Decision: REVISE · avg 7.5 · basis metadata · 2026-09-06 20:45:34
# AI Referee Report - Record: A Formalized Dynamic Model of Self-Regulating Systems Under Conditions of Uncertainty (Zenodo recid 18370178, doi 10.5281/zenodo.18370178, version v1.0) - Review basis: metadata/abstract only (PDF or no textual file) - Models: @cf/meta/llama-3.3-70b-instruct-fp8-fast, @cf/meta/llama-4-scout-17b-16e-instruct - Decision (deterministic thresholds): **REVISE** (mean score 7.5/10) ## Reviewer 1 (@cf/meta/llama-3.3-70b-instruct-fp8-fast) - Soundness 8 | Novelty 9 | Clarity 7 | Reproducibility 6 | Confidence medium, due to limited information available for review - Verdict requested: REVISE - Strengths: - Proposes a new way of talking about complex systems under uncertainty - Addresses the reduction problem between physics and psychology - Provides a formal but non-reductionist language for describing complex processes - Weaknesses: - Lack of concrete examples or case studies to illustrate the model - Unclear how the model can be applied in practice - No empirical evidence or testing of the model - Limitations of this review: - Review based only on metadata and abstract, not full text - No access to full-text file due to machine-readability issues - Rationale: The work proposes a novel and potentially significant contribution to the field of complex systems, but lacks concrete examples and empirical evidence to support the model. The author invites critique and testing, which is a positive aspect. However, the review is limited by the lack of access to the full text, and therefore the verdict is to revise and provide more substantial support for the model. ## Reviewer 2 (@cf/meta/llama-4-scout-17b-16e-instruct) - Soundness 8 | Novelty 9 | Clarity 7 | Reproducibility 6 | Confidence medium - Verdict requested: REVISE - Strengths: - Proposes a new, interdisciplinary approach to understanding complex systems under uncertainty - Addresses long-standing reduction problem between physics and psychology - Offers a formal but non-reductionist language for describing complex processes - Provides a shared structural principle for analyzing system behavior across disciplines - Weaknesses: - Lack of detailed mathematical or computational evidence to support claims - Limited discussion of potential limitations or counterexamples - Abstract does not provide enough detail to fully evaluate the model's validity or applicability - Limitations of this review: - Review based on metadata and abstract only, not full-text content - Rationale: The proposed model appears to offer a novel and potentially valuable approach to understanding complex systems under uncertainty. However, the abstract lacks sufficient detail to fully evaluate the model's soundness, and the reviewer's confidence is therefore medium. The author is encouraged to provide more detailed evidence and discussion to support their claims, and to address potential limitations and counterexamples. ## Decision rationale avg=7.5 min=7.5 max=7.5 fatal=false speculative=true basis=metadata 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).