⚙️ TL;DR: without touching the model, automatically improving the software that runs the agent cuts token cost by roughly half. Title: SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness URL: 📌 Highlights 🔍 Searched ~150 directions across ~500 environments, running 3,000+ trials to discover efficiency mechanisms 🧩 4 mechanisms found, including Action Fusion, which merges a file edit and its test run into one request 📉 On EdgeBench: 49.0% less token traffic, 33.2% lower cost, at 93.7% of baseline performance 🔄 Applied to Opus 5 with zero extra tuning, still keeps 44.7% token and 33.5% cost reduction 💰 On Terminal-Bench 4, cost per solved task drops 11.6% ⏱ Estimated $8.75-13.50/hour savings versus native Codex The interesting part: optimizing the harness instead of the model turns out to be a genuinely production-relevant efficiency lever. #AIAgents# #LLMCostOptimization#