AI4AI Survey: From Long-Horizon Agents to Recursive Self-Improvement—Definitions, Reliable Horizons, and Open Problems

Can AI improve AI? AI agents can now run experiments, modify code and training pipelines, and iteratively improve AI artifacts—bringing a long-standing idea closer to an empirical research question. Yet the rapidly expanding literature remains fragmented across long-horizon agents, AI for AI (AI4AI), self-improvement, and recursive self-improvement, making it difficult to tell how much progress has actually been made. This survey synthesizes evidence from hundreds of studies to answer this question. We organize the emerging AI4AI landscape around a simple question: how far can an AI system reliably carry an improvement process from idea to verified result? Across model design, agent harnesses, benchmarks, automated research, and self-modifying systems, we examine what current systems can do, how progress should be evaluated, and where claims of self-improvement remain unsupported. A striking pattern emerges in the information flow within the taxonomy– benchmark-model-harness structure of this survey. AI systems increasingly excel at the work of improvement—planning, coding, experimentation, optimization, and repair—but humans still largely determine the goals, evaluation criteria, and what ultimately counts as progress. Moreover, strong performance on individual components rarely translates into reliable end-to-end AI improvement. We call this composition gap. Today’s systems can already produce impressive improvements under bounded conditions, but evidence for reliable research judgment, causal experimentation, persistent gains, and compounding improvement remains limited. By separating demonstrated capability from extrapolated autonomy, this survey maps what AI4AI can do today—and what must change before AI can reliably improve AI itself. The eve ends the moment a system, for the first time, reliably strengthens its successors without humans specifying the goals or evaluation criteria. We hope this taxonomy and survey will help that moment arrive a little sooner.