6 citations · 9 across the 17 of their papers we have counts for
7 papers · 1 filter
MiniCorp: The Last Mile to an AI Agent Corporation
Jingying Zeng, Zhenwei Dai, Jinning Li +6
The last mile toward enterprise AGI is a company that runs itself. Training and adapting such agents require longitudinal enterprise data, which remain scarce, costly to acquire, a…
The Tasteful Agent: Measuring and Improving Taste in Long-Horizon Tasks
Wenbo Pan, Zhichao Liu, Shujie Liu +6
LLM agents increasingly work on long-horizon tasks, and the decisions they make along the way, such as which hypothesis to test or which implementation to build on, determine the o…
OmniCAD: A Large-Scale Benchmark for 3D Spatial Reasoning in Robotics Assemblies
Mingjia Wang, Taiting Lu, Ziwei Dong +25
Recent vision-language models (VLMs) show strong capabilities in robotic perception and spatial reasoning, yet their ability to reason about complex mechanical assemblies remains u…
OmniMech: All-in-one Multimodal Mechanical Benchmark for 3D Reconstruction
Taiting Lu, Runze Liu, Ziwei Dong +18
Recent vision-language models (VLMs) can generate executable CAD programs from images, but existing methods mainly target coarse, general-purpose 3D objects and rarely address the…
OmniRouting: A Semantic-Coupled Multimodal Benchmark for Constraint-Aware Spatial Reasoning in PCB Routing
Taiting Lu, Kaiyuan Lin, Ziwei Dong +18
Recent large language models (LLMs) have demonstrated remarkable progress in constraint-aware navigation, maze reasoning, and graph reasoning. However, their ability to reason abou…
Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference
Wenbo Pan, Shujie Liu, Chin-Yew Lin +5
AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually improving this harness is essential for adapting to new tasks. However, existing…
How Far Are LLMs from Professional Poker Players? Revisiting Game-Theoretic Reasoning with Agentic Tool Use
Minhua Lin, Enyan Dai, Hui Liu +11
As Large Language Models (LLMs) are increasingly applied in high-stakes domains, their ability to reason strategically under uncertainty becomes critical. Poker provides a rigorous…