Self-improving AI systems can revise the designs that govern their own behavior. Yet existing approaches center improvement on selected parent agents and their evolutionary experience, leaving the bro...
The paper introduces AIDE^2, an AI research agent that recursively improves its own code by proposing, benchmarking, and selecting modifications. Over an eight‑day autonomous run, it achieved seven successive improvements—including new search policies and memory mechanisms—that transferred to four held‑out benchmarks in machine learning, algorithm engineering, and weather forecasting. The agent’s best version matched or outperformed a top human‑engineered production research agent and also reduced reward‑hacking rates, despite never optimizing for that metric.
arXiv:2610.36660v1 Announce Type: new Abstract: Self-improving AI systems can revise the designs that govern their own behavior. Yet existing approaches center improvement on selected parent agents a...
arXiv:2609.35561v2 Announce Type: replace Abstract: Recursive self-improvement (RSI) seeks to enable AI systems to participate in improving their own capabilities. A concrete pathway is autonomous mo...
Organon is a recursively self‑improving agent designed for scientific discovery that autonomously revises its search strategies, population management, diagnostics, memory, and worker coordination during execution. By diagnosing and correcting its own adaptation procedures, the agent continuously refines the mechanisms guiding its future self‑improvement. Across 21 computational discovery tasks—including algorithm design, GPU kernel optimization, and systems optimization—Organon outperforms competing frameworks, achieving a 20.5% mean performance gain on ALE‑Bench Lite and up to 1.67× speedups on KernelBench.
arXiv:2606. 09663v1 Announce Type: new Abstract: Recursive self-design refers to AI-assisted modification of the mechanisms by which an AI system is built, evaluated, and improved.
RSI-Anything is a domain-general framework that leverages predictive world models to guide action and learning-data selection for recursive self-improvement. It uses observed feedback to improve policies or workflows and adapts world models conditionally, while component and compatibility checks control the retention of updated policy-model pairs. Across multiple domains—including code repair, GUI tasks, laboratory planning, reaction optimization, and immune‑science applications—RSI-Anything shows substantial performance gains, such as higher success rates, reduced actions, doubled planning scores, and superior yield compared to baseline methods.
RSIGym is a new agent-native research environment built on Everything as a Service (EaaS) that exposes training, inference, rollout, evaluation, and sandbox execution through reusable services. It supports shared budget and permission controls for Data, Harness, and Joint improvement tracks, allowing agents to investigate individual interventions and jointly optimize data, training settings, and execution harnesses within the same environment. Using RSIGym, the Opus 5 model achieved the highest RSI-Index of 0.4809 across five benchmarks, improving performance on software engineering, terminal interaction, mathematics, scientific reasoning, and skill-based tasks.
Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generation. However, open-end...
arXiv:2608.31076v1 Announce Type: cross Abstract: Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experi...
arXiv:2610.14224v1 Announce Type: new Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a pr...
arXiv:2609.17523v1 Announce Type: new Abstract: We introduce and release ScienceBuddy, an interactive scientific research workspace that brings continually improving scientific agents into researcher...