arXiv:2605.26667v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empirical work has been done to understand the specific failure modes and design choices that these systems present. Existing benchmarks report aggregate question-answering accuracy and treat memory systems as black boxes, making it impossible to attribute an incorrect answer to a particular failure mode of the system. We introduce MemFail, a diagnostic benchmark that isolates the failure modes of mode
As LLMs become increasingly central to complex applications, robust and reliable memory systems are critical for their effective deployment and trustworthiness.
Understanding and addressing the specific failure modes of LLM memory systems is crucial for developing dependable AI agents and preventing cascading system failures.
The introduction of diagnostic benchmarks like MemFail allows for a more granular understanding of LLM memory system vulnerabilities, moving beyond black-box evaluations.
- · AI developers
- · LLM researchers
- · Enterprises deploying AI agents
- · Underperforming memory system providers
- · Applications relying on fragile LLM memory
Improved reliability and consistency of LLM agents in long-horizon interactions.
Accelerated development of more robust AI agent architectures and memory management techniques.
Enhanced trust and broader adoption of AI agents in critical professional and industrial workflows.
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Read at arXiv cs.LG