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Memory Undercutting Model Performance?

AI 摘要

一位用户对 Fable 的性能表示失望,认为这归因于 Anthropic 糟糕的内存管理。审计发现,Claude 的内存是一团糟,充满了规则累积、误解和夸大其词,旧指令与更正并存,狭隘的反馈变成了普遍规则。用户希望 Anthropic 能改进持久内存的维护,并指出虽然可以禁用自动内存,但某些方面仍有使用需求。这引发了对 Codex 如何处理内存的不同方式的好奇。

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2026年9月7日 01:41
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首次发现2026年9月7日 12:00时区UTC · UTC+0
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I've been pretty disappointed with Fable's performance on my project recently. After looking into Claude's memory, Anthropic seems to neglect memory cleanup and management. If you've used claude on a project for a long period of time, I'd suggest having Codex help you audit it. Here's a summary of what I found today:

Recently, I have used Fable a lot to help me with complex prompt engineering tasks. Honestly, it is quite poor at it. This made me wonder if it is also poor at writing to its own memory and succumbs to the same issues. Indeed, it is a mess of rule accretion, misunderstandings, and overstatements. Old instructions remained alongside corrections, narrow feedback was turned into blanket rules, and Claude saved its own explanations as if they were decisions I had approved.

I can’t say how much of the performance decline this explains, but my Astra audit found many contradictions and quite a mess. I hope Anthropic would dedicate a little more attention to how persistent memory needs ongoing maintenance. It hasn’t been doing that reliably on my project. Yes, you can disable auto-memory, but there are some aspects of it I would like to use. Makes me curious if Codex handles memory differently.

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