3 papers
cs.AI2026
AgentOmnia: Scaling Agentic Models for Full-Scenario Applications
Hao Jiang, Gangtao Xin, Yingdi Huang +35
Large language model agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings. We frame this as full-sc…
cs.CL2026
OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
Chengyu Shen, Yujie Fu, Gangtao Xin +13
Large language models are increasingly evolving from text generators into general agents capable of understanding user requests, invoking external tools, and completing complex tas…
cs.AI2026
PaperScope: A Multi-Modal Multi-Document Benchmark for Agentic Deep Research Across Massive Scientific Papers
Lei Xiong, Huaying Yuan, Zheng Liu +2
Leveraging Multi-modal Large Language Models (MLLMs) to accelerate frontier scientific research is promising, yet how to rigorously evaluate such systems remains unclear. Existing…