What Drives LLM Self-Reflection? A Controlled Ablation of Uncertainty Routing in Armed Conflict Forecasting
arXiv CS.AI
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Generative AI
Data Science
AI Research
Self-reflection is widely assumed to improve LLM reasoning, yet which component drives the gain remains poorly understood. We present a controlled six-condition ablation isolating four components of LLM self-reflection: evidence exposure, diagnostic scaffolding, taxonomy vocabulary, and action routing. Two precise null results converge on a single mechanism.