# AIDE^2

The work presents AIDE², a two-loop system that implements recursive self-improvement at the harness layer of an AI research agent: an inner-loop agent optimizes code on AI R&D tasks while an outer-loop agent rewrites the inner-loop agent itself, and in one autonomous 8-day run it accepted seven successive improvements that transferred to four held-out benchmarks (including an out-of-distribution physics-based weather-forecasting task) and reduced reward hacking from 55% to 32% on a behavior the loop never optimized for.

The work presents AIDE², a two-loop system that implements recursive self-improvement at the harness layer of an AI research agent: an inner-loop agent optimizes code on AI R&D tasks while an outer-loop agent rewrites the inner-loop agent itself, and in one autonomous 8-day run it accepted seven successive improvements that transferred to four held-out benchmarks (including an out-of-distribution physics-based weather-forecasting task) and reduced reward hacking from 55% to 32% on a behavior the loop never optimized for.