SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
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TL;DR - SoL-Pi is an agent harness developed through recursively scaled auto-research loops to make long-running coding agents more efficient. On EdgeBench, it matched Pi’s performance while cutting token traffic by 44.7–49.0% and API costs by roughly one-third.
- Its four selected mechanisms improve action execution, context compaction, observation handling, and delegated reading.
- The approach searches across increasingly numerous and diverse rollout environments for reusable harness improvements.
- Results span the 51-task EdgeBench evaluation using GPT-5.6 Sol and Opus 5.
- Estimated hourly savings are $8.75–$13.50 versus native Codex and Claude Code harnesses, and $4.36–$5.71 versus Pi.
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SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
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Hugging Face upvotes 130
TL;DR - SoL-Pi is an agent harness developed through recursively scaled auto-research loops to make long-running coding agents more efficient. On EdgeBench, it matched Pi’s performance while cutting token traffic by 44.7–49.0% and API costs by roughly one-third.
- Its four selected mechanisms improve action execution, context compaction, observation handling, and delegated reading.
- The approach searches across increasingly numerous and diverse rollout environments for reusable harness improvements.
- Results span the 51-task EdgeBench evaluation using GPT-5.6 Sol and Opus 5.
- Estimated hourly savings are $8.75–$13.50 versus native Codex and Claude Code harnesses, and $4.36–$5.71 versus Pi.