做AI智能体开发的团队值得关注——记忆机制是当前瓶颈，这篇论文直接挑战了“记忆越多越好”的假设，看完会重新思考你的记忆策略。
一项新研究揭示，即使经过超万亿美元的投资，LLM智能体的记忆系统仍存在根本性缺陷。研究发现，持续更新的记忆（如压缩后的可复用记忆）不仅无法提升性能，有时甚至比完全没有记忆的表现更差，包括在已解决过的问题上。相比之下，保留原始片段的“情景记忆”更为可靠。这表明当前模型尚无法从经验中学习可复用的抽象知识，而这正是智能体持续改进的关键能力。
🚨Breaking new study: memory in LLM agents still can’t really be trusted, even after over trillion d...
🚨Breaking new study: memory in LLM agents still can’t really be trusted, even after over trillion dollars has gone into the development of the field. Hao Peng @haopeng_uiuc Excited to share our new paper: “Useful Memories Become Faulty When Continuously Updated by LLMs. Can LLM agents keep improving by turning past experience into compact, reusable memories? We find this is much more fragile than it looks. Continuously consolidated memories can perform worse than no memory at all — sometimes even on problems the agent previously solved. Episodic memories that preserve raw episodes are much more reliable. There is still limited evidence that today’s models can learn reusable abstractions from experience over the long term, which I believe is a crucial capability for agents that continuously improve. Paper: arxiv.org/pdf/2605.12978 . Congrats to @dylan_works_ and team! 🔗 View Quoted Tweet 💬 9 🔄 4 ❤️ 26 👀 2951 📊 10 ⚡