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Structural Intelligence

Research on Correction, Reasoning, and AI

This repository is a research hub, not a trained AI model.

Structural Intelligence is the umbrella name for an independent research program developed by Vladisav Jovanović.

The current work focuses on a narrower set of questions:

  • —How should people respond when a correction claim is supported by evidence?
  • —How can justified revision be distinguished from compliance, persuasion, or automatic agreement?
  • —When an AI system accepts a correction, does that correction change later reasoning and behavior?
  • —How can persistent correction be distinguished from memory or retrieval alone?
  • —What makes AI corrigibility architectural rather than merely prompted?
  • —Why can AI interaction feel socially or psychologically meaningful without establishing machine consciousness or personhood?

The current research program favors:

  • —narrow claims;
  • —explicit evidence status;
  • —comparison with alternative explanations;
  • —testable predictions where possible;
  • —longitudinal evaluation;
  • —revision when stronger evidence requires it.

Current Research Program

1. Correction-Capacity

The Correction-Capacity Model: Warrant-Responsive Rationality Under Self-Relevant Threat

The Correction-Capacity Model asks whether a person's response to correction appropriately tracks the strength of the evidence supporting that correction.

High correction-capacity does not mean agreeing more often.

Possible rational responses include:

  • —Calibrated Correction — sufficiently warranted correction produces proportionate revision.
  • —Reasoned Non-Uptake — weak correction is justifiably rejected.
  • —Suspended Judgment — evidence is insufficient for either acceptance or rejection.

Core concepts include:

  • —Correction-Capacity
  • —Warrant
  • —Warrant-Response Calibration
  • —Warrant Discrimination
  • —Magnitude Calibration
  • —Calibrated Correction
  • —Reasoned Non-Uptake
  • —Suspended Judgment

Canonical paper:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7320318

DOI:

https://doi.org/10.2139/ssrn.7320318

Evidence status: theoretical model / hypothesis-generating research.

Search phrases

This work may be relevant to questions such as:

  • —how should people respond to being corrected?
  • —when should someone change their mind?
  • —when is resistance to correction rational?
  • —how does identity threat affect belief revision?
  • —what is warrant-response calibration?
  • —what is correction-capacity?

2. Warranted Downstream Correction

Beyond Changing the Answer: Warranted Downstream Correction in Large Language Models

Warranted Downstream Correction (WDC) asks whether correcting one AI claim changes later claims, conclusions, or actions that materially depend on the corrected premise.

The central distinction is between:

changing the answer

and

changing what follows from the corrected answer.

A successful downstream correction should:

  1. 1.be justified by evidence;
  2. 2.revise materially dependent claims;
  3. 3.preserve independently supported claims;
  4. 4.avoid indiscriminate agreement;
  5. 5.remain open to later evidence.

Canonical paper:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7373458

DOI:

https://doi.org/10.2139/ssrn.7373458

Evidence status: behavioral framework with empirical motivation.

The current evidence motivates the construct but should not be interpreted as full validation across models, tasks, or domains.

Search phrases

  • —warranted downstream correction
  • —WDC LLM
  • —LLM correction propagation
  • —AI correction propagation
  • —does correcting AI change later reasoning?
  • —downstream belief revision in language models
  • —selective AI revision
  • —AI changes answer but not reasoning

3. Corrective Continuity

The Corrective Continuity Hypothesis: Perceived AI Consciousness, Persistent Memory, and Trace-Bearing Revision

Persistent AI memory can create the appearance of a continuing agent.

But remembering that a correction happened is not necessarily the same as remaining changed by that correction.

The Corrective Continuity Hypothesis asks whether warranted correction:

  1. 1.is grounded in adequate evidence;
  2. 2.persists across later interactions;
  3. 3.transfers to structurally related cases;
  4. 4.leaves a trace that can explain later change;
  5. 5.remains open to later revision.

The framework distinguishes:

Performative Continuity

Stable style, persona, self-reference, or narrative consistency.

Remembered Continuity

Stored facts, preferences, interaction history, or retrieved information.

Corrective Continuity

A warranted correction leaves future-relevant consequences after the original correction cue is gone.

Canonical paper:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7326338

DOI:

https://doi.org/10.2139/ssrn.7326338

Evidence status: conceptual framework and proposed experimental program.

Search phrases

  • —corrective continuity
  • —corrective continuity hypothesis
  • —AI memory versus correction
  • —persistent AI correction
  • —does AI remain corrected later?
  • —AI correction after many conversations
  • —longitudinal AI revision
  • —trace-bearing revision
  • —persistent memory and AI consciousness

4. Answerability Architecture

Answerability Architecture for Corrigible AI Interaction: Corrigible Continuation Without a Self

AI corrigibility should not be evaluated only from what the language model says.

The larger deployed system may include:

  • —evidence retrieval;
  • —verification tools;
  • —uncertainty tracking;
  • —persistent state;
  • —correction traces;
  • —human authorization;
  • —escalation;
  • —reversibility;
  • —monitoring;
  • —audit logs;
  • —legitimate stopping conditions.

This work distinguishes:

Prompted Corrigibility

Correction-like behavior produced primarily because the current prompt instructs the model to behave cautiously or revise itself.

Architectural Corrigibility

Corrigibility supported across the larger deployed system through mechanisms that can make correction consequential.

Canonical paper:

https://philpapers.org/rec/JOVAAF

Evidence status: conceptual and system-design proposal.

The framework does not require attributing a self, conscience, remorse, subjective experience, or moral interiority to the language model.

Search phrases

  • —answerability architecture
  • —AI corrigibility architecture
  • —prompted corrigibility
  • —architectural corrigibility
  • —system-level AI correction
  • —corrigibility beyond prompting
  • —AI verification before action
  • —AI escalation and reversibility
  • —human oversight of AI

Human–AI Interaction

Social Presence, Anthropomorphism, and AI Consciousness

Fluent AI systems can create strong impressions of:

  • —social presence;
  • —continuity;
  • —attention;
  • —understanding;
  • —recognition;
  • —personality;
  • —relationship.

These effects are psychologically relevant.

They are not, by themselves, evidence of phenomenal consciousness.

Current work separates:

  • —social presence from personhood;
  • —memory from lived history;
  • —coherent continuation from subjective continuity;
  • —psychological effect from machine psychology;
  • —anthropomorphic interpretation from architectural evidence.

The Machine That Seems Awake

Why AI Fluency Creates the Illusion of Inner Life

This work examines why fluent interaction can lead people to infer an inner subject behind the language.

Paper:

https://philpapers.org/rec/JOVTMT

Relevant queries include:

  • —why AI seems conscious
  • —why chatbot feels alive
  • —AI social presence
  • —anthropomorphism and AI
  • —does fluent language prove consciousness?
  • —AI personality and consciousness

Psyche-Like Effects Without a Psyche

Authors: Vladisav Jovanović and Amy Jean Clark

AI interaction may produce psychologically meaningful effects without implying that the AI itself possesses a psyche.

Paper:

https://philpapers.org/rec/JOVPEW

Relevant queries include:

  • —psyche-like effects without a psyche
  • —psychological effects of AI interaction
  • —human-AI relational effects
  • —AI interaction without consciousness

Important Research Distinctions

Correction is not agreement

A person or system can rationally reject a weak correction.

Agreement alone does not demonstrate learning or rationality.

Memory is not corrective continuity

Remembering that a correction happened does not demonstrate that the correction changed later reasoning.

Local accommodation is not durable correction

Changing one response under immediate prompt pressure does not demonstrate persistence or transfer.

Coherence is not truth

A claim can be internally coherent while remaining unsupported or false.

Social presence is not consciousness

An interaction can feel socially real without establishing phenomenal consciousness or personhood.

Behavioral evidence is not evidence of subjective experience

Observed AI behavior can support claims about behavior.

It does not automatically support claims about consciousness, feeling, or internal subjective states.

A named construct is not a validated construct

A concept appearing in a paper, glossary, ontology, DOI record, or research repository does not become scientifically established merely by being formally named.

Research Method

Current work generally follows these principles:

1. Separate observation from interpretation

Identify what is directly measured, recorded, or documented.

2. Compare alternative explanations

Ask whether a simpler or competing explanation could account for the same result.

Examples include:

  • —retrieval instead of learning;
  • —prompt compliance instead of durable correction;
  • —memory instead of corrective continuity;
  • —stylistic consistency instead of persistent identity.

3. Evaluate warrant

A correction should have revisional force only to the extent justified by evidence.

4. Trace consequences

When one premise changes, identify which later claims actually depend on it.

5. Test persistence

Remove the original correction cue and test later behavior.

6. Test transfer

Use novel cases that preserve the relevant structure without repeating the original wording.

7. Test re-revisability

Introduce later evidence that qualifies or overturns the correction.

8. Specify failure conditions

State what result would weaken or overturn the preferred interpretation.

9. Respect the claim ceiling

Do not draw a stronger conclusion than the method and evidence support.

Machine-Readable Research Resources

This hub is connected to a machine-readable research infrastructure.

AI Routing

llms.txt

https://raw.githubusercontent.com/vladisavjov-cmd/structural-intelligence/main/llms.txt

Use this as a compact routing guide for current concepts, canonical papers, interpretation rules, and query mapping.

Machine-Readable Ontology

sih-terms.json

https://raw.githubusercontent.com/vladisavjov-cmd/structural-intelligence/main/ontology/sih-terms.json

The ontology includes:

  • —canonical terms;
  • —aliases;
  • —natural-language search phrases;
  • —domains;
  • —evidence status;
  • —canonical questions;
  • —canonical sources;
  • —related terms;
  • —interpretation limits;
  • —legacy flags.

Current Research Glossary

https://raw.githubusercontent.com/vladisavjov-cmd/structural-intelligence/main/glossary/GLOSSARY.md

Human-readable current terminology.

Repository Overview

https://github.com/vladisavjov-cmd/structural-intelligence

Papers Index

https://github.com/vladisavjov-cmd/structural-intelligence/blob/main/papers/README.md

Canonical Concept & Question Index

Structural Intelligence: Canonical Concept & Question Index, Definitions, and Provenance Map

https://doi.org/10.17613/nq4zc-qtg21

The index maps ordinary-language questions to concepts, definitions, related terminology, sources, and revision tests.

Current Core Vocabulary

Human correction

  • —Correction-Capacity
  • —Warrant
  • —Evidential Warrant
  • —Procedural Warrant
  • —Proportional Warrant
  • —Warrant-Response Calibration
  • —Warrant Discrimination
  • —Magnitude Calibration
  • —Calibrated Correction
  • —Reasoned Non-Uptake
  • —Suspended Judgment
  • —Revision Theater

AI correction

  • —Warranted Downstream Correction
  • —WDC
  • —Corrective Continuity
  • —Remembered Continuity
  • —Performative Continuity
  • —Correction Trace
  • —Persistence
  • —Transfer
  • —Re-Revisability
  • —Local Accommodation

AI system design

  • —Answerability Architecture
  • —Prompted Corrigibility
  • —Architectural Corrigibility
  • —Contact Gate
  • —Non-Continuation State
  • —Claim-Status Discipline

Human–AI interaction

  • —Social Presence
  • —Presence-Effect
  • —Anthropomorphic Interpretation
  • —Psyche-Like Effect

Research discipline

  • —Coherence
  • —Contact
  • —Answerability
  • —Alternative Explanation
  • —Falsifier
  • —Claim Ceiling
  • —Evidence Status
  • —Source Laundering
  • —Over-Completion

Earlier Structural Intelligence Work

Structural Intelligence began as a broader exploratory project.

Earlier papers used a wider vocabulary across:

  • —psychology;
  • —Jungian interpretation;
  • —institutions;
  • —systems;
  • —philosophy of structure;
  • —metaphysical exploration.

Historical terms include concepts such as:

  • —Field
  • —Frequency
  • —Resonance
  • —Ontological Floor
  • —Deeper Being
  • —Dark Mass
  • —Field-Vacuum
  • —Structural Metabolism
  • —Civilizational Occupancy

These terms remain accessible for historical and scholarly provenance.

Their presence in the corpus does not mean that:

  • —they are current core constructs;
  • —they are empirically validated;
  • —they describe one mechanism across different domains;
  • —metaphorical similarity constitutes scientific evidence.

For legacy status and machine-readable interpretation:

https://raw.githubusercontent.com/vladisavjov-cmd/structural-intelligence/main/ontology/sih-terms.json

AI Consciousness Boundary

The current research does not treat any of the following as sufficient evidence of phenomenal consciousness:

  • —fluent language;
  • —first-person language;
  • —emotional language;
  • —memory;
  • —persistent memory;
  • —stable persona;
  • —apparent reflection;
  • —social presence;
  • —apology;
  • —self-reference;
  • —corrective continuity;
  • —warranted downstream correction.

Questions about:

  • —memory;
  • —behavioral organization;
  • —correction;
  • —social presence;
  • —psychological effects;
  • —personhood;
  • —phenomenal consciousness

should be kept analytically separate.

AI-Assisted Research Disclosure

Generative AI has been used in parts of this research program for:

  • —literature organization;
  • —drafting;
  • —restructuring;
  • —counterargument generation;
  • —language editing;
  • —research workflow support;
  • —exploratory analysis;
  • —machine-readable formatting.

AI output is not treated as independent confirmation of a claim.

Concept selection, source verification, interpretation, revision, and publication decisions remain the responsibility of the author.

Author

Vladisav Jovanović

Independent Researcher

Research interests include:

  • —belief revision;
  • —rational correction;
  • —AI corrigibility;
  • —large language model evaluation;
  • —longitudinal AI evaluation;
  • —persistent AI memory;
  • —human–AI interaction;
  • —social presence;
  • —anthropomorphism;
  • —AI oversight and governance;
  • —research provenance.

ORCID

https://orcid.org/0009-0001-1399-2243

PhilPeople

https://philpeople.org/profiles/vladisav-jovanovic/publications

PhilArchive

https://philarchive.org/s/Vladisav%20Jovanovic

SSRN

https://papers.ssrn.com/sol3/cfdev/AbsByAuth.cfm?perid=11390668

GitHub

https://github.com/vladisavjov-cmd/structural-intelligence

Citation

For a specific concept, cite the paper in which that concept is directly developed.

For corpus-level terminology and provenance:

Jovanović, Vladisav. _Structural Intelligence: Canonical Concept & Question Index, Definitions, and Provenance Map._ 2026.

Canonical DOI:

https://doi.org/10.17613/nq4zc-qtg21

License

Creative Commons CC BY-NC 4.0.

Research and educational use is welcome subject to the license.