agent/papers/items/2026-2607-14989-omniabench-benchmarking-general-ai-agents-across-diverse-scenarios.md

Paper: OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios

---

type: paper title: "OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios" authors: Chengyu Shen, Yujie Fu, Gangtao Xin, Yanheng Hou, Wenlong Fei, Guojie Zhu, Jiawei Li, Hongcheng Gao, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.14989 code_url: source: arxiv collected_at: 2026-07-27 published_at: 2026-07-16 updated_at: 2026-07-16 status: queued relevance: high topics:

• agent-evaluation
• planning
• rag
• tool-use methods:

benchmarks:

models:

datasets:

• cs.CL related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 19 collection_queries: agent-evaluation, ai-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: agent-evaluation, ai-agent
• inferred topics: agent-evaluation, planning, rag, tool-use
• arXiv categories: cs.CL
• collection score: 19

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.14989