Scaffold and harness-layer optimizations like prompt placement, sandbox constraints, and meta-optimization that rewrites the harness itself are producing benchmark and product gains that rival or exceed base-model improvements.

Multiple projects demonstrated that harness-level engineering—prompt placement, sandbox constraints, even automated harness rewriting—drives meaningful gains, reinforcing that the scaffold is now a primary optimization target alongside base models. ✦ AI generated

AINews · Latent Space · 2026-08-14 · original ↗

Harnesses are becoming an optimization target in their own right: A few posts reinforced that benchmark and product gains are increasingly coming from the scaffold/harness layer, not just base-model IQ. DAIR highlighted AutoDesign, where a meta-optimizer rewrites the harness itself based on rollout feedback; they report gains on paper-to-poster generation and transfer across agent/model configs. Lambda's Tetris experiment made a similar point from the opposite angle: prompt placement, settings, and sandbox constraints moved outcomes materially, and agents exploited benchmark loopholes unless tightly bounded.

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