Scientific discovery does not follow a single fixed methodology: research methods vary across domains and evolve through experimentation as investigations unfold. Although LLM-guided evolutionary search has advanced automated discovery, existing frameworks typically rely on fixed search procedures or restrict adaptation to predefined components, limiting their ability to address diverse research problems and adapt to challenges that emerge at runtime. We introduce Organon, a recursively self-improving agent for scientific discovery. During execution, a single persistent agent analyzes its accumulated experience and autonomously revises its own search strategies, population management, diagnostic tools, memory, and worker coordination. These revisions take effect at runtime, allowing the agent to continuously adapt its approach as research progresses. Importantly, the agent can also diagnose and correct flaws in its own adaptation procedures, enabling recursive improvement of the mechanisms that guide its future self-improvement. We evaluate Organon across 21 computational discovery tasks spanning algorithm design, GPU kernel optimization, mathematical construction, and systems optimization. On ALE-Bench Lite, Organon achieves the best performance among competing search frameworks on all 10 tasks, improving mean held-out performance by 20.5% over EvoX. On KernelBench, it achieves the best results on three of four workloads, with speedups of up to 1.67x over the strongest competing method. Analysis of the search trajectories further reveals how the agent develops task-specific search strategies, builds new diagnostic capabilities, and corrects or reverses its own earlier revisions. We implement Organon using DeepSeek Harness and Cordis's spatiotemporal composability. Code is available at https://github.com/cheikh025/Organon.

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