arXiv:2604.15774v2 Announce Type: replace Abstract: Equipping Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks. Specifically, contaminated or biased memory accumulation can trigger abnormal agent behaviors. Existing evaluation methods have not yet established a standardized framework for measuring memory misevolution. This phenomenon refers to the gradual behavioral drift resulting from repeated exposure to misleading information. To address this gap, we introduce MemEvoBench, the first benchmark evaluating
The proliferation of LLM agents in various applications necessitates robust safety and reliability measures, making this benchmarking effort timely as organizations deploy these systems.
This work directly addresses a critical safety concern—memory misevolution—that could undermine the trustworthiness and effectiveness of AI agents, which are increasingly central to enterprise operations.
The introduction of MemEvoBench provides the first standardized framework for measuring cumulative behavioral drift in LLM agents, enhancing the ability to develop safer and more robust AI systems.
- · AI safety researchers
- · LLM developers
- · Enterprises deploying AI agents
- · Unsafe AI agent deployments
- · Systems lacking rigorous evaluation
- · End-users exposed to biased AI
The benchmark facilitates the development of mitigation strategies against memory-induced behavioral drift in LLM agents.
Improved safety standards for LLMs could accelerate their adoption in high-stakes environments, potentially collapsing more white-collar workflows.
Enhanced reliability and trustworthiness of AI agents may lead to greater societal acceptance and integration of advanced AI systems, but also concentrated power among the most reliable AI systems.
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Read at arXiv cs.CL