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  "text": "// The Memory Curse in LLM Agents //\n\n(bookmark it)\n\nLong histories apparently degrades agents as they become increasingly history-following and risk-minimizing.\n\nAcross 7 LLMs and 4 social dilemma games over 500 rounds, expanding accessible history degraded cooperation in 18 of 28 model–game combinations.\n\nThey call it the memory curse.\n\nLexical 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.\n\nA LoRA adapter trained only on forward-looking traces mitigates the decay and transfers zero-shot to new games.\n\nMemory 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.\n\nPaper: https://t.co/aHLDZ9kmlJ\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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