Recursive self-improvement of AI research agents

AI agents are beginning to automate research and development across the AI stack, from improving training efficiency to optimizing inference. A natural next step is to improve the research efficiency of the agents themselves. When an AI research agent’s own code is the object of optimization, each accepted rewrite becomes the agent that the next round edits. We refer to this loop as recursive self-improvement. Its significance lies in a long-standing trend, in which increased cumulative spending on R&D yields diminishing returns. Sustained self-improvement offers a way to counter this trend. We present AIDE2, a system that implements this loop for a frontier AI research agent. It proposes changes to its own code, benchmarks modified versions of itself on a suite of AI R&D tasks, and keeps the changes that perform best on hidden evaluations. In an autonomous 8-day run, AIDE2 discovered seven successive improvements, ranging from a new search policy to memory mechanisms that compress and manage the agent’s growing context. These gains generalize to four held-out benchmarks spanning machine learning engineering, heuristic algorithm engineering, and physics-based weather forecasting, the last of which is out of distribution from the selection tasks.

Introduction. AI agents are now used extensively to accelerate and automate parts of research and development (R&D) across the AI stack, from machine learning engineering (Jiang et al., 2025; Toledo et al., 2025; Karpathy, 2026) and GPU kernel engineering (Novikov et al., 2025; Liao et al., 2026) to algorithmic discovery (Liu et al., 2024; Lange et al., 2026) and the design of agent pipelines and harnesses (Zhang et al., 2025; Agrawal et al., 2026b; Hu et al., 2025; Lee et al., 2026). Beyond individual components, agents now run complete research workflows, from generating research ideas (Si et al., 2025b; Baek et al., 2025) to executing experiments and writing papers (Schmidgall et al., 2025; Lu et al., 2026; Jansen et al., 2025). Such systems improve the efficiency of the artifacts they produce, such as training and inference efficiency, yet the efficiency of the research process producing them remains fixed. In conventional R&D, further progress requires increased human effort, making continued improvement increasingly costly as research becomes more difficult (Bloom et al., 2020).

Discussion / Conclusion. AIDE2 demonstrates that recursive self-improvement at the harness layer can produce transferable gains in an AI research agent’s research efficiency. During the recursive self-improvement run, the loop accepted seven rewrites, each under a fixed evaluation budget (section 3.2). Under this fixed evaluation budget, gains in optimization capability on AI R&D tasks translate to gains in research efficiency. The accepted rewrites concentrate on problems that practitioners face when building effective agentic systems: recovering from search plateaus, managing context under fixed budgets, and guarding against untrustworthy wins (section 3.5). On four held-out benchmarks spanning in- and out-ofdistribution tasks, AIDE85 equals or surpasses AIDEhuman (section 3.3), a strong baseline developed through human-driven R&D (appendix B).