2 citations · 2 across the 10 of their papers we have counts for
10 papers
Decoupled Physical Modeling and Execution for Physics Reasoning
Ye Zhang, Xuehang Guo, Rui Pan +4
Physics reasoning requires constructing a consistent model of the underlying physical system rather than relying solely on symbolic or formula-based manipulation. Although large la…
Brick-Composer: Using MLLMs for Assembly with Diverse Bricks
Jiateng Liu, Bingxuan Li, Zhenhailong Wang +8
We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks. As a first step toward this vision, we study whether multimoda…
Augmenting Interface Usability Heuristics for Reliable Computer-Use Agents
Jiateng Liu, Rushi Wang, Bingxuan Li +6
Recent advances have enabled general computer-use agents that interpret screens and execute grounded actions from human instructions, yet they still struggle to generalize to unsee…
OSExpert: Computer-Use Agents Learning Professional Skills via Exploration
Jiateng Liu, Zhenhailong Wang, Rushi Wang +6
General-purpose computer-use agents have shown impressive performance across diverse digital environments. However, our new benchmark, OSExpert-Eval, indicates they remain far less…
Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contexts
Rushi Wang, Jiateng Liu, Cheng Qian +6
Incorporating external context can significantly enhance the response quality of Large Language Models (LLMs). However, real-world contexts often mix relevant information with disp…
Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation
Chenkai Sun, Denghui Zhang, ChengXiang Zhai +1
Given the growing influence of language model-based agents on high-stakes societal decisions, from public policy to healthcare, ensuring their beneficial impact requires understand…
Atomic Reasoning for Scientific Table Claim Verification
Yuji Zhang, Qingyun Wang, Cheng Qian +7
Scientific texts often convey authority due to their technical language and complex data. However, this complexity can sometimes lead to the spread of misinformation. Non-experts a…
RM-R1: Reward Modeling as Reasoning
Xiusi Chen, Gaotang Li, Ziqi Wang +9
Reward modeling is essential for aligning large language models with human preferences through reinforcement learning. To provide accurate reward signals, a reward model (RM) shoul…
SafeSwitch: Steering Unsafe LLM Behavior via Internal Activation Signals
Peixuan Han, Cheng Qian, Xiusi Chen +3
Large language models (LLMs) exhibit exceptional capabilities across various tasks but also pose risks by generating harmful content. Existing safety mechanisms, while improving mo…
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents
Cheng Qian, Peixuan Han, Qinyu Luo +9
Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting creative ada…