New NVIDIA paper finds that AI agents barely lose accuracy when they keep contexts short…
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New NVIDIA paper finds that AI agents barely lose accuracy when they keep contexts short by compacting their history often. Each new token costs more to generate as the context grows. Serving open models at a 256K context window instead of 16K cost 3.6x to 7x more per generated token. They had agents stop at a set limit, write a short handoff note, and restart with only the task and that note. For long documents, the agent searched files with Bash tools instead of reading everything at once. On 6 long-horizon benchmarks, at most 5 of 22 compaction settings per model lost more than 2 points
Posted by Rohan Paul (160.8k followers) 1 days ago · 65 likes · 5.7k views · view the original post on X. Kept by the Vibe Coding Radar as Prompts & workflows.
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