Gear open-source framework targets recursive AI self-improvement
Gear developers introduced an open-source, meta-learning-inspired framework in which a task agent produces execution traces and a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. The framework separates search decisions from execution and evaluation and includes a working GEPA variant with shared failure diagnosis and staged evaluation. Its stated goal is to make methods such as GEPA, RRSI, and Harness² easier to implement and compare. Developers reported higher task pass rates with Gear-optimized configurations using GPT 5.6 Luna at medium reasoning effort, but supplied no independent measurements.
Gear provides an open-source implementation layer intended to make recursive self-improvement methods easier to develop and compare. Initial performance results came only from the developers and have not been independently validated.
| After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search: That motivated us to build Gear, an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI, making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluation. With GPT 5.6 Luna held at medium reasoning effort, we observed these task pass rates: At max effort on Marketing, Luna with the optimized harness reached 88.88% objective completion, versus 84.08% for Astra + Codex. Strict task pass rates were 53% versus 57%, so the advantage was in partial completion. Separately, with both models using the same optimized DSH at max effort, Luna’s estimated API cost averaged $0.0568 per task versus $1.1858 for Astra—95.21% lower, excluding optimization and infrastructure costs. We’d welcome feedback on candidate evaluation and diversity preservation under limited budgets. Code · Paper submitted by /u/Pale-Training-6944 [link] [comments] |
#Gear#GEPA#RRSI#Harness²#GPT 5.6 Luna#Astra#Codex#AutomationBench#Terminal-Bench 2.1