CORE distills contrasts between successful and unsuccessful reasoning traces into compact natural-language insights that enable faster model self-improvement on reasoning tasks with fewer rollouts than parametric or other non-parametric baselines.
Metacognitive reuse: Turning recurring llm reasoning into concise behaviors.ArXiv preprint arXiv:2509.13237
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Recovering an orthogonal basis from model activations yields a model-native skill characterization that improves reasoning Pass@1 by up to 41% via targeted data selection and supports inference steering, outperforming human-characterized alternatives.
MILES dynamically expands step-wise memory with learnable selection heads that rerank candidates and guide reasoning, improving LLM test-time performance under limited supervision.
MESA-S framework translates human metacognitive control into LLMs via delayed procedural probes and Metacognitive Skill Cards to separate parametric certainty from source trust and reduce overthinking.
citing papers explorer
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CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning
CORE distills contrasts between successful and unsuccessful reasoning traces into compact natural-language insights that enable faster model self-improvement on reasoning tasks with fewer rollouts than parametric or other non-parametric baselines.
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Characterizing Model-Native Skills
Recovering an orthogonal basis from model activations yields a model-native skill characterization that improves reasoning Pass@1 by up to 41% via targeted data selection and supports inference steering, outperforming human-characterized alternatives.
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MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning
MILES dynamically expands step-wise memory with learnable selection heads that rerank candidates and guide reasoning, improving LLM test-time performance under limited supervision.
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Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMs
MESA-S framework translates human metacognitive control into LLMs via delayed procedural probes and Metacognitive Skill Cards to separate parametric certainty from source trust and reduce overthinking.