SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
SoL-Pi scales auto-research loops to produce an agent harness matching Pi while cutting token traffic 44.7-49.0% and API cost by a third.
The paper takes an RSI-inspired approach at the harness layer, scaling auto-research loops across increasingly numerous and diverse environments to yield reusable improvements that transfer beyond their development setting. Four mechanisms survived selection to form SoL-Pi, spanning action execution, context compaction, observation handling, and delegated reading. On the 51-task EdgeBench evaluation, SoL-Pi achieves performance comparable to Pi across GPT-5.6 Sol and Opus 5 while reducing recorded token traffic by 44.7-49.0% and API cost by about one third. Estimated hourly savings are $8.75-13.50 relative to native Codex and Claude Code harnesses.