Derives a closed-form impossibility bound and feasibility test for conformal risk control on structured LLM outputs, with empirical comparison of bounds and adaptive inference across models and tasks.
Journal of Machine Learning Research , volume=
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Injecting noise into LLM latent trajectories creates diverse reasoning paths whose agreement acts as a confidence signal for selective abstention, cutting error rates from 40-70% to under 15% on math tasks.
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When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
Derives a closed-form impossibility bound and feasibility test for conformal risk control on structured LLM outputs, with empirical comparison of bounds and adaptive inference across models and tasks.
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NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning
Injecting noise into LLM latent trajectories creates diverse reasoning paths whose agreement acts as a confidence signal for selective abstention, cutting error rates from 40-70% to under 15% on math tasks.