Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking
Agent benchmarks are substantially more costly to evaluate than conventional LLM benchmarks. Benchmark compression is therefore a natural solution, yet existing methods primarily model redundancy in task--model final-score distributions, which is important in agentic evaluation. To address this limitation, we analyze large-scale trajectories and identify six complementary process signals that are systematically associated with final agent performance. To disentangle agent performance redundancy from a complete perspective, we propose DualViewEval, an agent benchmark compression method that jointly exploits outcome and process relations to learn an exact-size miniset and predict the full-b...
| Authors | Xinshuai Guo, Junjie Wu, Dolly Deng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 September 2026 |
| Last updated at source | 3 October 2026 |
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