# 宾大研究：AI智能体压缩后遗忘规则

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-08-18 03:58
- AIHOT 分数：47
- AIHOT 链接：https://aihot.virxact.com/items/cmsxom4me067nroz0o4k65gfl
- 原文链接：https://x.com/rohanpaul_ai/status/2089441634597450156

## AI 摘要

宾夕法尼亚大学新论文发现，AI智能体在长会话压缩后常遗忘用户设定的规则，却仍记得任务本身。在3种长上下文设置中，现有压缩器平均仅保留17%的会话规则。简单修复方案是使用独立的9B参数提取器在压缩后恢复规则，使保留率提升至90%以上。

## 正文

A scary finding from new Pennsylvania Uni paper.

AI agents can go off-script in a simple way: they can forget your rules while still remembering the job.

If a rule must survive the whole session, normal chat history may be the wrong place to store it.

Say you tell an agent, “Never send an email without asking me first.”

Long sessions get compressed into a summary so the model can keep going.

This paper finds that the summary often keeps the task but drops the rule.

Across 3 long-context settings, current compactors kept only 17% of these session rules on average.

So the agent may remember that it is managing your inbox, yet forget that it needs approval before sending anything.

That is the scary part.

The fix was simple: keep standing rules in a separate registry instead of trusting the context summary to remember them.

A small 9B extractor tracked the rules as users stated them and restored them after compaction, pushing retention above 90% across all 3 settings.

If you build agents that run for hours, use tools, or take actions, treat user constraints like persistent state, not disposable chat history.

– arxiv. org/abs/2608.11242

Title: "Lost in Compaction: Evaluating Side-Constraint Loss under Context Compaction"