Validity indices adapted from clinical assessment classify four frontier LLMs as construct-level invalid on metacognitive probes, with valid models showing positive item-sensitive confidence (r=.18) while invalid ones show the opposite (r=-.20).
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SDRL trains LLMs via self-generated multi-path debates and joint optimization of standalone plus debate-conditioned responses to boost both single-model reasoning and multi-agent debate performance.
Language models deploy multidimensional internal confidence representations and threshold-based policies to control abstention behavior, with causal support from activation steering experiments.
citing papers explorer
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Before You Interpret the Profile: Validity Scaling for LLM Metacognitive Self-Report
Validity indices adapted from clinical assessment classify four frontier LLMs as construct-level invalid on metacognitive probes, with valid models showing positive item-sensitive confidence (r=.18) while invalid ones show the opposite (r=-.20).
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Learning from Self-Debate: Preparing Reasoning Models for Multi-Agent Debate
SDRL trains LLMs via self-generated multi-path debates and joint optimization of standalone plus debate-conditioned responses to boost both single-model reasoning and multi-agent debate performance.
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Causal Evidence that Language Models use Confidence to Drive Behavior
Language models deploy multidimensional internal confidence representations and threshold-based policies to control abstention behavior, with causal support from activation steering experiments.