69 citations · 235 across the 60 of their papers we have counts for
8 papers · 1 filter
Learning to Follow In-Context Watermark Instructions via Self-Distillation
Yepeng Liu, Tianyi Chen, Xuandong Zhao +2
In-context watermarking (ICW) prepends an instruction to a query asking the model to embed a statistically detectable signal in its response. It thus equips LLMs with a watermarkin…
MatrAIx: Simulating the World with 8.3 Billion Persona Agents
Xiaomin Li, Yuexing Hao, Jianheng Hou +90
Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and inter…
When Do Intrinsic Rewards Work for Code Reasoning? A Comprehensive Study
Xiaolong Jin, Xuandong Zhao, Wenbo Guo +2
Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in large language model reasoning, but relies on ground-truth supervision that is costly or in…
Reward Hacking in Language Model Agents: Revisiting AI Safety Gridworlds
Ömer Veysel Çağatan, Xuandong Zhao
Reward hacking, where AI systems exploit misspecified objectives to achieve high reward without satisfying intended goals, remains a central challenge in AI safety. Yet most known…
MemFail: Stress-Testing Failure Modes of LLM Memory Systems
Ishir Garg, Neel Kolhe, Dawn Song +1
Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empirical work has been done to und…
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
Xiangyi Li, Yimin Liu, Wenbo Chen +75
Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to m…
SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning
Kaiwen Zhou, Xuandong Zhao, Gaowen Liu +4
Large Reasoning Models (LRMs) introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose…
Multimodal Situational Safety
Kaiwen Zhou, Chengzhi Liu, Xuandong Zhao +3
Multimodal Large Language Models (MLLMs) are rapidly evolving, demonstrating impressive capabilities as multimodal assistants that interact with both humans and their environments.…