Author

Zhao Cao

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Preprint Jul 2026

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications

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), and To-Employee (ToE) applications.

Hao Jiang, Gangtao Xin, Ying Huang et al. · 0 citations

Preprint Jul 2026

OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios

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.

Chengyu Shen, Yujie Fu, Gangtao Xin et al. · 0 citations