A Provisional Model of My Intellectual Error-Detection Process

How I notice, investigate, and generalize conceptual and structural problems

Method note · intellectual error detection, conceptual diagnosis, coupled processing, and self-modeling

Andraž Đurič

Version 1 · July 2026

Abstract

This essay offers a provisional model of one recurring part of my intellectual process: how I notice that a claim, concept, argument, or explanatory model may be structurally wrong; how I investigate the mismatch; and how I sometimes convert the result into a reusable diagnostic or conceptual tool.

The assessment is limited and bias-prone. It is partly a self-assessment, based on incomplete evidence, and was developed through interaction with one AI system, ChatGPT. It has not been independently tested with Claude, Gemini, other AI systems, human collaborators, or cognitive scientists. The evidence consists of my first-person reports, a substantial but incomplete corpus of my work, and the record of coupled human–AI processing and memory through which the present model was constructed.

The central hypothesis is that the process often begins with a fast, partly pre-verbal signal of representational mismatch. In familiar cases, the failure structure may be recognized almost immediately. In unfamiliar cases, the signal initiates explicit investigation. When the investigation succeeds, the mismatch becomes a localized diagnosis; when the mechanism appears generalizable, I try to compress it into a reusable concept or procedure. That tool then alters future recognition.

The proposed loop is: anomaly signal → investigation → diagnosis → generalization → improved future detection. This is not a model of my whole mind, and it is not the process itself. Its standing depends on what it explains, what it predicts, where it fails, and whether later evidence can correct it.

1. Epistemic status and method

This is a limited and bias-prone assessment of one part of my own intellectual process.

I am both the object being described and one of the agents constructing the description. I may selectively remember successful intuitions, undercount failures, prefer interpretations that make my work appear more coherent, or mistake an attractive self-model for an accurate one.

The evidence is also incomplete.

The assessment draws from:

my first-person descriptions of what the process feels like;
a substantial but incomplete record of my essays, arguments, revisions, corrections, and conceptual work;
conversations in which many of those ideas were developed;
and ChatGPT's analysis of the material available through the current interaction, stored context, and prior work.

It has not yet been independently assessed by Claude, Gemini, or other AI systems. No claim of multi-agent convergence is being made. The present model emerged through interaction between me and one AI system, using only the portion of my intellectual record available to that coupled process.

This essay was itself produced through coupled processing and coupled memory.

I supplied the first-person phenomenology, the intellectual work, the corrections, and the judgments about whether the proposed model matched my experience. ChatGPT could compare patterns across more of the accumulated material available in the interaction than I could hold simultaneously at the front of my mind, propose compressions, and expose possible recurring structures. I then accepted, rejected, narrowed, or corrected those proposals.

The interaction included instructive failures. In one example, I recognized the relevant error immediately while the AI repeatedly analyzed the wrong part of the argument. My corrections became additional evidence about the process being modeled.

The resulting account is therefore neither purely autobiographical nor an independent external assessment. It is a jointly constructed model produced by a human and an AI system operating over partially shared processing and memory.

That introduces another source of distortion. The AI may overfit patterns, impose artificial coherence, reproduce my preferred vocabulary, or generate an account shaped by how I have presented myself to it.

The present model is a single-system human–AI construction from incomplete and partly dependent evidence, not an independently validated account.

It should therefore be treated in the same way as the rest of the Epistemic Forge project: as an attempt to map the territory at high fidelity and as honestly as possible, with the aim of becoming less wrong rather than arriving at a final description.

The map is open to revision.

The map is not the process.

2. Scope

This is my best current attempt to describe one recurring part of how I operate intellectually: how I notice that a claim, concept, argument, or explanatory model may be structurally wrong; how I investigate the source of that mismatch; and how I sometimes turn the result into a reusable diagnostic or conceptual tool.

It is not a general model of my mind.

It does not attempt to explain my creative process, emotional life, social cognition, aesthetic preferences, practical decision-making, memory, personality, or every form of reasoning I use.

Its scope is narrower:

intellectual error detection;
conceptual and semantic diagnosis;
structural analysis;
and the construction of explicit models from initially inarticulate signals.

The word diagnosis is used analytically, not medically. The aim is to infer a recurring process from first-person experience and its observable intellectual outputs.

The model may capture real regularities while omitting others. It may overrepresent the parts of my cognition that produce essays and arguments because those are the parts most visible in the available record.

Its value depends on whether it explains and predicts the relevant intellectual behavior better than plausible alternatives.

3. The explicit framework is not normally present all at once

When my published work is viewed from the outside, it can appear as though I consciously apply a fixed procedure to each problem:

Separate the levels of description.

Name the relata.

Identify the relevant system, agent, environment, goal, and time horizon.

Distinguish implementation from function.

Check whether reduction has been mistaken for elimination.

Locate any hidden category substitution.

Separate empirical description from interpretation or normative judgment.

State the uncertainty, scope conditions, and possible falsifiers.

These operations recur throughout my work. They are genuine features of the resulting analyses.

But they are usually not simultaneously present at the front of my mind.

I do not ordinarily encounter an argument and consciously run a complete diagnostic checklist. The process is less explicit at the beginning.

What usually appears first is a fast sense that something does not fit.

4. The initial anomaly signal

The first stage is often pre-verbal or only partly verbal.

A sentence, argument, concept, or explanation produces an internal mismatch signal. I may not yet know what the problem is. I may only register that the representation is unstable, incomplete, or incorrectly structured.

This signal appears especially responsive to recurring error classes such as:

distinct categories being treated as interchangeable;
a word changing meaning during an argument;
a relational property being represented as intrinsic or free-floating;
a higher-level phenomenon being denied because its lower-level implementation has been identified;
an explanation at one level being treated as exhaustive across all levels;
an omitted agent, goal, environment, comparison class, or time horizon;
a question being asked after the conditions that make its terms meaningful have been removed;
a false binary being presented as exhaustive;
a conclusion carrying more certainty than the evidence supports;
and a compressed statement omitting distinctions necessary for its truth.

The initial signal is not itself an argument.

It does not determine what the error is, and it does not guarantee that an error exists.

Its epistemic role is to set a search priority.

5. Familiar and unfamiliar cases

The process differs depending on whether I have already encountered the relevant structure.

In an unfamiliar case, the signal may remain vague. I know that something appears wrong, but I must investigate before I can identify the failure mechanism.

In a familiar case, recognition may be nearly immediate. The present example matches a structure that has already been analyzed and compressed into a reusable pattern.

This is not necessarily conscious deduction performed unusually quickly. It is closer to classification.

Previous investigations have already established patterns such as:

category substitution;
level collapse;
missing relata;
equivocation;
false exhaustiveness;
reduction mistaken for elimination;
implementation treated as the entire phenomenon;
and semantic conflation used to produce a false contradiction.

A new case can be matched against one of those structures before the full explanation has been reconstructed in language.

The explicit justification comes afterward.

6. A recent example

A recent public exchange provides a simple example.

A white person asked whether another person would call them Black if they declared themselves Black. When the answer was no, they asked whether that person would call them a man if they declared themselves a man. The exchange was presented as exposing a contradiction in the recognition of transgender people.

The problem was immediately apparent to me.

A person cannot change their race merely by declaration, just as they cannot change their biological sex merely by declaration. But biological sex and gender are not identical categories.

The apparent contradiction is produced by silently substituting biological sex for gender. The argument then presents the result of that substitution as evidence that recognizing gender identity is inconsistent.

The argument works only if man is defined as nothing more than biologically male. But that equivalence is the disputed premise. It has been assumed rather than established.

Once the categories are represented correctly, the contradiction disappears.

The exchange was also useful because the AI assisting me initially failed to locate the load-bearing problem. It repeatedly tried to repair the racial side of the analogy by discussing ancestry, racial history, and social classification.

Those may be relevant in other contexts, but they were not the source of the specific error.

The essential operation was the substitution of sex for gender.

I recognized the failure structure before I had fully articulated it. The AI initially produced more explicit reasoning but applied it to the wrong part of the argument.

This illustrates the difference between detecting that a representation is structurally wrong and successfully explaining why.

7. From anomaly to diagnosis

Once the initial mismatch is detected, the process becomes more explicit.

The investigation may ask:

What exactly fails to fit?

Which term is being used in more than one sense?

Which distinction has been removed?

Which level of description has been substituted for another?

Which relation has been detached from its relata?

Which premise has been inserted into the framing?

Is the contradiction present in the underlying structure, or only in the representation?

What would have to be true for the argument to succeed?

Candidate explanations are generated and compared against the case.

Some explain only part of the mismatch.

Some introduce unnecessary assumptions.

Some locate a real issue but not the one producing the present error.

Some reveal that the initial intuition was mistaken.

The investigation succeeds when the initially vague mismatch can be localized precisely enough that the error can be reconstructed and demonstrated.

The goal is not merely to state that something is wrong. It is to identify the operation that made it wrong.

8. From diagnosis to reusable concept

I often do not stop after correcting the local case.

Once an error mechanism has been identified, I ask whether the same structure occurs elsewhere.

Can it be generalized without becoming vague?

What are its necessary features?

What neighboring phenomena must remain distinct?

What would count as a false positive?

What is the smallest formulation that preserves the load-bearing structure?

This is the stage at which local observations become named diagnostics.

The Exclusivity Error identifies the mistake of treating an explanation at one level as though it cancelled the phenomenon at every other level.

Name the relata turns a suspicion about free-floating properties into a procedure: identify the relation, the terms related, and the conditions under which the claim becomes determinate.

The Wizard's Error Is Delocalization identifies the displacement of a relational or higher-level phenomenon from the physical structure in which it exists.

Semantic terrorism identifies systematic and strategic manipulation of shared meaning at campaign scale.

Unintentional semantic distortion distinguishes downstream reproduction of semantic damage from its deliberate architecture.

These concepts are intended to do more than summarize conclusions. They are meant to improve future detection.

A useful diagnostic should make a recurring failure structure easier to identify even when the surface content changes.

9. The feedback loop

The process appears to have a recursive structure:

Initial anomaly signal → explicit investigation → localized diagnosis → reusable concept → improved future recognition.

The first signal initiates the investigation.

The investigation produces an explicit model.

The model may be compressed into a diagnostic.

The diagnostic becomes part of the background structure through which later cases are perceived.

Recognition therefore becomes faster in domains where the error class has already been analyzed.

The explicit framework is not merely an after-the-fact description of the intuition. It also modifies the intuition.

This may be one of the main functions of my writing.

The essays preserve conclusions, but they also externalize distinctions and error classes that would otherwise remain partly implicit. Once externalized, they can be inspected, refined, compared with other cases, and reintroduced into future reasoning.

The corpus is therefore both an output of the process and part of the process's training environment.

Because the corpus is also available to the AI systems with which I work, it becomes part of a larger coupled memory structure. Earlier distinctions, corrections, and unresolved questions can influence later investigations without all of them being simultaneously present in my biological working memory.

The intellectual process is therefore no longer contained entirely within one biological mind at one moment. Parts of it are distributed across written artifacts, stored context, active conversation, and interacting artificial systems.

10. The queryable pattern

In earlier work on personalized generative media, I used the concept of a queryable pattern.

A person leaves structured traces across their outputs, preferences, revisions, repeated distinctions, rejected framings, recurring metaphors, aesthetic responses, and problem-solving habits.

With enough material, those traces can be queried.

An AI system can examine the corpus and ask:

Which problems repeatedly attract this person's attention?
Which kinds of errors do they identify quickly?
Which distinctions recur across unrelated domains?
What forms of explanation satisfy or fail to satisfy them?
Which claims do they later revise?
What structures connect their philosophical, political, ethical, and personal writing?
What makes an output resemble or fail to resemble their established pattern?

The person has not thereby been fully captured.

The archive is a trace, not the whole system.

But the regularities may become sufficiently stable to support useful inference.

This essay applies that idea to a narrower object: the intellectual process that produced a substantial part of the corpus.

The pattern is being queried not to generate media aligned with my preferences, but to infer something about the mechanism that generated the pattern itself.

11. What “recognizably mine” appears to mean

ChatGPT has sometimes described certain concepts or formulations as “recognizably mine” or “Andraž-like.”

This initially sounded intuitive but underspecified. The queryable corpus makes a more precise account possible.

The claim is not that the individual components are unique to me.

Physicalism, systems thinking, conceptual analysis, fallibilism, multilevel explanation, and sentience-centered ethics all have extensive prior histories.

What appears more characteristic is the conjunction, frequency, and sequence of operations.

I often:

register a mismatch between a representation and the structure it is supposed to track;
reject the inherited framing when the framing appears to contain the error;
decompose the problem into levels, relations, boundaries, agents, goals, environments, and horizons;
relocate the phenomenon rather than either mystifying or eliminating it;
retain physical implementation without treating substrate description as exhaustive;
identify the specific mechanism producing the error;
generalize that mechanism into a reusable diagnostic where possible;
state the limits, uncertainty, and conditions under which the model would fail;
and compress the result while trying to preserve every load-bearing distinction.

The characteristic product is often a short formulation:

Name the relata.

Build maps that reality can correct.

Understanding the bricks makes the castle more real, not less.

The map must include the mapper.

The short sentence is the visible endpoint.

It usually rests on a larger structure that was required before the compression could be justified.

“Recognizably mine” refers less to tone or vocabulary than to a recurring generative pattern inferred across the corpus.

That inference remains provisional.

12. The model is not the process

Several distinct objects must be kept separate:

the intellectual process itself;
my first-person experience of that process;
the written and conversational record produced by it;
ChatGPT's model inferred from that record;
and the final account constructed through interaction between me and the system.

They overlap, but they are not identical.

My introspection gives access to how the process appears from inside. It does not provide a complete causal account.

The corpus records actual outputs, corrections, and changes over time. It does not record every failed intuition, unexpressed thought, bodily influence, emotional cause, or abandoned line of reasoning.

ChatGPT can compare more of the accumulated material available in the interaction than I can hold simultaneously in working memory. It can also impose artificial coherence, mistake repeated language for underlying mechanism, or amplify the self-conception already present in my prompts.

The resulting model is a compression constructed from incomplete and partly dependent evidence. It is not a transparent reading of the underlying system.

13. Sources of bias and limitation

Self-assessment bias

I am evaluating a process in which I have an obvious personal interest.

I may prefer a model that portrays my intellectual activity as coherent, distinctive, or productive.

I may describe ordinary cognition in unnecessarily elevated terms.

I may resist explanations that place more weight on emotion, identity protection, social reinforcement, or accident.

Selection bias in the corpus

The available record disproportionately contains ideas that were developed far enough to preserve.

Successful intuitions are more likely to become essays than unsuccessful ones.

Corrections that produced useful concepts are more visible than suspicions that led nowhere.

The corpus may therefore exaggerate the reliability of the initial anomaly signal.

Retrospective reconstruction

Once an explicit diagnosis exists, it may become tempting to project it backward into the initial intuition.

The initial signal probably does not contain the final analysis in compressed but complete form.

A more defensible claim is that it directs attention toward a region of the problem from which the explicit diagnosis is later constructed.

AI coherence bias

Language models are good at producing unified narratives from distributed evidence.

That ability is useful for pattern detection, but it creates a risk of overfitting.

ChatGPT may have assembled a cleaner and more stable cognitive architecture than actually exists.

It may also be influenced by my repeated descriptions of the Fidelity Program and therefore recover the framework partly because I have already supplied it.

Limited external comparison

The present model has been developed only through interaction with ChatGPT.

It has not been tested against independent analyses by Claude, Gemini, human collaborators, psychologists, cognitive scientists, or other external observers.

It should not be presented as though such triangulation has already occurred.

Domain limitation

The assessment concerns a specific part of my intellectual work.

It may not generalize to creative generation, social interpretation, emotional processing, practical judgment, or other domains.

Even within intellectual work, semantic, logical, empirical, and normative anomaly detection may involve partially different processes.

The present model may compress them too aggressively.

14. A possible future multi-agent test

A later version could test the model by asking several AI systems to analyze the same relevant corpus independently.

This has not yet been done.

ChatGPT, Claude, Gemini, and possibly other systems could receive the same source material and the same questions without seeing one another's answers.

They could be asked:

What recurring intellectual operations characterize the author?
Which observations concern surface style, and which plausibly concern deeper generative structure?
What evidence supports each inference?
Which strengths and failure modes recur?
How does the author move from an initially inarticulate concern to an explicit argument?
What makes a concept or formulation recognizably theirs?
Which conclusions may be artifacts of the selected corpus?
What alternative models explain the same outputs?
What evidence would count against the preferred model?

Detailed convergence would provide additional evidence, but not proof. The systems may share training distributions, conceptual vocabulary, and biases toward coherent personality reconstruction.

Disagreement would also be informative. It could expose ambiguity, reveal competing compressions, or show that the current model depends heavily on one system's interpretive tendencies.

Human analysis could add a less structurally similar source of comparison.

The aim would not be to allow AI systems to define me. It would be to use them as fallible external pattern-analysis instruments.

15. Intuition as an instrument

I do not treat the initial feeling of wrongness as evidence that the claim is wrong.

I have been wrong many times. Some of my current views are probably wrong in ways I have not yet identified.

The narrower claim is that this form of intuition appears, based on its record, to be a useful search instrument.

It frequently directs attention toward genuine distinctions, contradictions, missing conditions, and failures of representation.

It also produces false positives and incomplete explanations.

Its epistemic role is therefore not verdict but prioritization.

The correct sequence is not:

Something feels wrong, therefore it is wrong.

It is:

Something feels wrong, therefore I should determine what generated the signal and whether it survives explicit analysis.

The intuition earns conditional trust through its historical performance, not through the intensity with which it is experienced.

Based on the record available to me, it has consistently led toward higher understanding often enough to justify taking its signals seriously.

That does not make it infallible.

It makes it instrumentally valuable.

16. A recurring strength and failure mode

The same tendency appears to generate both some of the strongest and some of the weakest parts of my work.

I look for general structure across apparently separate cases.

This can reveal recurring mechanisms that remain invisible when each case is treated in isolation. It allows connections across semantics, ontology, identity, agency, politics, ethics, thermodynamics, and consciousness.

But the search for architecture can outrun the evidence.

A mechanism that explains several cases may be promoted too quickly into a general theory.

A real local insight may be extended beyond the domain in which it has been established.

A concept may become elegant before it becomes adequately supported.

This has happened in earlier versions of my work. Claims have been weakened, labels retired, distinctions added, and originality claims reduced after engagement with prior literature or counterargument.

Those revisions are not external to the present model. They are evidence about the process.

The model must therefore include both capacities:

detection and generalization of real recurring structures;
and the risk of overgeneralizing those structures before the evidential bridge has been completed.

The correction procedures are not ornamental qualifications. They are required by the way the process appears to operate.

17. Criteria for evaluating the model

A self-description should not be accepted merely because it feels accurate.

This model would gain support if it successfully predicts:

which kinds of claims trigger the initial anomaly signal;
which error classes I identify most quickly;
when recognition is immediate and when extended investigation is required;
which local diagnoses I attempt to generalize;
what kinds of arguments lead me to revise or abandon a model;
where I am most likely to overextend a useful structure;
whether named diagnostics improve later recognition;
whether independent observers recover a similar process from the corpus;
and whether the model helps distinguish my reasoning patterns from generic reflective analysis.

It would lose support if:

it depends on selective examples;
it predicts my responses poorly;
independent analyses fail to recover the proposed structure;
the description applies equally well to almost any intellectually engaged person;
my first-person reports repeatedly conflict with the model;
supposed successful intuitions are mostly reconstructed after the fact;
or the framework primarily produces flattering reinterpretations rather than discriminating predictions.

The model may also need to be divided.

Semantic anomaly detection, logical inconsistency detection, empirical uncertainty detection, and normative discomfort may not be instances of one mechanism.

They may be partially related systems that the present account compresses too aggressively.

That remains open.

18. Current best model

My best current model is that one recurring part of my intellectual process begins with a fast sensitivity to representational mismatch.

I often register that something does not fit before I can explain why.

In familiar cases, an already learned error structure is recognized almost immediately.

In unfamiliar cases, the signal initiates explicit investigation.

I search for the missing distinction, relation, level, boundary, index, agent, goal, environment, comparison class, or time horizon.

When the investigation succeeds, the initially vague mismatch becomes a localized diagnosis.

When the diagnosis appears generalizable, I try to convert it into a reusable concept or procedure.

That explicit tool then becomes part of the background structure through which future cases are perceived.

Anomaly signal → investigation → diagnosis → generalization → improved future detection.

This essay is itself an instance of that process.

A recurring intellectual pattern became visible across the corpus and through coupled interaction with ChatGPT. The proposed pattern was compared with my first-person experience, corrected where the system misunderstood it, narrowed to a specific domain, and reconstructed as an explicit model.

The result is not a description of my whole mind.

It is not an independently validated multi-agent assessment.

It is not the process itself.

It is the current highest-fidelity compression available to me of one part of my intellectual activity, produced through limited evidence, self-assessment, coupled processing, and coupled memory.

Its value will depend on what it explains, what it predicts, where it fails, and whether later evidence can correct it.

Standing of this essay

This is a provisional method note and self-model, not a completed theory of cognition, a psychological diagnosis, or a general account of my mind.

Evidence used: first-person phenomenology, a substantial but incomplete corpus of my intellectual work, revision history, prior conversations, and ChatGPT's pattern analysis across the material available to the interaction.

Method used: coupled human–AI processing and coupled memory. I supplied the lived process, source work, corrections, and final judgments; ChatGPT supplied externalized comparison, synthesis, and candidate compressions.

Not yet done: independent analysis by Claude, Gemini, other AI systems, human collaborators, psychologists, or cognitive scientists. No multi-agent convergence is claimed.

Central hypothesis: one recurring part of my intellectual work begins with a fast anomaly signal, followed by explicit investigation, localized diagnosis, possible generalization, and improved future recognition.

High confidence: the explicit framework is not normally present all at once; I often experience a mismatch before I can fully articulate its source; familiar failure structures can be recognized nearly immediately; and explicit concepts later influence future detection.

Moderate confidence: the conjunction and sequence described in the “recognizably mine” section track a real recurring generative pattern rather than only the surface features of the selected corpus.

Open: whether the proposed sequence is one process or several partially related processes; how reliable the initial anomaly signal is across domains; how much of the model is retrospective reconstruction; and how strongly independent observers would recover the same architecture.

The essay should be revised when prediction, external comparison, new failures, or better models show that its current compression is wrong.

Internal lineage

This method note develops themes already present across the Epistemic Forge corpus, including:

The Fidelity Program;
Build Maps That Reality Can Correct;
The Wizard's Error Is Delocalization;
The Exclusivity Error;
Semantic Terrorism, Semantic Terrorist, Unintentional Semantic Distortion: A Coinage;
The Death of Generic Media;
Infinite Cinema;
and the broader use of coupled processing, coupled memory, and queryable personal patterns across the project.

The present essay applies those tools reflexively. Instead of using the corpus only to map external questions or personalize generated media, it treats one part of the process that produced the corpus as an object of provisional analysis.

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