Problem
Research questions and friction points this paper is trying to address.
token efficiency
recursive self-improvement
auto-research loops
coding agents
unattended exploration
SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
🤖 AI Summary
为提高编码代理效率，通过SoL-Pi方法在多个环境中递归扩展自动研究循环，减少了44.7-49.0%的token流量和约三分之一的API成本。
📝 Abstract
As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedback. Token efficiency therefore becomes important for scaling recursive self-improvement. We take an RSI-inspired approach at the harness layer, scaling auto-research loops across increasingly numerous and diverse environments for harness rollouts. At this scale, the process yields reusable improvements that transfer beyond their development setting, moving automated harness discovery toward production-level outcomes. Four mechanisms survive selection and 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. In other words, estimated hourly savings are \$8.75-\$13.50 relative to native Codex and Claude Code harnesses, and \$4.36-\$5.71 relative to Pi.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Recursive Self-Improvement
Token Efficiency
Auto-Research Loops
Harness Rollouts
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