AutoCrit - A Meta-Reasoning Framework for Self-Critique and Iterative Error Correction in LLM Chains-of-Thought: Adaptation and Calibration Across Domains

Cross-domain use depends on whether predictions remain calibrated when data sources, populations, and decision thresholds change. This structured evidence review evaluates "AutoCrit: A Meta-Reasoning Framework for Self-Critique and Iterative Error Correction in LLM Chains-of-Thought" alongside nine author-disjoint, topically matched publications in reliable retrieval-augmented generation. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through cross-domain adaptation and calibration, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda emphasizes external calibration, domain-specific error analysis, and predefined rules for recalibration or withdrawal.

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