Harness Evolution Underperforms Simpler Scaling Baselines
This research paper and video analyze automatic harness evolution, revealing that current methods often overfit to specific tasks due to shared search and evaluation benchmarks. Under equivalent inference budgets, the technique fails to consistently outperform simpler test-time scaling baselines.
Automatic harness evolution is largely an illusion of progress caused by leaky evaluation protocols and a lack of proper search-budget baselines.
* **Overfitting by Design:** Because the harness is optimized on the target task, the agent essentially "teaches to the test," resulting in poor generalizability to held-out tasks.
* **Inference Budget Inflation:** Much of the reported gain vanishes when comparing harness evolution to simple test-time scaling (like self-correction or multiple attempts) that uses the same computational budget.
* **Flawed Evaluations:** Future agent research must adopt strict, held-out evaluation tasks and report performance as a function of the total inference budget.
DISCOVERED
1h ago
2026-07-16
PUBLISHED
1h ago
2026-07-16
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