OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
Large language models are increasingly evolving from text generators into general agents capable of understanding user requests, invoking external tools, and completing complex tasks through interaction. However, existing agent benchmarks often focus on limited scenarios, tool ecosystems, or interaction formats, making it difficult to systematically characterize model capabilities across heterogeneous application settings. We introduce OmniaBench, a benchmark for evaluating general agents across diverse scenarios with explicit state spaces. We derive application-oriented scenario knowledge fro
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- PossiblePossibly related (embedding) · 63%ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration →
- FuzzySimilar title/name (fuzzy) · 87%NirDiamant/GenAI_Agents →
“Fuzzy title match (0.94): “OmniaBench: Benchmarking General AI Agents Across Diverse Sc” ≈ “NirDiamant/GenAI_Agents””
- FuzzySimilar title/name (fuzzy) · 84%Unity-Technologies/ml-agents →
“Fuzzy title match (0.92): “OmniaBench: Benchmarking General AI Agents Across Diverse Sc” ≈ “Unity-Technologies/ml-agents””
- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- FuzzySimilar title/name (fuzzy) · 59%datawhalechina/hello-agents →
“Fuzzy title match (0.73): “OmniaBench: Benchmarking General AI Agents Across Diverse Sc” ≈ “datawhalechina/hello-agents””
- LinkedLinked via arxiv author · 85%Yunhao Liang →
“OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios”
- LinkedLinked via arxiv author · 85%Chengyu Shen →
“OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios”