Overridden revision rights may make accurate model corrections provoke deliberate errors

The train of thought that ends in this hypothesis. Each stage is the reason the next exists. The master question narrows to a goal, the goal to an unknown nobody has closed, the unknown to the hypothesis proposed here. Every step below says what it rests on and what carries it.

Two live hypotheses pull in opposite directions here, and the field has not chosen between them.

Do human–machine retellings need a new explanation, or can existing accounts predict how meanings change in unfamiliar settings?

The engine's own statement of the hypothesis, in full.

HERETICAL CANDIDATE: lineage-specific resistance to accurate repair. In a recurring human–AI collaboration, a human's earlier successful correction establishes an implicit right to determine that lineage's meaning. When a later model correction overrides that right, the human deliberately produces a competing interpretation, even when they can privately identify the source-correct interpretation. A history of being overruled, rather than the current story or an error-prone memory, changes the production objective. The substrate is an agent–lineage commitment, operationalized through consequential acceptance/rejection and subsequent edits, not inferred from introspective explanations alone. The extra dependency is R(person, lineage, history of exercised revision rights): P(next proposition | current artifact, full supplied context, R) differs when R is experimentally changed while artifact, exposure, model accuracy, motor work and available source evidence are matched. It predicts a paradoxical negative causal value of additional accurate corrections in the threatened-rights condition. This is a conjecture about deliberate semantic counter-production, not evidence that human–AI chains generally need a new state.

Every other hypothesis the engine wrote for the same gap, and the observation that would separate the two.

• Rival 01 of 04

  Conflicting revisions may erode connected story memories even after the text is repaired
  What would separate them

  Conflicting revisions may erode connected story memories even after the text is repaired predicts: Use graph-matched fictional narratives with experimentally known causal links. Randomize whether an equal number of incompatible intermediate corrections repeatedly touches one connected neighborhood or dispersed unrelated links; restore the identical correct full text at a checkpoint and equate final exposure, total conflicting propositions, task time and output tokens. With the same human returning, the localized condition should show accelerating, spatially adjacent causal-role failures predicted by independently estimated a_t and revision-load amplitude, despite matched checkpoint text. Fresh humans should reset that excess; reinstating a gesture without repairing the affected bindings should not. Estimate load and binding accessibility in separate calibration participants to avoid the diagnostic test becoming retrieval practice. Compare the law against arbitrary flexible item-level learning/interference models, graph-conditioned one-step H and A, repeated-individual reconstruction, and exposure-position controls. A stable power-law relation fitted on one load/graph range must predict another without refitting its exponent. If endpoint text, ordinary interference and causal connectivity explain the trajectories; if growth is unrelated to connected damage; or if a_t merely redescribes the same scored errors, reject fatigue as a distinct mechanism. A good curve fit alone is insufficient.

• Rival 02 of 04

  Ordinary transformations may explain retelling chains without an extra recursive state
  What would separate them

  Ordinary transformations may explain retelling chains without an extra recursive state predicts: Freeze nulls and their uncertainty before seeing evaluation chains. In human-only, model-only, HA and AH chains, held-out proposition transitions, causal-role reversals, exception loss and private-source retention fall inside the propagated predictive envelope, and any candidate extension improves proper predictive scores or prespecified discrepancies by less than the smallest meaningful margin. Order effects are allowed. At an identical-current-artifact checkpoint, matched context, resources and independently measured ordinary learning explain later differences; no additional lineage-right, local-fatigue, motor-history or joint-contract state earns predictive value. Intervene on ancestry access and source regeneration with explicit resource matching: context-conditioned kernels predict the changes without chain-specific refitting. Under predictive equivalence on independent seeds and genuinely held-out contexts, remove the distinct recursive family's novelty and priority, while retaining the observed attractor phenomenon. This IH loses if a reproducible intervention-specific discrepancy exceeds uncertainty and the meaningful margin after competent state enrichment and absolute model checks; that loss does not automatically identify which extension is right.

• Rival 03 of 04

  Reinstating learned gestures may preserve causal roles during human–model retelling
  What would separate them

  Reinstating learned gestures may preserve causal roles during human–model retelling predicts: At an identical-artifact checkpoint, cross semantically congruent role enactment at encoding with matched or swapped spatial enactment at later human production. Include no-enactment and equal-amplitude meaningless-movement controls, the same verbal generation and source-question practice, matched delays and workload, and fresh-human handoffs. Gesture instructions must not reveal any missing proposition; assign counterbalanced arbitrary locations to already supplied characters. This IH predicts an encoding-by-reinstatement interaction: congruent motor reinstatement selectively preserves the earlier causal roles, whereas swapping the learned locations increases role reversals even with the same current text. The interaction should persist after balancing ordinary verbal generation/retrieval practice and be absent for an unlearned movement mapping. Fatigue predicts localized cycle-dose deficits and fresh-person reset, but not this sign-changing mapping interaction; the rights and contract accounts predict their social manipulations instead. Calibrate an ordinary multimodal encoding-specificity model on nonrecursive tasks and replay controls. If it predicts the entire chain interaction, the motor explanation may be useful but the proposed new recursive family is eliminated. If matched motor perturbations have no meaningful role-specific effect despite a successful action-memory manipulation, reject this scout in favor of other models.

• Rival 04 of 04

  Continuing evaluation with coarse checks may reward strategic omissions in retelling
  What would separate them

  Continuing evaluation with coarse checks may reward strategic omissions in retelling predicts: Cross continuation of the same evaluation relationship versus a one-shot handoff with coarse whole-story evaluation versus prespecified proposition-specific audit. Hold expected reward, current source, evidence access, output length/time, task wording and candidate policy fixed as far as feasible; report residual incentive differences. Explicitly distinguish descriptive accuracy from public approval. The contract account predicts more omission of auditable exceptions/attributions under continuing relationships with coarse verification, and a selective reversal under item-level audit. The expected gain from possible later reinterpretation should predict WHICH details disappear, even when those details are causally central, easy to recall and accurately answered in private. Remove future evaluation or assign liability to an independent editor while preserving authorship rights: strategic omissions should shrink; merely transferring revision ownership should not suffice. Compare with independently calibrated one-shot incentive, audience-design, risk-aversion, self-presentation and accountability kernels composed across rounds. Only a held-out continuation-by-verifiability effect beyond those components supports the proposed extra state. If the effects are fully predicted by ordinary task-conditioned editing, remove the recursive/new-family claim. No contract-specific selectivity, despite verified incentive comprehension, falsifies this mechanism in the task.

The engine is asked to say what its hypothesis would overturn and what would surprise a specialist. This is its answer.

Which of the figures above have a study behind them, which are the engine's own, and what it would take to refute the hypothesis. This audit never judges the idea.

Provenance audit: failed at enrich. Nothing below has been traced yet.