run receipt, retaining raw server response and usage evidence. This documents the serving route and observed response, while provider-side weight immutability remains unverified. The synthetic and in-context recorders retain registered probe tags as run labels, but their scored calls use the hosted model named in the receipt, not distinct local weights. Replay is protocol-specific. The external benchmark can reuse eligible ordinary calls, but its repeat-agreement trials make both requests fresh. Every scored synthetic and in-context call is fresh on a publication run. The drift recorder pairs authenticated stored log-probability replies with fresh uncached observations. Re-running the phase is therefore a new measurement and can change scientific values.

The external, synthetic, and in-context recorders namespace every fresh scored call with a UTC run scope, the registered probe tag, and that call’s own position slot — every scored call’s namespace is unique within its run, because the hosted transport’s replies are not assumed deterministic — preserving older records without overwriting them. The in-context recorder authenticates the synthetic producer’s stored zero-shot replies under that producer’s scope before making its own scored calls. The drift recorder instead binds its sampled stored records and new observation pairs directly. Receipts record hit, miss, and fresh-call accounting as execution evidence; their validator reconstructs the claims from the exact declared records and rejects missing, extra, or inconsistent evidence. Source and vendored-input manifests must remain unchanged throughout recording.

Separately, the external recorder’s --verify-cache audit re-measures a stratified sample, requires exact answer strings, and compares log-probability candidates at aligned emitted positions under the frozen tolerance before writing claims. Distribution-bearing rows must retain their emitted-token identity and shared candidates; the audit does not flatten candidates across different output contexts. Its strata cross registered probes with ordinary and answer-distribution requests. This compares current replies with the sampled stored replies; it establishes neither stability of every cached call nor a guarantee about future server behavior.

The ordinary offline inventory is the dynamically discovered scripts/record_*_claims.py set. scripts/record_confirmatory_replication.py deliberately sits outside it: decision 111’s sentinel records that the held-back population has been spent and refuses another scoring run. Ordinary reproduction preserves that record. By contrast, scripts/record_confirmatory_stage6_clustered_claims.py belongs to core: decision 115 permits deterministic replay of its separately specified estimand on the already-spent population with frozen development fits. Its status remains descriptive; replay supplies no new held-back replication.

The longer commands from the table are expanded here for copying. Run each at its position in the ordered reproduction path above.

Check the formal layer.

(cd formal && lake build && ./Scripts/axiom_report.sh) && .venv/bin/python -m pytest tests/test_formal_isomorphism.py::test_the_negative_control_does_not_compile -v --no-cov

Render the publication. Set TEMPLATE_REPO_ROOT to the explicitly selected template checkout and run the following from the AILM root.

export TEMPLATE_REPO_ROOT
.venv/bin/python scripts/z_render_docs.py --supporting-protocols
.venv/bin/python scripts/z_render_docs.py --check
(cd "$TEMPLATE_REPO_ROOT" && uv run --locked python scripts/pipeline/stage_03_render.py --project working/active_inference_language_model)

Resolve and check individual HTML references. Run these commands from the AILM checkout after Stage 03. Complete supporting HTML acceptance follows the repair; the earlier documentation check covers only the hydrated mirror.

.venv/bin/python scripts/z_render_docs.py --publication-references
.venv/bin/python scripts/z_render_docs.py --publication-references --check
.venv/bin/python scripts/z_render_docs.py --check --supporting-protocols

Check PDF integrity.

qpdf --check output/pdf/active_inference_language_model_combined.pdf && .venv/bin/python -m pytest tests/test_pdf_integrity.py -q --no-cov

Validate the publication.

(cd "$TEMPLATE_REPO_ROOT" && uv run --locked python scripts/pipeline/stage_04_validate.py --project working/active_inference_language_model)

Generated prerequisites must be current before the full suite or release profile starts: those tests consume figure and cover outputs, local web artifacts, both gate-audit reports, and Stage 04 provenance. Hydrated repository documentation is also generated and checked before rendering. The release profile subsequently repairs individual HTML references from the actual combined index and performs strict complete HTML checks before Stage 04 binds final provenance. It regenerates and checks these outputs. Its receipt distinguishes completed checks (success) from release eligibility (publication_acceptance); a run using the development dirty-tree override cannot establish publication acceptance. docs/method/release_checklist.md records the required local model cache, upstream GNN checkout, template checkout, and toolchain configuration. Structural checks do not establish visual correctness: inspect the final PDF’s rendered pages, figure labels, tables, equations, and metadata after regeneration.

The gate-audit and claims-discipline rows of tbl. 21 are the minimum pair a reader should run. scripts/audit_gates.py re-derives the gate classification from measured baselines and ceilings rather than from anything recorded, so it would catch a threshold that had drifted back into vacuity. tests/test_claims_discipline.py scans the real repository tree rather than a fixture, which is what makes it a statement about the shipped artifact (sec. 4.11). Where a reproducibility checklist such as (Pineau et al. 2021) asks an author to self-report a study’s degrees of freedom, this repository’s gate audit and claims-discipline scan check the corresponding properties mechanically against the shipped tree on every run, rather than once at submission time.

Interactive inspection and the reply-generation boundary

The local dashboard exposes input controls and a temporal trajectory beside a chat demonstration (fig. 27). Its input-analysis views and generated reply have different computational scopes: the reply follows the L3-to-L1g policy path described in sec. 4.9. The upper-level input posteriors do not constitute an end-to-end reply-generation cascade. The matrix explorer provides a separate inspection surface. These interfaces support inspection; they do not add an evaluation population to the results.

Open full-size figure

Variable injection and source provenance

All 2577 claims live in data/claims.json, each recorded by the run that produced it with its run_id and source location. src/ailm/manuscript_variables/ supplies the double-brace token map this manuscript is written against. Recorded-value tokens follow a mechanical rule: the token is the ledger label uppercased, so stage1_letter_perplexity becomes STAGE1_LETTER_PERPLEXITY. Provenance companions are generated alongside each value, so a claim’s originating run is available without a separate lookup. Other tokens are explicitly derived expressions or configuration and metadata values; they do not acquire ledger entries merely by appearing in the map.

The failure behavior is the point. A required claim absent from the ledger raises MissingClaimError and the render stops; it does not emit a plausible default and does not leave an unresolved marker in the output, because an unresolved token in a rendered PDF reads as a fabricated result. tests/test_manuscript_variables.py tests that failure path claim by claim, deleting each required label in turn and requiring the error to name it. The consequence for a reader is narrow but real: each ledger-backed value in sec. 5 can be traced to its entry and owning recorder; derived values require the additional calculation in the token writer. The refusal protects required recorded inputs. It does not prove the measurements correct or make arbitrary hand-written numerals impossible.

A concrete trace starts at tbl. 9, which prints the letter-level perplexity as the token STAGE1_LETTER_PERPLEXITY. Lowercasing gives the ledger label stage1_letter_perplexity; its companions resolve to run stage1_letter_gate and source scripts/record_stage1_claims.py. Running that recorder, .venv/bin/python scripts/record_stage1_claims.py, re-records the entry; .venv/bin/python -m pytest tests/test_stage1_gate.py -v recomputes the same perplexity from the vendored counts and re-applies the declared bar; and .venv/bin/python scripts/audit_gates.py re-derives the verdict that bar is judged under. The first two steps of that walk — token to label, label to run and source — are mechanical and hold for every one of the 2577 claims. Only the last step is claim-specific, and it is not guesswork either: the source field names the script that recorded the number. tbl. 21 identifies the ordinary recorder phases; the one-shot replication is retained under its separate protocol above.

Selected mathematical claims are stated and proved in Lean 4 under formal/: the gate taxonomy of eq. 10 and eq. 12, the pre-registered sweep-shape predicates of sec. 4.10, both halves of eq. 6, and the Bonferroni arithmetic behind tbl. 36. The layer ships 149 named theorems and 34 witness examples with a recorded formal_sorry_free verdict of true, and it cannot drift from the recorded measurements: the one ledger-facing module is generated from data/claims.json, byte-identity-checked on regeneration, and every ledger-facing theorem that fixes a threshold closes by decide, so a re-record that moves a measured value across a declared threshold stops the corresponding proof from compiling. What the layer does not establish — that any recorded number is correct — and the rest of its deliberate scope boundaries are set out in sec. 14.

Deterministic build epoch and figures

This document was rendered at 2026-10-27T02:45:22Z from version 1.0.0. The timestamp honors SOURCE_DATE_EPOCH; when it is omitted, the manuscript orchestrator derives the epoch of the last audited commit that touched an input outside the regenerable output/ tree, so the build stamp remains reproducible along with everything else and a commit that only refreshes rendered output does not move it. Figures are written deterministically by src/ailm/figures/ with fixed layout and recorded inputs. Where a figure validates a bootstrap statistic, it uses the registered seed. Numeric content is drawn from the same recorded values as the prose. Sharing that source removes a separate transcription step; correct token selection, current generation, and rendered figure/table agreement still require their own checks.

One caveat on exact equality: pymdp computes in float32 under JAX, so analytic anchors are compared with recorder- or test-owned tolerances rather than exact equality. A uniform transition over 39 phones measures marginally below 39, not exactly it. That float32 premise is recorded, not assumed: jax_enable_x64 is recorded as false, preserving the default float32 path used by the shipped pymdp agents. The Stage 0 recorder pins the live flag at record time (env_jax_enable_x64, alongside the JAX, jaxlib, and pymdp versions and the platform) and refuses to record with JAX’s 64-bit mode enabled, since a flipped flag would change the numeric story, not merely the rendering. tests/test_pymdp_smoke.py asserts the recorded value still matches the live interpreter after importing the pymdp path, so the recorded premise cannot silently outlive the environment it describes. Reproducing the figures to the byte requires the same matplotlib and font stack; reproducing the numbers requires only the pinned environment.

Availability and retained upstream licenses

AILM’s own code (src/, scripts/, tests/, formal/, and gnn/) is licensed MIT; the manuscript, documentation, figures, and other authored data are licensed CC BY-SA 4.0. The vendored count artifacts keep their own, narrower upstream terms, which neither AILM license relicenses: the Brown-derived counts carry NLTK’s non-commercial package terms, the Czech-PDTC artifact is CC BY-NC-SA 4.0, the English EWT artifacts are CC BY-SA 4.0, and the CMUdict counts retain the upstream BSD-2-Clause notice. THIRD_PARTY_NOTICES.md inventories each component with its transformation, and LICENSE states the commercial-use boundary the two non-commercial artifacts impose on the distributed wheel.

The recorded environment names an arm64 workstation (macOS-27.0.1-arm64-arm-64bit) and the pinned JAX precision setting. The CUDA extra is an untested install path. These fields do not record device placement for each computation and do not establish CPU-only execution of the separate Ollama model server. No wall-clock performance claim is made here.

The work has a single author, who wrote every gate the audit reports on and every control that tests it. The external scrutiny to date is the audit and cross-vendor model reviews recorded in the decisions log, which the author commissioned and answered; no independent reproduction of the checkout is recorded in this evidence.

The work received no funding: there are no grants and no external funding of any kind. The author has no competing interests to declare.

sec. 6.1 states what that single-author position means for the gate-audit method’s own evidence.