给AI加记忆，可能越加越笨
给AI配记忆系统，本意是让它记住关键信息、别在长任务里丢三落四。但大规模评测发现：很多精心设计的记忆系统，反而比直接翻原始聊天记录更差——你给它加了记忆，它表现还退步了。研究者把这笔亏空叫「记忆税」:记忆系统在整理信息时，会顺手丢掉或模糊掉一些关键证据，这些损失往往被「记忆带来的好处」掩盖了。它不是你明天能用上的东西，但提醒你一件事：给AI加功能，未必是净赚。
📄 原文摘要(英文)
Memory is increasingly used in long-horizon agents, yet the net value of building memory over direct retrieval from raw history remains largely unclear. Through a large-scale evaluation across multiple benchmarks and model backbones, we find many dedicated memory systems underperform naive RAG in many cases, indicating a negative net value. Taking RAG as a reference, we dissect the net value of memory into the gains and losses over direct retrieval, termed memory benefit and memory tax. These gains and losses arise because memory process changes how the raw information is organized and accessed. We view such changes as the double-edged deviations from the raw history. They are originally designed and introduced to benefit the task, for example by reorganizing facts or distilling preferences, but some may also remove or obscure evidence, giving rise to memory tax. Since memory benefit reflects the expected gains of its design, we then focus more on the overlooked memory tax, study how it arises from the system and evaluation side, and generalize the concept to more application scenarios of memory. We hope the notion of memory tax can provide a new lens and shift more attention from standalone performance to the net value of memory, informing more reliable memory designs and evaluations. We will open-source the code and results to the community. Project Page: https://hexuan21.github.io/MemoryTax/