most citedWhere LLM Agents Fail and How They can Learn From Failures

3 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CR2026

SINGED: Correct Outputs Do Not Certify Safe Execution in LLM Agents

Xiaoyu Xu, Zi Liang, Minxin Du +4

Tool-using language-model agents select and execute third-party artifacts. Different implementations can return the requested output while producing hidden execution effects that t…

cs.CR2026

When Routine Chats Turn Toxic: Unintended Long-Term State Poisoning in Personalized Agents

Xiaoyu Xu, Minxin Du, Qipeng Xie +3

Personalized LLM agents maintain persistent cross-session state to support long-horizon collaboration. Yet, this persistence introduces a subtle but critical security vulnerability…

cs.CV2025

Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding

Jiaqi Tang, Jianmin Chen, Wei Wei +7

Multimodal Large Language Models struggle to maintain reliable performance under extreme real-world visual degradations, which impede their practical robustness. Existing robust ML…

cs.AI2025★ 3 cited

Where LLM Agents Fail and How They can Learn From Failures

Kunlun Zhu, Zijia Liu, Bingxuan Li +15

Large Language Model (LLM) agents, which integrate planning, memory, reflection, and tool-use modules, have shown promise in solving complex, multi-step tasks. Yet their sophistica…

cs.CL2025

Compliance-to-Code: Enhancing Financial Compliance Checking via Code Generation

Siyuan Li, Jian Chen, Rui Yao +8

Nowadays, regulatory compliance has become a cornerstone of corporate governance, ensuring adherence to systematic legal frameworks. At its core, financial regulations often compri…