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LMBuild: Evaluating LLM Agents for Generating Buildable and Functional Structures
Jiateng Liu, Rushi Wang, Cheng Qian +9
LLM-based agents are increasingly capable of generating complex 3D structures, with the potential to reshape how objects are designed and realized in the physical world. Yet, produ…
Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training
Kunlun Zhu, Xuyan Ye, Yibo Li +3
An unsuccessful LLM agent rollout contains more information than its final reward: the observations available to the agent, the actions it chose, and the environment's responses. R…
Learning Meta-Skills for Agent Harness Design in Test-Time AI4AI
Cheng Qian, Kunlun Zhu, Beibin Li +2
Agent performance depends on both reasoning ability and the environment in which it acts. We study test-time AI-for-AI, asking how a Builder can learn to construct better execution…
PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems
Jiayu Liu, Qihan Lin, Cheng Qian +8
LLM agents increasingly operate in large tool ecosystems, where real-world tasks require discovering relevant tools, inferring implicit sub-goals, and adapting to dynamic environme…
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…
Advancing Creative Physical Intelligence in Large Multimodal Models
Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10
Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to discovering visually grounded…
CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing
Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10
Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative problem-solving remains under…
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…