elvis@omarsar0 · X研究发现，长历史记录会在大语言模型（LLM）代理中引发“记忆诅咒”，导致其过度遵循历史、规避风险，从而削弱合作能力。该结论基于7个LLM和4个社会困境游戏的实验，在28个模型-游戏组合中，有18个因历史扩展而合作退化。机制分析表明，长历史侵蚀了模型的前瞻性意图，使其更关注过去的冲突而非未来收益。通过仅在前瞻性轨迹上训练的LoRA适配器可缓解此问题，且能零样本迁移至新游戏。实验证明，触发因素是历史内容而非长度，而消除显式思维链通常能减轻合作崩溃。
// The Memory Curse in LLM Agents //
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Long histories apparently degrades agents as they become increasingly history-following and risk-minimizing.
Across 7 LLMs and 4 social dilemma games over 500 rounds， expanding accessible history degraded cooperation in 18 of 28 model-game combinations.
They call it the memory curse.
Lexical analysis of 378，000 reasoning traces shows the mechanism： it's not that agents become paranoid， it's that forward-looking intent erodes. Long histories pull the model into reasoning about past slights instead of future payoffs.
A LoRA adapter trained only on forward-looking traces mitigates the decay and transfers zero-shot to new games.
Memory sanitization， keeping prompt length fixed but swapping in synthetic cooperative records， restores cooperation， proving the trigger is content， not length. And ablating explicit Chain-of-Thought often reduces the collapse， meaning deliberation actively amplifies the curse.
Paper： https：//arxiv.org/abs/2605.08060
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