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  "text": "New research from NVIDIA.\n\nLong-running agentic tasks like deep research require multi-hop reasoning over many documents.\n\nOne of the biggest challenges with agents is that context grows rapidly, and KV cache memory usage becomes the bottleneck.\n\nAs agents take on longer tasks, memory management can't rely on static heuristics. Letting the model manage its own context is both more effective and more adaptive.\n\nExisting cache compression techniques use fixed heuristics to decide what to keep. But in agentic reasoning, a token that seems unimportant early on may become critical ten turns later.\n\nThis new NVIDIA research paper introduces SideQuest, a framework where the reasoning model itself manages its own KV cache. The model reasons about which tokens are still useful and clears the rest, essentially performing its own memory garbage collection.\n\nThis management runs as an auxiliary task in parallel with the main reasoning thread, so the management tokens never pollute the primary context. That's important.\n\nTrained with just 215 samples, SideQuest reduces peak token usage by up to 65% on agentic tasks with minimal accuracy loss, outperforming all heuristic-based compression techniques.\n\nPaper: https://t.co/n3P6UjtLJ7\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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