AuthMem-Bench: New Benchmark Exposes Authority Collapse in LLM Agent Memory
A recent paper available on arXiv (2608.01679) presents AuthMem-Bench, a tool aimed at assessing 'authority collapse' in self-evolving LLM agents. This issue arises when the consolidation of memory retains a claim while eliminating the source constraints that dictate its authorized application, leading to stored memories suggesting a level of authority beyond what the source allows. The benchmark maintains a consistent focal claim and downstream task, altering only the source authority to evaluate write-time collapse, downstream authorization errors, and automatic authority preservation. The study examines seven consolidators from popular agent-memory systems alongside seven LLM backbones. It emphasizes a significant challenge in AI memory systems: the implicit authorization boundary that affects how stored information can be utilized later. The results highlight the necessity for effective memory management in AI agents to avert unauthorized information usage.
Key facts
- AuthMem-Bench is a controlled paired benchmark introduced in arXiv paper 2608.01679.
- Authority collapse is identified as a phenomenon where consolidation preserves a claim but erases source constraints.
- The benchmark evaluates write-time collapse, downstream authorization errors, and automatic authority preservation.
- Seven consolidators based on widely used agent-memory systems are tested.
- Seven LLM backbones are used in the evaluation.
- The research focuses on self-evolving LLM agents that consolidate heterogeneous interaction histories.
- The paper is announced as a new arXiv submission with type 'new'.
- The study addresses the implicit authorization boundary in memory consolidation.
Entities
Institutions
- arXiv