arXiv:2606.23195v2 Announce Type: replace Abstract: 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 st
Ongoing advancements in LLM agent development and memory systems necessitate deeper understanding of their biases, particularly as these systems move towards autonomous operation.
This research highlights a critical vulnerability in autonomous AI systems, where evaluation biases can propagate through memory, impacting long-term behavior and decision-making for those relying on these agents.
Understanding 'Memory Contagion' introduces a new facet to AI safety and alignment, requiring developers to address not just initial biases but also their cross-temporal propagation within agent memory systems.
- · AI safety researchers
- · Developers of robust AI evaluation frameworks
- · Organizations implementing bias mitigation strategies
- · Developers of unmonitored AI agent systems
- · Users relying on un-scrutinized agent outputs
- · Organisations with biased training data or evaluators
LLM agents trained or guided by biased evaluators will exhibit increasingly biased behavior over time.
This could lead to a loss of trust in autonomous AI systems, particularly in sensitive applications.
New regulatory frameworks may emerge to mandate bias auditing and mitigation for AI memory systems and continuous learning agents.
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Read at arXiv cs.LG