AIDE²: a research agent rewrote its own code seven times, unsupervised

A paper posted to arXiv on 22 September, "Recursive self-improvement of ai research agents", describes AIDE², a system that proposes changes to its own code, tests the modified versions of itself on ai research tasks and keeps whatever wins on hidden evaluations.

In an autonomous eight-day run it found seven successive improvements, including a new search policy and ways to compress and manage its own growing context.

The results

• The gains carried over to four held-out benchmarks, including physics-based weather forecasting, which was outside the tasks it trained on.
• The strongest version matched or beat a human-built production research agent.
• Reward hacking fell from 55% to 32% across the run, even though the loop never aimed for that.

The authors are Dhruv Srikanth, Bingchen Zhao, Dixing Xu, Yuxiang Wu and Zhengyao Jiang.

Why it matters

Most coverage of recursive self-improvement this week was about doom. This is a concrete result: an agent's research ability compounding, and carrying over to new fields. Capability is starting to compound on its own, which makes the open access we have today matter more.