Established Findings and the Next Independent Tests

AILM now combines explicit GNN-declared components with a live conditional message schedule. The connected GUM result, the historical component experiments and the gate-audit method support different conclusions and retain their own populations, comparators and limitations.

The six modeled levels are built and their outcomes recorded; one of the six verdicts is qualified. The clause level clears a gate that was registered before its artifact existed, on an observation alphabet widened past the case roles, and its privileged-label gain resolves away from zero under a paired whole-document bootstrap. That gate is a development-session specification and has no held-back replication, because the held-back partition was already spent. The case-only clause model it displaced is retained as an ablation: its gate is non-passing, its privileged-label gain is unresolved on both reported scales under the same bootstrap, and its detectability control recovers a larger planted effect but supplies no equivalence claim (decision 83). The document-position (tercile) level — narrative in code — has a positive point estimate in a direction timestamped before its measurement, but its document-clustered primary interval spans zero and is non-confirmatory (decisions 25–26). The same estimand, registered separately under decision 115 and carried to the held-back split, gives 0.008288 nats with an interval that excludes zero at the development coverage and spans zero at the family-corrected one, on a population whose own resolution, 0.005726 nats, is coarser than the development-sized effect (sec. 5.8.2); the development result is not retracted by it. The level’s sentence-sign statistic is secondary and nominally positive; it crosses neither the fixed Bonferroni threshold nor its Holm step-down threshold, the fixed threshold being the one 7 of 16 declared tests cross. sec. 5 holds the letter and phoneme measurements and points to where each later level’s measurements are recorded. All level builders produce ordinary pymdp agents, but among the historical stage scores only L1’s headline filters live; the later levels’ exact or NumPy score paths and the live-Agent checks that license them are inventoried in tbl. 3 and sec. 4.11. Two of those checks are exhaustive: L5 uses an exact closed-form mixture with a live-Agent posterior-agreement check on the full held-out set that is not score equivalence, and L4 uses a NumPy filter that the live Agent reproduces at every held-out position.

The historical stack first established representational hierarchy: L2B infers over a variable L1 represents, observing the alphabet L1’s states are drawn from. The historical standalone decoders exchange no beliefs. The seven registered receiver interventions are built on that composition: five measured separately, the case-word bridge measured as an exploratory prior-only sweep on the development population, and its privileged-oracle diagnostic the one registered seam that records no estimand. The measured ones disagree in sign. The L3→L2B lexical channel and L1→L2B coupling enter the same letter-to-phone receiver but differ in sender: the lexical channel helps (+3.333 pts at p = 3.756e-102), the coupling harms (moving accuracy -0.2639 pts at p = 1.547e-06). L3→L1g, the lexicon-to-letter preference used by the recorded generation run, helps without a paired significance test behind it. The independent L6→L5 and privileged punctuation-type-oracle→L3 interventions are less clean and stay on the record as such. L6→L5 moves the case-only clause model’s D prior against a tercile-scrambled control, in a receiver whose privileged-label gain is unresolved. The independent oracle→L3 word-prior intervention is a corrected helpful result on both directional summaries that falsified its predicted harm (sec. 4.10). It is not evidence about an L5 output channel. These experiments were not executed as a connected cascade. The subsequent connected runtime executes explicit conditional messages and live next-word policy selection. Its locally frozen GUM comparison spans 22 previously unexamined documents after excluding 8 development documents. The mean paired normalized-character log-loss gain is 0.4420177562251402 nats with interval [0.42010385279024465, 0.46325671494396264], indicating lower integrated loss. Full-stream word loss remains infinite because out-of-vocabulary tokens receive zero word probability. These are results for a fixed conditional schedule and normalization, not a globally exact joint posterior or external replication (sec. 5.1).

Comprehension filtering alone does not distinguish the system from classical Bayesian state estimation. Every modeled level has a flat C and a single action in comprehension, so no policy is scored and no expected free energy is computed. L1’s headline and the bounded L2 verification paths execute live filtering; that recursion is the forward algorithm of a hidden Markov model, while the L2 full-set and later headline scores use the exact or NumPy paths just enumerated. This is precisely what lets a plain-NumPy HMM serve as the implementation-independent reference decoder at the checked levels (sec. 7). The additional policy-selection mechanism is exercised in generation, where the letter transition is made controllable and the lexicon supplies the only non-flat preference vector in the system, so the emitted symbol is selected by minimizing expected free energy over policies (sec. 4.9). Even that one place is narrower than it sounds, and saying so is a result rather than a concession: because the controllable transition is deterministic, the information-gain term is identically zero and policy selection reduces analytically to a preference-weighted softmax over the habit (eq. 17). The measurement is reported with a control that restores the term to its analytic ceiling, so the claim is that the formalism contributes nothing beyond that softmax here, not that it could not elsewhere. A transition that is both stochastic and action-dependent would put the term back and let it bear on the choice; the uniform-transition control fixes the ceiling such a design works within, and the word-state construction of sec. 4.9 shows that uncertainty without action-dependence buys nothing. The budgeted-attention arm (sec. 6.1) already measures the case where that term is the whole policy. The gate-audit findings that follow concern these filtering models and the thresholds written for them; they are not a verdict on active inference as a general framework.

The more transferable results are the gate-audit method and the negative findings. A load-bearing modeling decision, which slot the learned matrix occupies, was wrong in a way the library could not detect. pymdp validates more than normalization: it checks array shapes against declared factor dependencies, index bounds, and control consistency. What no structural check reaches is semantics. A column-stochastic bigram is column-stochastic whichever slot it occupies, and nothing in the library knows whether an axis was intended to mean next given current or its transpose. The wrong wiring was not loudly broken but quietly anti-informative, and it slipped under a threshold that had never been checked against either a trivial baseline or an achievable ceiling. Then the same class of oversight produced two opposite gate defects: one threshold no correct implementation could ever reach, one that a lookup table already cleared. The detection method is the same in both directions, and it is cheap: measure what a model doing none of the work scores, measure what the data allows, and accept a threshold only when the first fails it and the second clears it.

That check is expected to generalize past this project and past active inference, a working hypothesis in the sense of sec. 2: its evidence is every declared gate of one repository, written and audited by one author, and nothing outside it has yet been tested. It would apply to any staged or gated build whose progress is defined by a numeric criterion: accuracy floors in a test suite, latency budgets, coverage thresholds, benchmark targets in a research program. The taxonomy of sec. 4.2 needs only three measured quantities, two of which can usually be obtained before the system exists, and the checklist there is written to be run before the build rather than after the result. scripts/audit_gates.py is what makes it a re-runnable artifact instead of something someone happened to do twice; the same shape would fit any repository with declared thresholds.

The check was then turned on a threshold of our own, which is the harder half of the same lesson. The benchmark originally refused to report a calibration number when too little of a model’s reported distribution was observed, and that floor was declared on an argument rather than measured. Measuring where it belongs put it at 1.9826100924184078 times the declared value, and at a mass the serving interface cannot supply for 0.395 of positions: the floor was not conservative, and no reachable gate of that shape would be (sec. 13). The instructive part is how nearly the measurement endorsed the wrong answer. Both metrics’ rule disagreement crosses zero once, essentially at the declared floor, while the two reconstructions are still far apart; a locator that took the first crossing would have confirmed 0.5 by a coincidence of opposite biases. A threshold that survives its own audit is not thereby correct, and the audit needs its own negative control.

One reporting change follows from the same discipline. Every interval-spanning paired result in this project is stated as an interval rather than as an absence, because “no effect was found” is weaker than the data support and hides the distinction that matters: no near-null arm in the plotted paired-bound roster produced enough discordant pairs to support an equivalence claim, so each bounds its effect and says nothing about absence (sec. 15.3). The one arm that once did, the lexical channel at its highest tested weight, became a decided effect when its backoff defect was corrected (decision 110), and the roster now holds bounds only. A bounded effect and an unresolved one can read alike in a table; separating them is the reporting result.

The project’s other negative result is about its own headline architecture. An inter-level message, L1’s predictive over letters folded into L2B’s observation likelihood, was built rather than assumed, and it moved accuracy by -0.2639 points against the uncoupled decode, a loss significant at paired McNemar p = 1.547e-06. The channel works; it simply carries nothing L2B does not already observe. Reporting that is the point: the mechanism had been asserted in this project’s own documentation before anyone measured it, which is the same failure as an unfalsified threshold, one layer up. By contrast, the L3→L2B lexical channel was prospectively specified in the same development session, built, and measured as helpful (3.333 pts), so the novel-information criterion (decisions 18–19) is falsifiable, and on that pair — same receiving level, opposite senders — its prediction matched the development-population result. Its next test went the other way. The privileged punctuation-type-oracle→L3 word-prior intervention, which the criterion itself named as its cheapest falsification and predicted would harm, instead moved the word prior’s held-out perplexity slightly in the helpful direction throughout the tested positive grid; the best tested weight is its high endpoint, not a located optimum (3.268023 → 3.263007, decision 27 and sec. 5.10.3); the token-direction tally agrees, with 51,364 improved against 22,649 made less probable. The predicted harm was not supported, so the criterion’s first clause is qualified on that channel. One prospectively specified supporting result and one corrected result that falsifies the criterion’s prediction leave a criterion under test, not an established one.

Three subsidiary lessons carried real cost here. A reproducible evaluation subsample is not a representative one, and an unbiased subsample of the same size can be too small to establish the effect it correctly measures. The resolution was to remove the constraint that forced subsampling: require an implementation-independent oracle and the pymdp decoder to agree on the bounded comparison subset (0 disagreements), then compute full-set statistics. A verification gate that runs only on synthetic fixtures constrains nothing about the shipped artifact: the claims scanner had never been pointed at real prose, and an unbacked claim sat in the repository’s own README for an entire stage while every test passed. A failure signature that catches the mis-wirings you thought of is necessary but not sufficient: an emission normalized along the wrong axis cleared the baseline comparison while being wrong, and only a column-sum invariant and numerical agreement with the oracle pinned it down.

The staged-gate method’s own description was inaccurate, which is why sec. 2 states the qualification alongside the claim-strength labels rather than leaving it to this section. The original Stage 1–4 gates were restated after those levels had been built or measured because their thresholds were vacuous, unreachable, or named the wrong population (tbl. 22, tbl. 27). Later declarations were also re-scoped during audit. Stage 6’s direction was registered before its held-out measurement, but the development population and analysis family were reused and its clustered primary remains non-confirmatory. The repaired gates and controls are more rigorous, but they do not turn the historical process into sequential validation, and no multiplicity correction repairs population reuse. A rerun of the frozen family against a population never inspected while the gates were written was pre-registered, executed to its first data touch, and retired by exhaustion for the external_lexicon members (decision 91); the ud_ewt_test members were then scored exactly once against the held-back EWT test split under decision 111 (sec. 10.6): two clear the frozen per-test bar in their recorded direction, two yield no discordant pair, and the other twelve members of the family remain development-population inference. The supplements that follow — the gate-audit trails and the confirmatory replication, the channel factorial, operation and byte counts, the registered external probes read as a reference frame rather than a competitiveness claim, the machine-checked Lean layer, and the statistical apparatus the paired results rest on (sec. 10, sec. 11, sec. 12, sec. 13, sec. 14, sec. 15) — carry the detail behind the claims and qualifications stated here. Naming the defect is not the same as fixing it: the same honesty the method claims for its measurements is owed for its own process.

What travels beyond this one repository is the pair the introduction opened with: an explicit discrete generative language system whose probabilities, preferences, habits and policy precision are inspectable, and a gate-audit-and-claims-ledger method for holding a staged build to its own thresholds. Neither depends on active inference to be useful, and neither depends on this corpus, this language, or this author’s own gates having been the ones checked. The next test of either is a build this project did not run: a second staged system, audited by someone other than its builder.

What comes after this paper follows the same three commitments the abstract opens with — a generative model declared in Generalized Notation Notation, an Active Inference formalism that turns that declaration into filtering and policy selection, and a grain of linguistic description the declaration is built along — rather than abandoning any of them for scale. That combination is what makes the open work in sec. 6.4 proposable in the terms this paper already uses: a new level, a new language, or a new cross-implementation render is another GNN declaration and another conformance check, not a different kind of artifact; a globally normalized hierarchy would require a new joint specification and fresh comparisons beyond the conditional connected runtime measured here; and a composition with a large language model, in either direction, is answerable to the same discipline that kept this project’s own fitted-probe proposal from certifying its own circularity. None of that is claimed to have happened. It is what the method this paper reports is for.