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Michael R. Lyu

ACL 2026×4KDD 2026×2检索与知识代码评测与基准推理安全
新星分资深
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方向广度13
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0 一作 · 6 篇 · ACL 2026 / KDD 2026

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2026 顶会论文

6
作者位次 #3/3ACL 2026Poster· Natural Language Processing

From Laboratory to Real-World Applications: Benchmarking Agentic Code Reasoning at the Repository Level

Jia Li · Yuxin Su · Michael R. Lyu

摘要

As large language models (LLMs) evolve into autonomous agents, evaluating repository-level reasoning, the ability to maintain logical consistency across massive, real-world, interdependent file systems, has become critical. Current benchmarks typically fluctuate between isolated code snippets and...

代码推理评测与基准OpenReview 项目页
作者位次 #7/7ACL 2026Findings· Findings of ACL

Understanding Secret Leakage Risks in Code LLM s: A Tokenization Perspective

Meifang Chen · Zhe Yang · Huang Nianchen · Yizhan Huang · Yichen LI · Zihan Li · Michael R. Lyu

摘要

Code secrets are sensitive assets for software developers, and their leakage poses significant cybersecurity risks. While the rapid development of AI code assistants powered by Code Large Language Models (CLLMs), CLLMs are shown to inadvertently leak such secrets due to a notorious memorization p...

代码安全数据与预训练OpenReview 项目页
作者位次 #8/8ACL 2026Findings· Findings of ACL

Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification

Yuxuan Wan · Tianqing Fang · Zaitang LI · Yintong Huo · Wenxuan Wang · Haitao Mi · Dong Yu · Michael R. Lyu

摘要

Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving.While the majority of existing efforts focus on enhancing policy capabilities via post-training, we propose an alternative paradigm: test-time self-evolving the agent’s ability by ite...

强化学习与后训练评测与基准检索与知识OpenReview 项目页
作者位次 #10/10ACL 2026Findings· Findings of ACL

Identifying the Achilles’ Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models

Wenxuan Wang · Yuk-Kit Chan · Zixuan Ling · Shi Juluan · Youliang Yuan · Jen-Tse Huang · Yifei Zhang · Wenxiang Jiao · Zhaopeng Tu · Michael R. Lyu

摘要

Large Language Models (LLMs) like ChatGPT are foundational in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone to generating factual and commonsense errors, raising concerns in critical areas like healthcare, journalism, and edu...

评测与基准检索与知识社会与人本OpenReview 项目页

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