The Memory Curse: How Expanded Recall Erodes Cooperation in LLM Agents

Expanding the interaction history available to a large language model (LLM) agent is often treated as a straightforward capability upgrade, but we find that it can reduce cooperation in repeated social dilemmas. Across 7 LLMs and 4 games with interactions evaluated over up to 500 rounds, many model–game settings exhibit lower cooperation under expanded recall, a pattern we term the *memory curse*. Complementary diagnostics and interventions connect this effect to action persistence, memory content, and expressed reasoning patterns. Lexical analysis associates longer histories with less forward-looking language, supported by a blinded contextual evaluation of 2,016 rationales. An action-balanced, rationale-only low-rank adaptation (LoRA) model trained on 1,000 Public Goods traces shows a substantially smaller short-to-long-memory cooperation decline in the training game and positive average zero-shot changes in other, untrained games. Compact recent, summary, and retrieval views yield higher average cooperation than the full 80-round record; token-matched compact raw histories show similar aggregate gains to the summary and retrieval views. Removing explicit Chain-of-Thought attenuates the mean decline in most settings exhibiting the memory curse with explicit reasoning, with stability responses varying across settings. Together, these results establish accessible history as a central behavioral design choice for sustaining cooperation.