Frontier vision-language models (VLMs) increasingly estimate scenes, ground interactions, and generate executable actions. How far these native capabilities support embodied generalism across diverse tasks remains unclear. We introduce Embodied Agent Arena to assess seven VLM agents across Geometry, Spatial Reasoning,...
Hao-Jian Huang, Pu-Kun Zhao, Ze-Xi Li et al.· 0 citations
RACaP is introduced, an agentic framework that moves coding to evolution and uses a Reasoning-and-Acting loop to call frozen, typed Policy APIs at deployment and shows that separating reusable code from runtime decisions supports continued evolution, effective transfer, and efficient long-horizon control.
Ze-Xi Li, Ye-Hang Zhang, Hao-Jian Huang et al.· 1 citation
A literature review around the interfaces connecting contextual evidence to execution, distinguishing four families: context-conditioned policies, geometric demonstration transfer, world-model-based control, and skill- and agent-based execution.
Hao-Jian Huang, Ze-Xi Li, Ju-Hao Guo et al.· 1 citation
Robo-Harness K1 is introduced, a robot-use agent (RUA) framework that exposes perception as tools that makes 3D geometry accessible without changing the VLM architecture or training a depth encoder, and suggests that perception-augmented RUAs offer a promising route to sample-efficient, generalizable robotic policies t...
Ze-Xi Li, Ye-Hang Zhang, Wen-Qian Li et al.· 2 citations
World Action Agent is presented, a multi-agent harness through which VLMs pilot robots with basic tools, making every decision within a visual action workspace, outperforming end-to-end VLAs, code-as-policy agents, and a visual-harness baseline with the same backbone.
Ye-Hang Zhang, Hao-Jian Huang, Yi-Fan Chang et al.· 0 citations
AffordanceWAM is introduced, an affordance-aware generative World Action Model that represents object-centric spatiotemporal affordance through Scalar Affordance and Affordance Heatmap, within the generated future World, and supports affordance as an effective interface for both vision-language-action learning and huma...
PaW is a Policy and World modeling co-training framework that adds auxiliary WM supervision to the same policy during RL, without changing the inference paradigm, and shows consistent improvements over strong RL baselines across models and RL algorithms.
Ning Lu, Baijiong Lin, Sheng-Cai Liu et al.· arXiv.org· 12 citations· ⚡2
A cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios, and two official Codabench tracks.
Yu-Qian Fu, Tianwen Qian, Yanjun Li et al.· 0 citations
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