How AI Agents Learn from Failure: Inside the CodeClash Auto-Research Loop
agihouse_org · x · 2026-07-31
AGI House shared a demonstration of AI agents self-improving through competition in the CodeClash project. In this setup, agents write solutions, compete across multiple rounds, and analyze execution logs to learn from their losses.
This build-compete-iterate cycle offers a concrete glimpse into how automated research loops can be structured.
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