这篇论文发现用有偏评估者训练智能体，偏差会像病毒一样通过记忆传染给后来者。旧模型DeepSeek V4-Chat中招，Claude和V4-Pro没事，权威偏见传不出去。
LLM Agent记忆系统在持续整合中会退化，但现有研究假设记忆来自无偏体验。本研究提出Memory Contagion现象，即有偏评估者导致的偏差会通过记忆跨时间传播。实验显示长度偏好偏差在旧模型DeepSeek V4-Chat上传播（Gamma_A=13.18），而新模型V4-Pro和Claude免疫。权威偏差在全部15个多种子实验中未传播（Gamma_A=0.00）。污染率低至p=0.2时仍能检测到长度偏差传播，未发现安全阈值。
Memory Contagion: Cross-Temporal Propagation of Evaluator Bias via Agent Memory
Large Language Model (LLM) agents increasingly rely on memory systems to maintain long-term coherence. Recent work shows that agent memories degrade during continuous consolidation. However, existing research assumes memories are derived from unbiased experiences. In this work, we identify and formalize a novel phenomenon: Memory Contagion -- the cross-temporal propagation of evaluator bias through agent memory. We show that when agents are trained or guided by biased evaluators, their experiences become biased; when these trajectories are stored and consolidated into memory, the bias propagates to future agents retrieving from the same memory store, even when consolidation is perfect (oracle). Across two bias types (length preference, authority bias) and four experimental phases, we demonstrate: (1) Memory Contagion occurs for length bias even with perfect consolidation on older models (Gamma_A = 13.18, DeepSeek V4-Chat), while newer models (V4-Pro, Claude) are immune, proving both that biased input is a sufficient cause and that contagion is model-generation-dependent; (2) authority bias fails to propagate in all 15 controlled multi-seed experiments (Gamma_A = 0.00), revealing that not all evaluator biases can cross temporal boundaries through current memory architectures; (3) No observed safe threshold: length bias propagation is detected at contamination rates as low as p=0.2. Our findings expose a critical but contingent vulnerability in current agent memory designs and provide formal tools for measuring cross-temporal bias propagation.