OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
Score
81论文首次提出覆盖ToC/ToB/ToE的通用智能体基准，填补了现有基准场景单一的空缺，信息增量显著。
方法设计严谨，分类体系层次分明，任务合成途径多样，但数据集规模尚可进一步扩展。
对评估和改善通用AI智能体在实际多场景中的表现具有直接指导意义，为开发者提供了诊断工具。
当前LLM向Agent演进是热点，论文及时提出系统化的评估框架，时效性极强。
来自arXiv预印本，作者团队背景可信，方法透明可复现，但尚未经同行评审。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper proposes a broad and meticulously designed evaluation framework, which is valuable for understanding and improving general agent capabilities.
AI commentary
「This paper is a must-read for any researcher or engineer working on LLM agent development or evaluation. It not only provides a rich task set covering ToC, ToB, and ToE, but also uses a ten-dimensional capability taxonomy and difficulty factors to precisely pinpoint current model weaknesses—planning, constraint maintenance, and adaptive correction. If you want to know where your agent's upper bound lies in real-world scenarios, OmniaBench is an excellent litmus test.」
Quick overview
Large language models are evolving from text generators into general agents, yet existing benchmarks focus on limited scenarios. OmniaBench covers ToC, ToB, and ToE with 90 level-1 and 354 level-2 domains, synthesizing 1,431 tasks via four routes. It introduces a ten-dimensional capability taxonomy and eight compositional atomic difficulty factors. State-of-the-art models like Claude-Sonnet-5 and GPT-5.6-Sol achieve Overall Pass@1 scores of only 58.54 and 57.14, revealing persistent limitations in planning, constraint maintenance, and adaptive correction.
- OmniaBench constructs a hierarchical scenario knowledge taxonomy covering ToC/ToB/ToE from app stores, product documents, etc.
- Synthesizes 1,431 single-turn and multi-turn tasks via four complementary routes: DAG, DAG-S, Solver, and Program.
- Introduces a ten-dimensional capability taxonomy and eight compositional atomic difficulty factors for fine-grained evaluation.
- State-of-the-art models show persistent limitations in planning, constraint maintenance, and adaptive correction, with highest pass rates around 58%.
Full text
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