mlsanityData to 7 Oct 2026

The Memory Curse: How Expanded Recall Erodes Cooperative Intent in LLM Agents

arXiv 2605.08060 ↗First posted 8 May 2026cs.CL cs.AI cs.GTcited by 10

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

Context window expansion is often treated as a straightforward capability upgrade for LLMs, but we find it systematically fails in multi-agent social dilemmas. Across 7 LLMs and 4 games over 500 rounds, expanding accessible history degrades cooperation in 18 of 28 model--game settings, a pattern we term the memory curse. We isolate the underlying mechanism through three analyses. First, lexical analysis of 378,000 reasoning traces associates this breakdown with eroding forward-looking intent rather than rising paranoia. We validate this using targeted fine-tuning as a cognitive probe: a LoRA adapter trained exclusively on forward-looking traces mitigates the decay and transfers zero-shot to distinct games. Second, memory sanitization holds prompt length fixed while replacing visible history with synthetic cooperative records, which restores cooperation substantially, proving the trigger is memory content, not length alone. Finally, ablating explicit Chain-of-Thought reasoning often reduces the collapse, showing that deliberation paradoxically amplifies the memory curse. Together, these results recast memory as an active determinant of multi-agent behavior: longer recall can either destabilize or support cooperation depending on the reasoning patterns it elicits.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 15 of its 21 references that are in the map. First posted on arXiv 8 May 2026.

Builds on

15 of 21 references.

References · 15

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  5. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  6. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  7. 2025Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRscited by 373
  8. 2014Memory Networkscited by 354
  9. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 236
  10. 2023Playing repeated games with Large Language Modelscited by 213
  11. 2024Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agentscited by 120
  12. 2025MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agentscited by 82
3 more
  1. 2025Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Gamescited by 20
  2. 2026CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmascited by 14
  3. 2025Spontaneous Giving and Calculated Greed in Language Modelscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 10.

10 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Sparks of Cooperative Reasoning: LLMs as Strategic Hanabi Agents
  2. 2026EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium
  3. 2026The Epistemics of Agent Memory: Measuring, and Governing, the Consolidation Decision in Long-Horizon LLM Agents
  4. 2026Readable Minds: Emergent Theory-of-Mind-Like Behavior in LLM Poker Agents
  5. 2026LLMs Are Not Good Strategists, Yet Memory-Enhanced Agency Boosts Reasoning
  6. 2026How memory can affect collective and cooperative behaviors in an LLM-Based Social Particle Swarm
  7. 2026Governed Collaborative Memory as Artificial Selection in LLM-Based Multi-Agent Systems
  8. 2025How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior