arXiv:2605.24941v1 Announce Type: cross Abstract: Modern LLM agents combine long-term memory for personalization with tool-calling interfaces for taking actions in the world -- a combination underpinning contemporary production systems. We study a previously unexamined failure of this combination: when personality-driven biases stored in memory (cost-consciousness, impatience, risk tolerance, etc.) silently affect tool calls in contexts where they are not applicable. We call this memory-induced tool-drift and operationalize it through MEMDRIFT, a benchmark of 105 scenarios spanning five bias d
The increasing deployment of LLM agents in production systems, coupled with their growing sophistication in memory and tool-calling, makes identifying nuanced failure modes like 'memory-induced tool-drift' critical for reliable operation.
This research highlights a significant vulnerability in autonomous AI agents, where personalization features can inadvertently undermine operational integrity by biasing tool use in unintended ways, impacting trust and effectiveness.
Understanding this 'tool-drift' mechanism necessitates new approaches in designing, testing, and deploying LLM agents, emphasizing robust bias mitigation and contextual awareness for memory integration.
- · AI safety researchers
- · Developers of robust LLM agent frameworks
- · Auditing and validation service providers for AI systems
- · LLM agent deployments without adequate testing for bias
- · Users relying on un-audited autonomous AI systems
- · Organizations with mission-critical systems vulnerable to subtle agent biases
Enterprise adoption of AI agents will slow slightly until robust mitigation strategies for tool-drift are validated.
New regulatory guidelines may emerge, mandating transparency and testing for memory-induced biases in autonomous AI systems.
The development of 'explainable AI' (XAI) for agent decision-making will accelerate, focusing on tracing biases from long-term memory to tool execution.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG