activity
20242026
most citedAD-LLM: Benchmarking Large Language Models for Anomaly Detection

3 citations · 3 across the 14 of their papers we have counts for

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Showing 2026Show all

7 papers · 1 filter

cs.AI2026

Auditable Claims about AI Agents

Yue Zhao, Jiate Li, Li Li +4

Organizations make claims about their AI agents: a person approves every external email, every action is logged, an evaluation shows the agent is safe to deploy. Article 12 of the…

cs.CL2026

Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Writing

Shenzhe Zhu, Haoqian Zhang, Xu Yang +7

Teachers, conference chairs, and public readers all judge writing from limited evidence, seeing only a finished document and not the process that produced it. Final text alone cann…

cs.AI2026

MemoHarness: Agent Harnesses That Learn from Experience

Yue Huang, Wenjie Wang, Han Bao +7

An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. Whil…

cs.LG2026

DOG-DPO:Dynamic Optimization in Geometry for Safety Alignment

Yi Nian, Tiankai Yang, Yudi Zhang +7

Safety alignment for large language models relies on preference data, but current pipelines often train on large, redundant datasets. Existing data selection methods typically scor…

cs.CL2026

Cat-DPO: Category-Adaptive Safety Alignment

Tiankai Yang, Yi Nian, Xinyuan Li +6

Aligning large language models with human preferences must balance two competing goals: responding helpfully to legitimate requests and reliably refusing harmful ones. Most prefere…

cs.CL2026

No Attacker Needed: Unintentional Cross-User Contamination in Shared-State LLM Agents

Tiankai Yang, Jiate Li, Yi Nian +5

LLM-based agents increasingly operate across repeated sessions, maintaining task states to ensure continuity. In many deployments, a single agent serves multiple users within a tea…

cs.AI2026

When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Auditing

Yi Nian, Haosen Cao, Shenzhe Zhu +4

When a multi-agent system produces an incorrect or harmful answer, who is accountable if execution logs and agent identifiers are unavailable? In practice, generated content is oft…