1 citations · 1 across the 16 of their papers we have counts for
9 papers · 2 filters
Probing the Misaligned Thinking Process of Language Models
Kaiwen Zhou, Constantin Venhoff, Jonathan Michala +2
Large language models exhibit a growing range of misaligned behaviors such as strategic deception, sandbagging, and self-preservation. As they are increasingly deployed in high-sta…
Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancy
Xu Shen, Zhen Tan, Song Wang +4
Chain-of-thought (CoT) reasoning improves the problem-solving ability of large language models (LLMs), but generated reasoning traces may not faithfully reflect the model's actual…
SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills
Yingtie Lei, Zhongwei Wan, Jiankun Zhang +13
Large language model (LLM) agents accumulate rich episodic trajectories while solving real-world tasks, but it remains unclear whether such experience can be distilled into reusabl…
Stateful Reasoning via Insight Replay
Bin Lei, Caiwen Ding, Jiachen Yang +2
Chain-of-Thought (CoT) reasoning has become a foundation for eliciting multi-step reasoning in large language models, but recent studies show that its benefits do not scale monoton…
EnactToM: An Evolving Benchmark for Functional Theory of Mind in Embodied Agents
Gurusha Juneja, Dylan Lu, Saaket Agashe +7
Theory of Mind (ToM), the ability to track others epistemic state, makes humans efficient collaborators. AI agents need the same capacity in multi agent settings, yet existing benc…
BoolXLLM: LLM-Assisted Explainability for Boolean Models
Du Cheng, Serdar Kadioglu, Xin Wang
Interpretable machine learning aims to provide transparent models whose decision-making processes can be readily understood by humans. Recent advances in rule-based approaches, suc…
Don't Click That: Teaching Web Agents to Resist Deceptive Interfaces
Yilin Zhang, Yingkai Hua, Chunyu Wei +2
Vision-language model (VLM) based web agents demonstrate impressive autonomous GUI interaction but remain vulnerable to deceptive interface elements. Existing approaches either det…
How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning
Zhiyuan Zhai, Xinkai You, Wenjing Yan +1
Reasoning-capable large language models solve hard problems by emitting long chains of thought, paying heavily in latency, GPU time, and energy. Casual inspection of their traces r…
SafePro: Evaluating the Safety of Professional-Level AI Agents
Kaiwen Zhou, Shreedhar Jangam, Ashwin Nagarajan +7
Large language model-based agents are rapidly evolving from simple conversational assistants into autonomous systems capable of performing complex, professional-level tasks in vari…