Evaluating human-AI co-design requires testing whether coding agents can translate real users'multimodal requests into working application revisions. We introduce Co-Design Bench, a reference-free benchmark of 273 tasks curated from 37,463 deduplicated queries across 26,314 real user coding sessions. Its two tracks, Mu...
Xi-Lin Wei, Sheng-Yuan Ding, Xinyu Fang et al.· 0 citations
Recent advances in multimodal large language models (MLLMs) have enabled them to act as autonomous agents, creating and editing 3D models via code or direct software manipulation. In practical workflows, these models must infer object shapes, materials, spatial layouts, and lighting from diverse references while strict...
Xinyu Fang, Yu-Chi Wang, Wei-Hao Yu et al.· 0 citations
Beyond basic tool use and coding skills, effectively incorporating visual references into agentic generation is equally essential for applying AI agents to real-world productivity tasks. We study this crucial capability through webpage development, a practical and readily evaluable setting in which existing benchmarks...
Yu-Chi Wang, Rongyao Fang, Wei-Hao Yu et al.· 0 citations
This work introduces UniME-R1, an embedder-adviser framework that learns to reason over initially retrieved candidates and generate Retrieval-Centric Chain-of-Thought (RC-CoT), and aligns the adviser with retrieval outcomes through supervised learning and retrieval-oriented reinforcement learning.
Ze-Long Sun, Jun Wang, Kaicheng Yang et al.· 0 citations
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