Derives simultaneous finite-sample distribution-free upper bounds on false discovery proportions for conformal p-values that hold for every possible rejection threshold.
Proceedings of the 23rd international conference on Machine learning , pages=
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SCA framework applies Information Bottleneck to assign step-level confidence in black-box LLM reasoning traces, flagging errors and boosting self-correction success by up to 13.5% on math and QA tasks.
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Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
Derives simultaneous finite-sample distribution-free upper bounds on false discovery proportions for conformal p-values that hold for every possible rejection threshold.
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Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution
SCA framework applies Information Bottleneck to assign step-level confidence in black-box LLM reasoning traces, flagging errors and boosting self-correction success by up to 13.5% on math and QA tasks.