Papers by Sean Du
Agentic-R1: Distilled Dual-Strategy Reasoning (2025.emnlp-main)
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| Challenge: | Current long chain-of-thought models rely on slow and error-prone natural language traces. |
| Approach: | They propose a framework that distills complementary reasoning strategies from multiple teachers into a unified student model. |
| Outcome: | The proposed framework improves accuracy on computation-intensive tasks and reduces inference latency on standard benchmarks. |
Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities (2026.acl-long)
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Changdae Oh, Seongheon Park, To Eun Kim, Jiatong Li, Wendi Li, Samuel Yeh, Sean Du, Hamed Hassani, Paul Bogdan, Dawn Song, Sharon Li
| Challenge: | Uncertainty quantification (UQ) for large language models is a key building block for daily applications. |
| Approach: | They propose a general formulation of agent UQ that subsumes broad classes of existing UQ setups. |
| Outcome: | The proposed framework is based on the first general formulation of agent UQ that subsumes broad classes of existing setups. |
VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation (2026.findings-acl)
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| Challenge: | Existing self-evaluation methods rely on a model’s ability to estimate the correctness of its own outputs, but they depend heavily on language priors and are therefore ill-suited for evaluating vision-conditioned predictions. |
| Approach: | They propose a vision-aware uncertainty quantification framework that measures how strongly a model’s output depends on visual evidence. |
| Outcome: | The proposed framework outperforms existing methods across multiple datasets. |