Selected work

Representative Papers

Tiny Moves: Game-based Hypothesis Refinement

Feb 10, 2026

This work addresses a key limitation in traditional machine learning approaches, which often model scientific hypotheses as monolithic end-to-end predictions and thereby overlook their inherently incremental and structured reasoning process. To bridge this gap, the authors propose the “Hypothesis Game” framework, which formalizes hypothesis refinement as a turn-based game mechanism for the first time. Within this framework, multiple large language model agents collaboratively perform localized, incremental revisions on a shared hypothesis state, guided by a symbolic grammar of reasoning actions. Emphasizing small, context-sensitive modifications rather than global rewrites, the approach significantly outperforms strong prompting baselines on error-correction tasks—achieving higher accuracy while better preserving the original hypothesis structure—and matches their performance on partial-clue reconstruction tasks, demonstrating both competitive efficacy and enhanced interpretability.

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When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff

Sep 04, 2025

This paper addresses performance degradation in realistic batch active learning scenarios caused by correlated heteroscedastic aleatoric uncertainty. Methodologically, it proposes a novel bias–variance trade-off–driven active learning framework: (1) a cobias–covariance joint modeling mechanism to disentangle distinct uncertainty sources; (2) a feature-decomposition–based batch selection strategy that bypasses conventional information-theoretic heuristics (e.g., entropy or confidence); and (3) a historical-data–augmented double estimation scheme with a three-stage bias correction procedure. The key contribution is the first systematic integration of bias–variance decomposition into heteroscedastic batch sampling, substantially improving robustness to input-dependent noise and sampling efficiency. Empirical evaluation across multiple benchmark tasks demonstrates consistent superiority over strong baselines—including BALD and Least Confidence—effectively mitigating model performance deterioration induced by correlated aleatoric uncertainty.

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Recent publications

Latest Papers

Tiny Moves: Game-based Hypothesis Refinement

Feb 10, 2026

This work addresses a key limitation in traditional machine learning approaches, which often model scientific hypotheses as monolithic end-to-end predictions and thereby overlook their inherently incremental and structured reasoning process. To bridge this gap, the authors propose the “Hypothesis Game” framework, which formalizes hypothesis refinement as a turn-based game mechanism for the first time. Within this framework, multiple large language model agents collaboratively perform localized, incremental revisions on a shared hypothesis state, guided by a symbolic grammar of reasoning actions. Emphasizing small, context-sensitive modifications rather than global rewrites, the approach significantly outperforms strong prompting baselines on error-correction tasks—achieving higher accuracy while better preserving the original hypothesis structure—and matches their performance on partial-clue reconstruction tasks, demonstrating both competitive efficacy and enhanced interpretability.

0 citationsRead paper

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff

Sep 04, 2025

This paper addresses performance degradation in realistic batch active learning scenarios caused by correlated heteroscedastic aleatoric uncertainty. Methodologically, it proposes a novel bias–variance trade-off–driven active learning framework: (1) a cobias–covariance joint modeling mechanism to disentangle distinct uncertainty sources; (2) a feature-decomposition–based batch selection strategy that bypasses conventional information-theoretic heuristics (e.g., entropy or confidence); and (3) a historical-data–augmented double estimation scheme with a three-stage bias correction procedure. The key contribution is the first systematic integration of bias–variance decomposition into heteroscedastic batch sampling, substantially improving robustness to input-dependent noise and sampling efficiency. Empirical evaluation across multiple benchmark tasks demonstrates consistent superiority over strong baselines—including BALD and Least Confidence—effectively mitigating model performance deterioration induced by correlated aleatoric uncertainty.

0 citationsRead paper