NVIDIA 论文：智能体频繁压缩历史可在短上下文下几乎不损精度
NVIDIA 一篇新论文发现，AI 智能体通过频繁压缩历史保持短上下文时，精度几乎不下降。在 6 个长周期基准上，每个模型 22 种压缩设置中最多 5 种比完整历史低超过 2 分；每步动作后都压缩时，Claude Opus 4.5 在 SWE-bench Verified 上为 70.2%，完整历史为 70.0%。
Rohan Paul· @rohanpaul_ai · X·2026-11-05 02:20· 37 分钟前AI 评分63
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NVIDIA 论文发现，AI 智能体通过频繁压缩历史保持短上下文时精度几乎不降。服务 256K 上下文窗口比 16K 的每 token 生成成本高 3.6 倍到 7 倍。
正文 · 原文
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 versus full history. Compacting after every action kept Claude Opus 4.5 at 70.2% on SWE-bench Verified, versus 70.0% with full history.
Before paying for long context, give agents a structured handoff note and file search tools, then check if short contexts match your accuracy.
来源：Rohan Paul · x.com
来源：RadarAI · aihot.news