DualViewEval selects fixed-size benchmark minisets using both final outcomes and six trajectory-level process signals. With 20 tasks, the method reports 24x to 40x compression on APEX-Agents and BFCL. It reduces mean absolute error by 14.5% to 28.2% over the strongest baselines and improves ranking correlation on SWE-bench Verified. The study spans five agent benchmarks and includes held-out model-family tests.
DualViewEval compresses costly agent benchmarks with process signals
DualViewEval selects fixed-size benchmark minisets using both final outcomes and six trajectory-level process signals. With 20 tasks, the method reports 24x to 40x compression on APEX-Agents and BFCL. It reduces mean absolute error by 14.5% to 28.2% over the strongest baselines and improves ranking correlation on SWE-bench Verified. The study spans five agent benchmarks and includes held-out model-family tests.
Imported from the existing research archive. Migration preserves the recorded judgment and is not a fresh verification.
What this means for you
For teams that cannot rerun full agent suites on every release, process-aware subsets could turn evaluation from an occasional audit into a routine gate. Keep a rotating full-suite audit because a learned miniset can age as models, scaffolds and task distributions change. The absence of public code raises the cost of independent verification.
Public artifacts and validated results are different.
The recorded fact is the public work and its artifacts. Author-reported outcomes do not establish independent reproduction. Next checks include usable code, task conditions and third-party validation.
Evidence & related material
Technical details & scores
| Method | joint outcome and six process-signal relations |
|---|---|
| Miniset Size | 20 |
| Compression | 24x-40x on APEX-Agents and BFCL |
| Error Reduction | 14.5%-28.2% MAE versus strongest baselines |
| Coverage | five agent benchmarks; held-out Qwen-family generalization test |