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  "text": "// Language Models Need Sleep //\n\nLet your agents \"sleep\", folks.\n\nOn a serious note, this is a fascinating paper on getting the most from long-horizon agents.\n\nHere is the problem with agents today: Attention scales badly with context length, so long-horizon agents keep paying a quadratic tax at inference time.\n\nThis work proposes a sleep-like consolidation step instead. The model periodically does N offline recurrent passes over recent context, writes the result into persistent fast weights in its state-space blocks, then clears the KV cache.\n\nThe effect is that extra compute moves to sleep while wake-time prediction stays low latency. On cellular automata, multi-hop graph retrieval, and a math reasoning task where a plain transformer and SSM-attention hybrids fail, longer sleep durations improve performance, with the biggest gains on examples that need deeper reasoning.\n\nWhy does it matter?\n\nIt points at an alternative to ever-larger KV caches for agents that run for a long time. Consolidate, then forget the raw tokens.\n\nPaper: https://t.co/FfDTbhl98M\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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