Recursive self-improvement of AI research agents

Key points

• Problem: AI agents have improved the efficiency of the artifacts they produce, such as training and inference, but the efficiency of the research process producing them stays fixed. Research and development (R&D) has long shown diminishing returns as cumulative spending grows, and recent self-improving systems show promise in coding and other domains, but how effective their loops are at raising frontier AI research efficiency was unclear.
• Input and output: This is a system paper. The input is the code of a starting research agent, AIDE0, a pared-down version of AIDE (AI-Driven Exploration), and a selection benchmark of three task families, ML engineering, heuristic algorithm engineering and harness engineering, where each task has a public score, private held-out scoring and a fixed cost budget. The output is the code of the current best research agent (the incumbent), which changes whenever the grade improves, and the edited target is the harness, the code around the model that controls search, context and verification.
• Method: In the inner loop, a research agent edits task code while seeing public scores, and when its budget is spent the solution it picks is scored on hidden data; the average of hidden scores across tasks becomes the agent's grade. The outer-loop agent reads earlier agents and their grades, rewrites the code of the current best agent (the incumbent), and accepts the rewrite as the new incumbent only if the grade improves. Separating public scores from the hidden selection score blocks the inner-loop agent from directly optimizing the selection criterion, and giving every agent the same budget makes gains come from a better algorithm rather than extra compute.
• Implementation: The starting agent AIDE0 is a tree-search agent made by removing the ML-specific parts of AIDE, with draft, debug and improve operators and greedy selection of the highest-scoring solution. The 8-day recursive self-improvement run produced a 100-node trajectory of the initial agent and 99 rewrite proposals, and during it the outer-loop agent, Weco's production agent AIDEhuman, ran on Claude Opus 4.7 while every inner-loop agent was evaluated with Gemini 3 Flash. External evaluations used Gemini 3 Flash for ALE-Bench (AtCoder combinatorial optimization), MLE-Bench (Kaggle ML engineering) and KernelBench (GPU kernel optimization), GPT-5.4 for FML-Bench (research codebase tasks) and Gemini 3.1 Pro for WeatherBench 2 (physics-based weather forecasting), and the paper text gives no code repository for AIDE².
• Results: In the 8-day run, seven accepted rewrites raised the grade from 0.703 to 0.778, while AIDEhuman scored 0.749 on the same grade. On external benchmarks separated from selection, the final agent AIDE85 versus AIDEhuman scored 1790 vs 1511 on ALE-Bench, 0.722 vs 0.708 on MLE-Bench, 19.9% vs 19.6% on FML-Bench and 0.793 vs 0.404 on the out-of-distribution WeatherBench 2. On 38 KernelBench pairs, a separate task family, the reward hacking rate was 55% for AIDE0, 39% for AIDE47 and 32% for AIDE85, against 39% for AIDEhuman.
• Limits: Noise compounds across both loops, and if it is high enough one falsely accepted rewrite can derail the later outer-loop search. In the ignition test, which checks whether a discovered agent drives the loop better than the agent that produced it, the arm with AIDE47 as the outer-loop agent ran 3 seeds of 50 steps and ended at a seed-average grade of 0.780 against 0.782 for the AIDEhuman arm, and the authors call the result inconclusive. The discovered agents are complex and hard to interpret, and it is unclear which components drive their performance.

Paper links

External research summaries. These are not HDATF publications or measured product results.