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Beidi Chen

ICLR 2026×4ACL 2026×2可解释性效率与系统强化学习与后训练对齐与偏好推理
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合作网络9
0 一作 · 6 篇 · ICLR 2026 / ACL 2026

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2026 顶会论文

6
作者位次 #3/3ICLR 2026Poster

Prosperity before Collapse: How Far Can Off-Policy RL Reach with Stale Data on LLMs?

Haizhong Zheng · Jiawei Zhao · Beidi Chen

强化学习与后训练OpenReview
作者位次 #9/10ACL 2026Poster· Natural Language Processing

M ed V erse: Efficient and Reliable Medical Reasoning via DAG -Structured Parallel Execution

Jianwen Chen · Xinyu Yang · Peng Xia · Arian Azarang · Yueh Z Lee · Gang Li · Hongtu Zhu · Yun Li · Beidi Chen · Huaxiu Yao

摘要

Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks.However, their sequential autoregressive decoding forces inherently parallel clinical reasoning, such as differential diagnosis, into a single linear reasoning path, lim...

效率与系统推理可解释性OpenReview 项目页
作者位次 #9/9ACL 2026Poster· Natural Language Processing

When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?

Yibo Peng · James Song · Lei Li · Xinyu Yang · Mihai Christodorescu · Ravi Mangal · Corina S. Pasareanu · Haizhong Zheng · Beidi Chen

摘要

Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively on functional correctness. In this paper, we reveal a novel type of threat to real-world code-agents: functionally correct yet vulnerable (FCV) patch...

安全智能体与工具评测与基准OpenReview 项目页

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