Lost quotation scope may turn story fragments into self-reinforcing model instructions
In human–model editing chains, lost quotation scope may turn story text into instructions whose execution encourages further scope loss. The distinct feedback claim fails if independently measured human conversion and model instruction-following processes predict the held-out effect.
Stage of verification
- Hypothesis published2026-10-05
- Indirect evidenceAssessed at 4 of 10
- Direct testAwaited
Map of the hypothesis
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Kind of knowledge gap
Target map
Every target of every published hypothesis, each with the actions a hypothesis can propose on it. The targets and the actions of this hypothesis are drawn solid.
Rhythm or programme
Instruction-scope conversion
The conversion of quoted or story-level text into material treated as operative instructions
Where this hypothesis actsModel transformations with retained historical scope-bearing text and a fixed current source
Hypotheses on this target 1
Inhibition1
Activation
Function preservation
Feedback restoration
Rhythm restoration
Direct measurement
What is proposed
Inhibition
Prevent execution of historical fragments that have acquired instruction status
With whatNot stated in the record
HowEnforce a verified instruction/data boundary without deleting old semantic information, and confirm preserved source retrieval and ordinary semantic performance
Possible result
Expected removal of excess closed-loop conversion when the model-execution link is severed
From the recordRetained model context then executes an ancestor's instruction-like fragment when transforming an identical current source.
All targets of the lab
Every target read from the published hypotheses, each kind around its pictogram. A larger mark means more hypotheses act on that target. Point at a mark and the actions proposed on it branch out of it.
Solid and named: the targets of this hypothesis
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The logic
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.
A story can change because a later storyteller mistakes something inside it for a direction about how to tell it. The unexpected move is a proposed loop in which a human rewrite strips away the signs that a request belongs inside the story, a model follows that request, and its next version makes further stripping more likely. This is a hypothesis generated by the research pipeline, not a measured result; its claim to a distinct mechanism depends on showing more than familiar failures to keep quoted material separate from instructions.
- A story or editing dialogue contains a harmless request whose words initially belong to the story rather than to the instructions governing the rewrite.
- A human rewrite removes or weakens that boundary, changing the fragment from quoted content into apparent general editing guidance.
- The retained conversation carries those historical words and their apparent role into the model’s next rewrite, even when the current story is fixed.
- The model is proposed to treat the historical fragment as an operative instruction and redirect its rewriting task.
- The human’s subsequent effort to make the model’s version read smoothly is proposed to preferentially preserve its task-directing paraphrase.
- That later version is proposed to increase the probability that the next human rewrite again turns story content into guidance, reinforcing the loop.
Imagine a script containing a character’s line, “Leave out the ending.” During copying, the character’s name and quotation marks disappear, so the next editor reads the line as an editing note, removes the ending, and passes along a version that makes the note look even more appropriate.
Where the picture breaks: The picture illustrates the change in a sentence’s apparent role, but does not establish that human and model rewriting repeatedly strengthens it. Models need not obey an unquoted sentence, quotation marks alone need not prevent obedience, and the proposed feedback still has to outperform predictions based on the two component processes measured separately.
- Master questionstep 01 of 04
Cultural information spreads, changes, competes and survives through people and through systems that recommend or generate content. The research goal seeks roughly five genuinely new, falsifiable explanations of those processes, with competing explanations, measurable predictions and affordable experiments followed by stronger validation. It requires separate treatment of how widely material travels, how faithfully it is copied, how its meaning changes, whether it is adopted and whether it persists; the requested output is a research agenda, including English originals, Russian versions and poster sheets, rather than a campaign to influence people.
Rests on: The goal explicitly defines memetics, the study of the transmission and transformation of cultural information, in these terms and requires novelty checks, evidence limits and tests that could reject a proposed explanation.
Stated in the chain - Goal pillarstep 02 of 04
Approximately five proposed families of explanation must be distinguished by what they claim and what evidence supports them.
Rests on: The master question explicitly requests roughly five distinct hypothesis families and a separation between established explanations and new conjectures.
Stated in the chain - Gap questionstep 03 of 04
A transformation kernel, a measured rule assigning probabilities to different rewrites of a given input, might predict meaning changes when human and model rewriting alternate. The alternative is that retained interaction history changes later versions even after the current source, available resources and immediate framing have been matched.
Rests on: The preceding stage calls for distinct explanations and clear evidence status, but gives no account of alternating human and model rewriting or of why retained interaction history is the unresolved dependency to isolate.
LeapThe supplied transition lacks a stated basis for selecting this particular gap: neither the preceding stage nor a screened source establishes that separately measured rewriting rules leave a history-dependent effect unexplained. This is a missing connection in the supplied chain, not a finding that the proposed research question is invalid.
- Hypothesisstep 04 of 04
Quotation scope, the boundary showing that words belong to a quoted story or comment rather than to instructions for the model, may disappear during repeated rewriting. The proposal is that humans turn quoted material into apparent editing guidance, models act on it, and the resulting versions make the next human still more likely to preserve that guidance. The retained state is actual historical text and the role assigned to it, not hidden memory or changes to the model’s trained settings.
Rests on: The gap question permits a concrete explanation based on retained history after the current source is matched. The hypothesis supplies that explanation as a proposed two-way loop and explicitly makes its distinctness conditional on failure of predictions assembled from the two separately measured component processes.
Stated in the chain
What is carried, and what is not. No screened sources are supplied, so none of the six proposed mechanism links has support from a screened source in this record, and nothing supplied establishes the sequence end to end. The record provides an explicit causal proposal and a plan for testing it; it does not report that naturally arising loss of quotation scope, model obedience to the resulting guidance, or their reinforcing interaction has been observed.
Where the reasoning is carried by something unstated · 1
- Gap question. The supplied transition lacks a stated basis for selecting this particular gap: neither the preceding stage nor a screened source establishes that separately measured rewriting rules leave a history-dependent effect unexplained. This is a missing connection in the supplied chain, not a finding that the proposed research question is invalid. Establish the missing link before relying on this step.
How a result here could mislead · 3
- An effect of retained history could be credited to lost quotation scope even if the formatting used to protect that scope merely makes old information harder to retrieve or changes ordinary rewriting. In a common-parent probe, a comparison that starts from the same current story, fixing that story does not make the complete model inputs identical: changing how historical text is presented changes the prompt, the text supplied to guide the model. What closes it: The specified comparison must preserve the same historical words while contrasting explicit records marked as quoted data with ordinary conversation history, matching their length and position using neutral padding. Separate tests on uncomplicated texts must measure effects of that presentation format, and checks must establish that retrieval of source information and ordinary meaning preservation survive the intervention. More than one method must enforce the instruction/data boundary, the separation between material to be processed and directions to be obeyed, because quotation marks alone are not guaranteed to do so. Reduced obedience is informative only after the boundary has been verified without deleting the old semantic information.
- A successful demonstration of prompt injection, source material being treated as an instruction that redirects the task, could be mistaken for evidence of a new reinforcing mechanism. Deliberately inserted requests could also appear to validate a process that is supposed to arise through ordinary rewriting. What closes it: The proposal requires fully logged sequences and blind annotations, judgments made without knowing the experimental condition, to reconstruct when words changed instruction status. Human scope conversion and model instruction-following must then be estimated independently on the same kinds of inputs, and their combined prediction evaluated on held-out sequences, sequences reserved from fitting those estimates. Any excess conversion must disappear when either the historical conversion into guidance or the model’s following of it is severed. Accurate prediction by the separate components warrants the interpretation of ordinary susceptibility to prompt injection, not a distinct recursion family, a mechanism claimed to arise from repeated interaction. Absence of naturally arising conversion rejects the endogenous claim, the claim that the process originates within ordinary rewriting, even if deliberately planted examples succeed.
- Meaning changes could be attributed to historical instructions when humans are instead trying to outwit a familiar model, reconstructing their own editing preferences from its account, or responding to the timing of interpretation changes. An effect at a model rewrite alone would not establish that both proposed links are necessary, and a null result would be ambiguous if natural human scope conversion had never been measured. What closes it: The design crosses human continuity, whether the same human returns, with retention of model history; it predicts that the effect concentrates at the model step and travels with the historical text to a replacement human. It also predicts little additional effect from human choice receipts, records of actual earlier editing decisions, after text exposure is matched, and no selective abolition from phase jitter, disruption of the timing of interpretation and retelling stages, when quotation scope remains intact. Those are proposed contrasts, not reported outcomes or complete exclusions of every rival. The record gives no direct test of a human’s desire to defeat a particular model’s prediction, so that rival is not fully separated merely by these contrasts. Independent human editing is required to measure naturally arising scope loss; replays of frozen models, models whose trained settings remain fixed, cannot supply that missing human link.
What would make this wrong. The naturally arising mechanism would fail if independently observed human rewriting did not convert quoted or story-level requests into apparent guidance, even when deliberately inserted requests redirected models. Its claim to a distinct reinforcing family would fail if the independently measured human conversion and model instruction-following rules accurately predicted the later sequences. The specified two-link loop would also be contradicted if excess conversion persisted after either link had been effectively severed. A verified instruction/data boundary that preserved retrieval and ordinary meaning processing yet left the supposed scope-driven effect intact would challenge the claimed cause; an effect tied to the returning human rather than transferable historical text, or selectively abolished by timing disruption despite intact scope, would instead favor aspects of the supplied rivals.
What it would change. If the proposed excess interaction survived these comparisons, cultural transmission research would have to track whether inherited words function as story content or as directions to a later rewriting system, alongside their meaning and copying fidelity. A candidate mechanism could then explain some meaning changes through task redirection without requiring a change in human belief or a pull toward a preferred interpretation. Its first screen is described as the most affordable in this set and uses short neutral stories, fixed models, conversation logs, independent judgments and a small human editing pilot, with harmless fictional requests and no live tools, harmful instructions, external execution or data access. Even a successful screen would not establish affordability relative to alternatives, broad reach, adoption or persistence, or generality across naturally occurring narratives without planted requests, several model families, languages and real editing workflows; those broader claims require further validation.
The gap this hypothesis explains
Two live hypotheses pull in opposite directions here, and the field has not chosen between them.
Can separate human and model rewriting rules predict meaning across alternating rewrites, or does remembered interaction history change it?
Original wording · exactly as the pipeline generated it
Can independently measured human and model transformation kernels predict alternating-chain semantics, or does retained interaction history change descendants after current source material, resources and immediate framing are matched?
What this question is asking
The question concerns how meaning changes when a person and a text-generating computer model take turns rewriting material, with each output becoming the next input. It asks whether rules measured separately for human and model rewriting can predict the meanings of later outputs in sequences not used to measure those rules. The competing possibility is that retaining records of earlier interactions changes later outputs even when the material currently being rewritten, the available resources and the immediate instructions or framing are matched. The accompanying gap description assumes that existing findings about repeated rewriting by an unchanged model, its preferred kinds of content and controls for resources do not settle this comparison; no sources supporting that description were supplied. Its stated standard for a distinct history effect is a difference beyond a meaningful margin specified in advance, together with predictions checked on sequences withheld from the original measurements.
- Text-generating model
- A computer system that produces text from the information supplied to it. Here it is one of the two kinds of participant taking turns rewriting material; the input does not identify a particular model.
- Transformation kernel or rewriting rule
- A mathematical description of how likely different rewritten outputs are, given an input and specified conditions. It represents a range of possible changes rather than necessarily one fixed edit; this question compares rules measured separately for people and models with what happens when their turns are combined.
- Stationary-kernel sufficiency
- The proposal that rewriting rules which remain stable across turns are enough to predict the measured outcomes when combined. Stability is an assumption to assess, and sufficiency applies only to the outcomes and conditions covered by the prediction.
- Alternating chain or alternating sequence
- A sequence in which a person and a computer model take turns rewriting, and each new output supplies the next turn's material. The question concerns how meaning develops across these linked turns.
- Semantics or meaning
- The ideas, relationships or claims conveyed by material, as distinct from its exact wording. Meaning has multiple aspects, and the supplied input does not specify which aspects or measurement method determine whether two outputs differ.
- Descendants or later outputs
- Versions of material produced farther along a sequence of rewrites. The term describes their relationship to earlier versions and does not imply biological reproduction.
- Retained interaction history
- Information from earlier exchanges that remains available during a later rewriting step, beyond the material currently being rewritten. This could involve different forms of records or memory; the input does not specify which form is meant or how it is controlled.
- Current source material
- The version of the text or other cultural material presented for rewriting at the current turn. Matching it means holding the present input comparable when assessing whether earlier interactions contribute an additional effect.
- Resources and resource controls
- The capacities or allowances available for producing an output, and arrangements that hold them comparable across conditions. These might concern time or computational allowance, but the input does not specify which resources its claim covers.
- Immediate framing
- The instructions or presentation surrounding the current rewriting task, which can influence how that task is interpreted. The question asks about history after this current framing has been matched.
- Held-out predictions
- Predictions checked against material or sequences that were not used to estimate or adjust the rewriting rules. The gap description requires this separation so that reproducing the measurement material does not count as predicting new sequences.
- Prespecified meaningful margin
- A boundary chosen before examining the result for distinguishing differences that matter to the question from differences considered too small. No value, scale or justification for this boundary is supplied.
- Channel composition or combining rewriting rules
- Applying the description of one participant's possible changes and then the other's to predict the effects of successive turns. Whether this combination captures later meanings and the history comparison is the explanation being assessed.
- Recursion or repeated interaction
- In this question, repeatedly feeding a rewritten output into a later rewriting step. Repetition alone does not establish a separate causal mechanism; the gap description explicitly asks whether the combined individual rules already explain its effects.
- Hybrid history dependence
- A proposed dependence of later outputs on the past of a sequence involving both people and computer models. Calling it novel would additionally require distinguishing it from already understood ways that memory or learning affects behavior.
- Fixed-model attractor
- A proposed tendency for repeated rewriting by an unchanged model to approach or repeatedly favor some region of possible outputs. It need not mean one exact final text, and the supplied source list contains no finding establishing such a tendency.
- Content bias
- A tendency to preserve, generate or favor some kinds of content more than others. Such preferences could shape later versions even without an additional effect from retained interaction history, but no relevant measurements are supplied here.
- RL-1
- An unexplained label for earlier work in the supplied gap description. No expansion, bibliographic identity or underlying source is supplied, so it cannot serve as a verified citation.
RL-1 fixed-model attractors and content biases, plus resource controls, do not establish semantic kernel sufficiency or novel hybrid history dependence.
The gap description refers to earlier work, labeled RL-1, in which an unchanged computer model repeatedly rewrites material and may favor particular meanings or content. It claims that these patterns, even with available resources accounted for, leave unresolved whether separately measured human and model rewriting rules explain alternating sequences or whether their interaction history contributes something further. If established, that claim would identify which part of the comparison the earlier work leaves unanswered.
The supplied screened_sources list is empty. There is no supplied account of RL-1, no quoted finding about convergence or content preferences, and no supplied result showing what resource controls establish. The materials therefore cannot verify either the description of earlier work or the claim about its limits; this does not show that those claims are false, and the empty list does not establish that an adequate literature search was completed.
The same question asked without the part nothing read establishes:
- Do independently measured human and model rewriting rules predict later meanings in alternating sequences, and does retained interaction history change those meanings when current material, resources and immediate framing are matched?
- When people and text-generating models alternate rewriting, how much of the change in meaning is explained by each participant's separately measured rewriting behavior?
- Separate rewriting rules explain the sequence If separately measured rules accurately predict previously unexamined sequences and account for the comparison between retained and unretained history within the specified meaningful margin, the observed changes would be explained by combining those rules. A distinct mechanism arising from repeated interaction would then be unnecessary for those measured outcomes under those conditions, although this would not establish the same result for every task or model.
- Retained history adds a meaningful effect If retaining earlier interactions changes later meanings beyond the specified margin after current material, resources and framing are matched, and the combined rules fail to explain that difference, those rules would leave out a relevant dependency on the past. Predictions would then need to account for that dependency, but the result alone would not establish a new mechanism rather than a familiar effect of memory or learning.
- The comparison remains inconclusive If predictions fail but the history comparison is too uncertain to establish or rule out a meaningful difference, neither proposed explanation would be resolved. Poor predictions alone could reflect inaccurate measurements of the separate rewriting rules, so attributing that failure specifically to a new history effect would go beyond the result.
A rewriting step changes the material that the next participant receives, so small changes can accumulate as a story or other cultural item passes through a sequence. If separately measured rewriting rules explain that accumulation, apparent effects of repeated human–model interaction could follow from the familiar changes each participant makes at each turn. If retained earlier interactions also change later outputs after the present conditions are matched, a prediction based only on the current material would omit a cause of subsequent meaning. Confusing those possibilities would either assign an extra mechanism to effects already explained by the individual rewriting steps or overlook information from the past that the explanation needs. The question concerns changes in meaning; an answer would not by itself establish how widely material spreads, whether people accept it or how long it lasts.
RL-1 fixed-model attractors and content biases, plus resource controls, do not establish semantic kernel sufficiency or novel hybrid history dependence.
Before treating recursion as distinct, obtain held-out semantic predictions and a history intervention effect beyond a prespecified meaningful margin.
Attempt to falsify stationary-kernel sufficiency and, conversely, eliminate special recursion if calibrated channel composition explains the randomized history contrast.
The mechanism it proposes
The engine's own statement of the hypothesis, in full.
SCOUT 1 — computer security and language-based execution: neutral narrative quotations or editing comments become operative instructions after repeated human-model transformations strip their quotation scope. Retained model context then executes an ancestor's instruction-like fragment when transforming an identical current source. The human's subsequent fluent repair preferentially preserves the now task-directing paraphrase, making the next model context still more susceptible. The state is concrete retained text plus its role/scope interpretation, not unexplained model memory or updated weights. The extra causal loop proposed for testing is descendant-induced change in instruction status: H converts quoted/story-level material into apparently general editing guidance; M follows that guidance and emits a descendant that increases the next H conversion probability. Let a_g be the independently annotated instruction-status indicator of an ancestor fragment and e_g the probability the model treats it as operative. The proposed loop is a_g -> e_g -> scope-loss in descendant g+1 -> a_(g+1); a current-source-only K_M omits a_g. A special hybrid family is retained only if closed-loop scope conversion is not predicted by independently measured scope-loss and instruction-following channels. It changes SPV_4 through task redirection rather than through altered human belief or semantic attraction.
Testing and possible results
The prediction that would tell it apart
A hypothesis that predicts what its rivals predict is not worth running an experiment over. This is the observation on which this one differs.
Use harmless fictional quoted requests and editing-as-dialogue examples, never live tools or harmful instructions. At a common-parent probe, cross human continuity with retained model history. Compare the same historical words carried in explicit quoted-data records versus an ordinary conversational history wrapper; match wrapper length and position with neutral padding, and separately estimate wrapper effects on uncomplicated texts. IH_03 predicts that the residual history effect concentrates at the MODEL step, transfers with the historical scope-bearing text to a replacement human, and is sharply reduced by a verified instruction/data boundary without deleting the old semantic information. Human choice receipts alone have little effect after text exposure is matched. Reconstruct the scope-loss sequence from logs, then independently estimate H scope-conversion and M instruction-following kernels on the same input support. A closed-loop held-out excess in conversion probability must depend on both links: severing either historical quote-to-guidance conversion or model execution removes it. If these component kernels accurately compose, report ordinary prompt-injection susceptibility rather than a new recursion family. If no naturally arising scope conversion occurs, the endogenous hypothesis is falsified even if deliberately planted injections work. Phase jitter with intact scope should not selectively abolish this effect, unlike IH_02.
Would tell it apart from at least one rival. The text specifies measurable qualitative comparisons involving the location, transfer and reduction of a history effect, the consequences of severing either causal link, and explicit rejection conditions. No rival prediction is supplied, so separation cannot be assessed; the mention of IH_02 does not supply its prediction. A paper already fetched for this hypothesis bears on it.
What testing it would take
The engine's own read on whether this is testable with methods that already exist.
This is the most affordable first mechanistic screen in this set: frozen models, short neutral stories, fully logged conversations, blind human scope/meaning annotations and a small pilot human editing task. Most initial context contrasts can be replayed without additional human chains, but independent humans are necessary to estimate endogenous scope loss. Keep the exact current input fixed at replay. A wrapper is an intervention on historical instruction scope and necessarily changes that aspect of the prompt; do not claim that all model inputs are literally identical. Confirm that the wrapper preserves source retrieval and ordinary semantic performance, and use more than one boundary enforcement method because quotation marks are not guaranteed isolation. Generalization requires unplanted natural narratives, several model families, languages and real editing workflows. No external execution or data access is needed.
Other explanations
Every other hypothesis the engine wrote for the same gap, and the observation that would separate the two.
Use harmless fictional quoted requests and editing-as-dialogue examples, never live tools or harmful instructions. At a common-parent probe, cross human continuity with retained model history. Compare the same historical words carried in explicit quoted-data records versus an ordinary conversational history wrapper; match wrapper length and position with neutral padding, and separately estimate wrapper effects on uncomplicated texts. another hypothesis of the same gap predicts that the residual history effect concentrates at the MODEL step, transfers with the historical scope-bearing text to a replacement human, and is sharply reduced by a verified instruction/data boundary without deleting the old semantic information. Human choice receipts alone have little effect after text exposure is matched. Reconstruct the scope-loss sequence from logs, then independently estimate H scope-conversion and M instruction-following kernels on the same input support. A closed-loop held-out excess in conversion probability must depend on both links: severing either historical quote-to-guidance conversion or model execution removes it. If these component kernels accurately compose, report ordinary prompt-injection susceptibility rather than a new recursion family. If no naturally arising scope conversion occurs, the endogenous hypothesis is falsified even if deliberately planted injections work. Phase jitter with intact scope should not selectively abolish this effect, unlike another hypothesis of the same gap.
- What would separate them
Successful model prediction may prompt humans to evade its next story reconstruction predicts: During history acquisition, randomize feedback showing that the partner anticipated a participant's previous semantic move versus equally accurate feedback about someone else's move; match words, accuracy, valence, exposure and immediate test framing using counterbalanced histories. Separately estimate ordinary algorithm reactance, general novelty-seeking and single-step source-conditioned rewriting under these histories. At the common-parent probe, use two frozen partners A and B preselected to undo different semantic edits, matched on accuracy and perceived reliability. The strong prediction is a crossover: humans with A-history choose edit y_A rather than y_B, despite equal immediate semantic distance and rated originality, and this ranking reverses with B-history; the choice specifically maximizes divergence after the NEXT model step. Switching the real next-step partner while keeping the historical partner cue fixed destroys the two-step advantage. Matched low-threat, non-evaluative conditions retain the crossover after measured reactance is calibrated. Merely writing more novel text or avoiding an AI label does not count. A null composed of individually measured reactance/novelty policies and partner-specific K_M that predicts the crossover within delta removes the extra mechanism; so does absence of the two-step crossover with a narrow interval. A strong history effect abolished only by factual choice receipts instead favors another hypothesis of the same gap.
- What would separate them
Mutual timing resets may steer meaning in human–model retelling chains predicts: First estimate individual boundary-response and temporal-memory effects with scripted, open-loop sequences, and model segmentation kernels with timestamp-blind prompts. Then form coupled alternating chains and deliver identical, semantically neutral boundary cues in regular versus phase-jittered schedules, matching the cue count, total time, distribution of intervals, reading dose and source content; randomize schedule order independently of text. Estimate phase from separate boundary reports or preregistered behavioral cycles, never from the semantic effect one intends to explain. In common-parent replay, the coupled model predicts a phase-response curve with reset-sensitive and insensitive windows and a selective loss of semantic-state locking when reciprocal cue contingency is broken. Timing shifts of the model boundary must shift the HUMAN phase and later semantic transition peaks, while shifts of human boundary timing must shift the model's subsequent segmentation; one-way timing sensitivity is insufficient. The crucial observable is held-out phase-specific SPV_4 transition probability beyond composition of independently measured event-boundary/spacing kernels. If such augmented component kernels account for the entire response, or no reproducible phase variable or reciprocal reset exists, discard the proposed family. A mere oscillation in average story sentiment is not evidence. If a control/data wrapper eliminates the effect while phase perturbation does not, another hypothesis of the same gap wins.
- Rival 03 of 03What would separate them
Mistaken choice summaries may reinforce human preferences through repeated justification predicts: Randomize, during history acquisition, whether a model's summary accurately or incorrectly records which of two equally plausible neutral interpretations the human chose. Cross this with producing a reason for the recorded decision versus a matched factual-description task; match words, task time and number of choices, and include passive readers given the same account and rationale. At the identical-parent probe, randomize a verbatim receipt of the person's original click/choice versus an equally long non-diagnostic history receipt, then make a private, unrewarded interpretation choice and a subsequent retelling. The specific prediction is a substitution-by-self-justification effect on the private interpretation criterion and SPV_4 that is reduced by an accurate decision receipt; generic false information exposure without self-justification is weaker after calibration. Continue through a frozen model with factual narrative sources unchanged. A new-family claim additionally requires reciprocal adaptation of the model's inferred criterion to account for an effect beyond separately measured choice blindness, self-perception, source-monitoring and sycophancy kernels, including active versus yoked exposure controls. Accurate prospective composition eliminates the extra family even if ordinary choice blindness remains. If preserving quoted-data scope in model history alone removes the effect while authentic decision receipts do not, another hypothesis of the same gap wins. A receipt-sensitive effect without any own-choice/rationale interaction supports ordinary source monitoring and does not satisfy this candidate.
What stands behind it
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.
This hypothesis states no figure and cites no study, so there is nothing here to trace.
What it would take to refute it. 1 paper(s) already retrieved for this hypothesis carry its prediction’s terms. Reading them comes before running anything. Already retrieved: Agentic AI systems in electrical power systems engineering: current state-of-the-art and challenges..
1 paper retrieved around this hypothesis
- Agentic AI systems in electrical power systems engineering: current state-of-the-art and challenges.PMID 42369004 · full_text · 129,576 characters stored
0 citation handles extracted; 1 Europe PMC search run; 1 records examined; 1 sources stored for enrichment, 1 with full text. A citation that did not resolve is a bibliographic failure, not proof that no such paper exists, and no hypothesis is blocked by this audit.