Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking
Xinshuai Guo, Junjie Wu, Dolly Deng, Yinghui Li, Hai-Tao Zheng, Suncong Zheng, Maxm Pan
cs.CL · cs.AI
Quick verdict
DualViewEval claims SOTA agent-benchmark compression via outcome+process relational learning; five-benchmark results look strong but rest on tiny agent pools without significance testing.
Summary
The paper addresses the high cost of agent benchmarking by compressing benchmarks: selecting a small task miniset from which full-benchmark scores and rankings of unseen agents can be predicted. The authors analyze trajectories from five benchmarks (BFCL, tau2-Bench, Terminal-Bench 2, SWE-bench Verified, APEX-Agents; ~132K agent-task outcome pairs), derive six benchmark-agnostic process metrics from twelve candidates (agent steps, tool failed rate, tool-category entropy, validation-tool rate, required-write execution, read-write-validate closure), and propose DualViewEval. For each task it builds agent-pair affinity matrices from outcomes and process profiles; a straight-through hard Top-K gate learns an exact-size miniset whose fused kernel trains a Kernel Ridge predictor with score and ranking losses under out-of-fold cross-fitting. Across five benchmarks and five compression baselines at budgets 20/40/60, it reports the best MAE and Kendall's tau in every cell, e.g., 34.3% MAE reduction over SparseEval on APEX-Agents at K=20 and 24x-40x compression on APEX/BFCL. Ablations support each component; a held-out Qwen-family test suggests transfer. Main limitations: test splits contain as few as ~4 agents, ranking metrics are statistically fragile, no significance tests are reported, process metrics were selected via correlation with the predicted scores, and no code release is stated.
Review complete
Assessment at a glance
6
risk flags identified
● high 1
● medium 3
● low 2
4 strengths · 5 weaknesses · 5 author questions
Levels reflect verification complexity — not a quality score. Evolve never rates your paper.
Key claims under review
- Six automatically extractable trajectory metrics (retained from twelve candidates) are systematically associated with agents' full-benchmark scores across five agent benchmarks (Figure 2).
- Jointly modeling outcome and process relations (DualViewEval) selects better minisets than five outcome-only compression baselines, achieving the best MAE and Kendall's tau on all five benchmarks at miniset sizes 20, 40, and 60 (Table 2).
- With 20 tasks, DualViewEval attains 24x-40x compression on APEX-Agents and BFCL while cutting MAE by 14.5-28.2% relative to the strongest competitors (Abstract).
- The learned minisets preserve diagnostic process profiles that reveal capability differences among agents (Figure 11, Tables 6-7).
- DualViewEval trains 4.2x-8.3x faster than SparseEval and transfers to a held-out model family (all seven Qwen configurations on APEX-Agents, Table 4).
Strengths
Novel process view for benchmark compression
Section 3.2 and Appendix B derive six benchmark-agnostic trajectory metrics (steps, tool failed rate, tool-category entropy, validation-tool rate, required-write execution, read-write-validate closure) grounded in a large-scale analysis over ~132K agent-task pairs across five benchmarks (Table 1, Figure 2), grounding task selection in execution behavior rather than final scores alone.
Clean selection-prediction coupling with exact budget
The straight-through hard Top-K gate (Eq. 5-7) guarantees exactly K tasks in the forward pass while propagating gradients, and out-of-fold prediction over B=5 folds (Eq. 9-10) explicitly prevents the selector from being rewarded for self-prediction - a cleaner design than swap heuristics in earlier coreset methods.
Component-complete empirical evaluation
Held-out family generalization and diagnostic release
Weaknesses
Statistically fragile ranking metrics on tiny agent pools
No significance testing behind the 'best on all datasets' claim
Potential circularity in process-metric selection
Internal inconsistencies and polish issues
Upfront-cost framing and availability gaps
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Frequently asked questions
What is DualViewEval?
DualViewEval is a benchmark-compression method for LLM agents. It automatically selects a small miniset of tasks (for example 20 out of 480-800) such that a new agent's full-benchmark score and ranking can be predicted from its performance on the miniset alone. Unlike earlier methods that use only final task outcomes, it also models trajectory process signals (steps, tool failures, validation behavior, etc.) as agent-to-agent relations and optimizes the miniset and score predictor jointly.
How much does DualViewEval reduce agent evaluation cost?
The paper reports 24x-40x compression using 20 tasks on APEX-Agents and BFCL, with 14.5-28.2% lower MAE than the strongest prior compression methods, and 4.2x-8.3x faster training than SparseEval. Caveat: the method still requires full-benchmark scores and trajectories for the training agents upfront; the savings apply to evaluating new agents.
What process metrics does DualViewEval extract from agent trajectories?
Six benchmark-agnostic statistics: agent steps (trajectory length), tool failed rate, tool-category entropy (diversity across read/write/validation/other calls), validation-tool rate, required-write execution (whether any write occurs on write-required tasks), and read-write-validate closure (the fraction of write episodes preceded by observed context and followed by successful validation).
Is code or data available for DualViewEval?
As stated in the reviewed text, the authors release aggregate process profiles for APEX-Agents and SWE-bench Verified (appendix tables), but no code release is announced. Reproducing the method would require collecting leaderboard outcomes and trajectories for five benchmarks and re-running APEX-Agents on 26 models.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 1-2 and 5-11 are present only as extracted text fragments; their quantitative trends (selector ablation, K-sweep, gamma distributions, process-profile shifts) cannot be independently verified from the text.
- Minor extraction artifacts exist (e.g., logit init 'N(0, 0.012)' likely means 0.01 squared; APEX tau '0.8000' printed with four decimals); conclusions rely on tables that extracted cleanly.
- Appendix B matching rules are summarized only; the reliability of automatic metric extraction across heterogeneous trajectory formats could not be independently assessed.
- Two unresolved citations appear as '(?)' (tau-bench; the Top-K reference), and several cited works are dated 2026 and unverifiable here.
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