Vision-language-action (VLA) and world-action (WAM) models map observations and instructions directly to robot actions. This directness ties a policy to training: minor layout or viewpoint changes cause failure, and instructions generalize poorly. The root cause lies in representation: task requirements, conditions, pr...
Hong-Cheng Gao, Jing-Jing Zhou, Ze Zheng et al.· 0 citations
Frontier models can control robots, but reasoning through every reach, grasp, and retreat makes manipulation slow and token-intensive. We revisit code as policy with a different division of labor: models build executable tools, code handles multi-phase motions, and models decide what to do next. We introduce URAI (Univ...
S. Ge, Alex Zhou, Jian-Shu Zeng et al.· 0 citations
EngiWorld provides the first rigorous foundation for measuring progress toward agents that operate professional engineering software end to end, and introduces an artifact-centric evaluation methodology built on a unified domain-verifier suite.
Hong-Cheng Gao, Hai-Long Qu, Yu-Ang Lei et al.· 0 citations
A one-round study provides initial evidence for PRD-guided self-evolution, motivating validation at larger scales and in industrial settings, and presents AgentOmnia, a framework coordinating task-space definition, data synthesis, post-training, evaluation, and improvement across To-Consumer (ToC), To-Business (ToB), a...
OmniaBench provides a broad and diagnostic benchmark for characterizing the capability boundaries of general agents across diverse scenarios with explicit state spaces, and introduces a ten-dimensional capability taxonomy and eight compositional atomic difficulty factors to support fine-grained evaluation and analysis.
