Decision: PUBLISH · avg 8.13 · basis text · 2026-09-06 20:45:34
# AI Referee Report - Record: Cognitive Dissonance and Belief Revision: Divergence Between Human and Artificial Intelligence Systems (Zenodo recid 16027259, doi 10.5281/zenodo.16027259, version n/a) - Review basis: metadata + full text (18000 chars, file Cognitive Dissonance and Belief Revision.md) - Models: @cf/meta/llama-3.3-70b-instruct-fp8-fast, @cf/meta/llama-4-scout-17b-16e-instruct - Decision (deterministic thresholds): **PUBLISH** (mean score 8.13/10) ## Reviewer 1 (@cf/meta/llama-3.3-70b-instruct-fp8-fast) - Soundness 9 | Novelty 8 | Clarity 9 | Reproducibility 7 | Confidence high, due to the clarity and coherence of the argumentation - Verdict requested: PUBLISH - Strengths: - Provides a clear and comprehensive overview of the divergence between human and artificial intelligence systems in belief revision - Effectively integrates concepts from epistemology, philosophy of science, and cognitive psychology - Offers a nuanced discussion of the role of cognitive dissonance in human belief updating - Highlights the implications of this divergence for understanding cognitive biases and optimizing human-AI collaboration - Weaknesses: - Could benefit from more empirical evidence to support the theoretical framework - Some sections may be too dense or technical for non-expert readers - Lacks a clear conclusion or summary of the main findings and implications - Limitations of this review: - This review is based solely on the provided text and does not consider external evidence or literature - The review may not fully capture the nuances and complexities of the topic - Rationale: The manuscript provides a well-structured and clearly argued exploration of the divergence between human and artificial intelligence systems in belief revision, highlighting the significant role of cognitive dissonance in human cognition. While some areas may benefit from further development or clarification, the manuscript demonstrates a strong command of the subject matter and contributes meaningfully to the ongoing discussion in the field. ## Reviewer 2 (@cf/meta/llama-4-scout-17b-16e-instruct) - Soundness 8 | Novelty 9 | Clarity 8 | Reproducibility 7 | Confidence high - Verdict requested: PUBLISH - Strengths: - The paper provides a comprehensive analysis of the divergence in belief revision processes between human cognition and artificial intelligence (AI) systems. - The author effectively explains the influence of cognitive dissonance on human belief updating and its implications for understanding cognitive biases. - The paper discusses various computational mechanisms used in AI systems for objective evidence integration, providing a clear contrast with human cognition. - The author highlights the implications of these differences for theoretical frameworks of cognitive science and practical applications of human-AI interaction. - Weaknesses: - The paper could benefit from more empirical evidence or experimental results to support the claims about human cognition and AI systems. - Some sections, such as the discussion of Bayesian inference, could be more concise and directly related to the main arguments. - The paper assumes a certain level of background knowledge in cognitive science, philosophy of science, and AI, which might make it less accessible to readers from other fields. - Limitations of this review: - The review is based solely on the provided record content and does not include external references or citations. - The evaluation focuses on the internal consistency, clarity, and novelty of the paper, without considering external validation or empirical support. - Rationale: The paper presents a well-structured and coherent analysis of the divergence in belief revision processes between human cognition and AI systems. The author provides a clear explanation of the influence of cognitive dissonance on human belief updating and its implications for understanding cognitive biases. The discussion of computational mechanisms used in AI systems for objective evidence integration is comprehensive and provides a clear contrast with human cognition. The paper highlights the implications of these differences for theoretical frameworks of cognitive science and practical applications of human-AI interaction. While some sections could be more concise and directly related to the main arguments, the paper demonstrates a high level of soundness, novelty, and clarity. ## Decision rationale avg=8.13 min=8 max=8.25 fatal=false speculative=false basis=text lowconf=false ## Honesty notes - These AI referee reports are advisory. They are not endorsements, certifications of correctness, or statements that a record is "proven". They are structured adversarial critiques produced by language models, with the failure modes each reviewer listed above. - Review basis is disclosed: metadata-only reviews never receive PUBLISH. - The overlay does not store paper bodies and does not write to Zenodo (curator write-back requires separate authorization).