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EverMind
@evermind
Long-term agent memory | self-improving | agent harness
加入 November 2025
22 正在關注    4.7K 粉絲
How many memories does an answer actually need? One query needs a single fact. Another needs several pieces of evidence. A fixed top-k gives both the same budget. EverOS uses multi-round retrieval: a small model reads candidate episodes, selects the core evidence, and decides whether to stop or search again for what’s missing. In our LongMemEval-S injection ablation, core-only averaged 1.95 episodes per query: - 77% fewer answer-stage prompt tokens than k=20 - 91.0% accuracy, versus 93.4% at k=20 Retrieve again when evidence is missing. Stop when there’s enough. Let the query determine the memory budget.
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