Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking
DualViewEval compresses agent benchmarks by jointly modeling outcome and process signals, achieving 24x-40x compression with only 20 tasks on APEX-Agents and BFCL.
DualViewEval is an agent benchmark compression method that jointly exploits outcome and process relations from trajectories to learn exact-size minisets predicting full-benchmark scores. The authors analyze large-scale trajectories and identify six process signals systematically associated with final agent performance. Across five agent benchmarks and five baselines, it achieves the best results on all datasets: with only 20 tasks it reaches 24x-40x compression on APEX-Agents and BFCL, reduces MAE by 14.5%-28.2% over the strongest competitors, and improves Kendall's tau by up to 7.2% relative to EssenceBench on SWE-bench Verified.
- Identifies six trajectory process signals correlated with final agent performance
- Learns exact-size minisets predicting full-benchmark scores from outcome and process relations
- 20-task minisets yield 24x-40x compression on APEX-Agents and BFCL
- MAE reduced 14.5%-28.2% versus strongest competitors
From the original article
1 min read at arxiv.orgAgent 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.
By Xinshuai Guo · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.