AgentOmnia: Scaling Agentic Models for Full-Scenario Applications
Large language model agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings. We frame this as full-scenario agentic scaling and present 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. An extensible Domain x Capability x Atomic Difficulty taxonomy aligns these stages and enables fine-grained diagnosis with OmniaBench. AgentOmnia combines bidirectional environment-task synthesis with tool-dependency, program-structured, and solver-based pipelines, constructing 5,0...
| Authors | Hao Jiang, Gangtao Xin, Yingdi Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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