Executive Summary
This report publishes a taxonomy of multi-model conflict for governance research: stylistic, factual, evidentiary, recommendation, risk, policy, and decision-critical classes. Publication date: 2026-09-10. Last updated: 2026-09-10.
Aggregate class distribution is not published. Dataset size: Not published — measured corpus not yet released. No labeled public cases are released. Methodology published; labeled distribution forthcoming.
The taxonomy supports the agreement/divergence benchmark, human override study design, and evaluator methodology. Operational framing: consensus vs. divergence, multi-model governance, and model vs. decision governance.
Key Findings
No class frequencies or sample-size statistics are claimed.
- A labeled public disagreement distribution is not yet available from the corporate evidence corpus.
- Stylistic disagreement and decision-critical disagreement require different escalation rules; collapsing them hides risk.
- Evidentiary conflict (different citations or source interpretations) should preserve both sides in the record.
- NIST AI RMF and ISO/IEC 42001 motivate structured risk identification and management-system documentation for AI conflict handling[1][2][4].
- OECD AI Principles emphasize accountability and transparency when automated systems produce conflicting advice[3].
Research Question
What types of multi-model conflict appear in governed decision workflows, and which types should trigger escalation versus ordinary review?
Class prevalence: measurement not yet available.
Dataset and Measurement Status
Dataset size: Not published — measured corpus not yet released. No labeled public cases are released.
Methodology document ID: SL-RG-METHOD-2026.1 (methodology only — not a dataset).
Status: taxonomy published; labeled dataset distribution forthcoming.
Methodology
Cases are labeled only after model freeze and prompt freeze. Dual labeling with adjudication is required for decision-critical and policy classes. Schema and inter-rater rules are defined in SL-RG-METHOD-2026.1.
Distribution tables will be published only after a public labeled corpus exists. Until then: measurement not yet available for class shares.
Multi-Model Conflict Taxonomy
Framework only — not an observed frequency table.
| Conflict class | What differs across models | Governance note |
|---|---|---|
| Stylistic | Tone, length, or phrasing without changing the recommended action | Usually soft divergence |
| Factual | Contradictory claims about facts or figures | Require source check before authorization |
| Evidentiary | Different citations, documents, or interpretations of the same evidence | Retain both evidence sets |
| Recommendation | Different proposed actions or priorities | Escalate when actions are incompatible |
| Risk | Different risk severity or likelihood judgments | Map to authority thresholds |
| Policy | Advice that conflicts with stated organizational or legal policy | Block or escalate; record exception if any |
| Decision-critical | Conflict that would change who authorizes or whether to proceed | Mandatory human authority and full ledger retention |
Source: Smart Logic AI research methodology SL-RG-METHOD-2026.1; measured results not yet published.
Management Implications
Train reviewers on class distinctions so “the models disagreed” is not treated as a single risk level.
Require Decision Ledger retention of compared outputs for recommendation, policy, and decision-critical classes — see what to record.
Use the taxonomy when designing override studies: human–AI override study.
Limitations
Zero labeled public cases are released with this report. Any implied prevalence would be fabricated and is forbidden.
Taxonomy boundaries can be ambiguous at the edges (for example, factual vs. evidentiary); adjudication guidance in SL-RG-METHOD-2026.1 must be followed before scoring.
External citations provide principles, not labeled Smart Logic AI cases[1][2][3][4].
How Smart Logic Approaches This
SmartSolo is intended to show reviewers disagreement rather than silently merging conflicting model outputs.
Corporate research will release labeled examples only under the evaluator methodology and SL-RG-METHOD-2026.1.
See consensus vs. divergence for operational signal handling.
References
Authoritative sources cited for standards and methodology claims. Inline markers link here.
Citation kit
Report title: What AI Disagreement Actually Looks Like: A Taxonomy of Multi-Model Conflict
Publisher: Smart Logic AI
Publication date: 2026-09-10
Last updated: 2026-09-10
Canonical URL: https://www.smartlogicusa.com/research/model-disagreement-dataset
Methodology URL: https://www.smartlogicusa.com/research/multi-model-evaluator-methodology
Dataset / version identifier: Not assigned — measured corpus not yet published
Method document ID: SL-RG-METHOD-2026.1
Data coverage: Measured public corpus not yet released; methodology coverage begins 2026-09-10.
Suggested citation: Smart Logic AI. “What AI Disagreement Actually Looks Like: A Taxonomy of Multi-Model Conflict.” 2026. https://www.smartlogicusa.com/research/model-disagreement-dataset.
See governed AI execution in a live workflow
Review how SmartSolo coordinates multiple AI models, routes human authorization, and preserves the decision record.