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  "text": "// Harnessing Agentic Evolution //\n\nPay attention to this one if you run iterative agentic search loops.\n\n(bookmark it)\n\nAEvo splits the self-improvement loop into two jobs:\n\n> One proposes the next candidate.\n\n> The other watches what worked, what failed, and edits the procedure that proposes future candidates.\n\nPast runs (candidates, feedback, traces, failures) become memory the meta-agent reads from.\n\nAchieves 26% relative gain over the strongest evolution baseline on agentic and reasoning benchmarks. SOTA on three open-ended optimization tasks under the same iteration budget.\n\nIf you are accumulating agentic search logs you never use, this is how to feed them back into the search procedure itself.\n\nPaper: https://t.co/eWFO4rI4iA\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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