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  "text": "New research on improving self-reflection in language agents.\n\nA core problem with agent self-reflection is that models tend to generate repetitive reflections that add noise instead of signal, hurting overall reasoning performance.\n\nIt introduces ParamMem, a parametric memory module that encodes cross-sample reflection patterns directly into model parameters, then uses temperature-controlled sampling to generate diverse reflections at inference time.\n\nParamMem shows consistent improvements over SOTA baselines across code generation, mathematical reasoning, and multi-hop QA. It also enables weak-to-strong transfer and self-improvement without needing a stronger external model, making it a practical upgrade for agentic pipelines.\n\nPaper: https://t.co/16Yp56j8Jm\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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