Build your own harness, folks. This is absolute banger paper from NVIDIA on self-evolving agent harnesses. (bookmark it) They introduce SoL-…

Build your own harness, folks. This is absolute banger paper from NVIDIA on self-evolving agent harnesses. (bookmark it) They introduce SoL-Pi which cuts token traffic by nearly half. And it matches its baseline harness on GPT-5.6 Sol and Opus 5. More details below: Instead of tuning a harness by hand, they run auto-research loops at the harness layer across many repository-derived and verifier-driven environments, keeping only the mechanisms that survive selection. Four mechanisms survived: > Action Fusion changes how actions execute > Online Context Compact handles compaction during a run > ObservationPack reshapes observation handling > Evidence-Preserving Reducer covers delegated reading On the 51-task EdgeBench evaluation, the savings translate to about a third off API cost. In dollars that is an estimated 8.75 to 13.50 per hour against native Codex and Claude Code harnesses, and 4.36 to 5.71 against the baseline harness. Because the search runs across many environments rather than one, the retained mechanisms keep working outside the setting that produced them. Code is on GitHub under NVlabs. Paper: https://arxiv.org/abs/2609.20519 Chat with Paper: https://academy.dair.ai/papers/sol-pi-recursively-scaling-auto-research-loops-for-efficient-agent-harness-2609.20519

Source: DAIR.AI (X) | 2026-09-18

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