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  "text": "🧠 Can agent memory scale without losing reasoning?\n🔥 We’re excited to share our latest work, SimpleMem, a principled memory framework for LLM agents built around semantic lossless compression.\n\n📉 30× fewer inference tokens\n📈 +26.4% avg F1 (vs Mem0)\n⚡ 50.2% faster retrieval (vs Mem0)\n\nInstead of storing raw interaction history 🗂️ or relying on costly iterative reasoning loops 🔁, SimpleMem treats memory as a structured, evolving representation whose primary objective is 🎯 maximizing information density per token.\n\n📄 Paper: https://t.co/4ZmiXMgzLA\n🔗 Code: https://t.co/hhdgvViDwh\n📦 Website：https://t.co/B3AhdoTBtS\n\nNice work @JiaqiLiu835914, Yaofeng Su, @richardxp888, @lillianwei423, and great collab. w/ @cihangxie, Zeyu Zheng, @dingmyu",
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