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  "text": "This is one of the most interesting papers on self-improving agents for this year.\n\n(bookmark this one)\n\nMost self-improving AI systems hit the same wall: the mechanism that generates improvements is fixed and can't improve itself.\n\nThis new work from Meta and collaborators breaks through this limitation.\n\nThey introduce Hyperagents, self-referential agents where the self-improvement process itself is editable.\n\nThe DGM-Hyperagent combines a task agent and a meta agent into a single modifiable program, enabling metacognitive self-modification.\n\nIt autonomously discovers innovations like persistent memory and performance tracking, and these meta-improvements transfer across domains and compound across runs.\n\nWhy does it matter?\n\n- On paper review, DGM-H improved from 0.0 to 0.710 test accuracy.\n\n- On robotics reward design, it went from 0.060 to 0.372.\n\n- Transfer hyperagents achieved 0.630 on Olympiad-level math grading in a domain they were never trained on.\n\nThis is a step toward AI systems that don't just find better solutions but continuously improve how they search for improvements.\n\nPaper: https://t.co/Q0f7zWhNMD\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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