AI

Forging More Tasks into Better Harnesses: Agentic Harness Evolution via Successive Scale Racing

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 22/10/2026, 04:03:24 UTC.

Briefings

A new framework called Successive Scale Racing (SSR) optimizes AI agent harnesses by selecting diverse evidence combinations during evolution. Unlike previous methods that fail when task pools expand, SSR improves pass rates from 64.3% to 68.3% as the task pool grows from 40 to 200. SSR achieves state-of-the-art results across three benchmarks and reduces validation executions by 60%.

Key points

• SSR uses a framework to adaptively search for optimal evidence combinations in each evolution round.
• SSR improves pass rates from 64.3% to 68.3% as the task pool grows from 40 to 200.

Context

This research addresses a paradox where increasing task complexity degrades performance in existing agent optimization methods. The study provides empirical evidence that SSR outperforms baselines across multiple benchmarks.

Limitations

The study is based on preliminary analysis from an arXiv preprint. Results are limited to three distinct benchmarks and six configurations. The framework's performance on other benchmarks or with different task pools remains unverified.

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