Cascade Memory: Dependency-Mediated Belief Revision for LLM Agent Memory

Long-term memory enables LLM agents to retain information across interactions, but new evidence can invalidate beliefs indirectly through their dependencies. These dependent beliefs are not directly contradicted, so retrieval returns them as if still valid. Existing memory systems improve retrieval, structure stored information, or update directly superseded facts, but these operations do not by themselves specify how support for dependent beliefs should be revised. We study this problem, dependency-mediated memory revision, and introduce Cascade Memory, a mechanism that represents beliefs together with the dependencies between them. The design draws on two formal precedents. Assumption-based truth maintenance for tracking support when assumptions change, and belief-base contraction for withdrawing defeated support. Cascade Memory operationalizes these ideas for an LLM-agent memory pipeline: extracted state changes update an explicit dependency structure, a support layer tracks which beliefs remain justified, a consistency check identifies incompatible symbolic states, and an ordered contraction removes beliefs whose support has been lost while preserving provenance. On STALE, the mechanism reaches 66.7% overall accuracy, leading the closest external system by 29.5 pp; on MEME, it achieves 93.52% overall accuracy, including 96.3% on the Cascade task and 96.0% on Deletion. Across four language-model backbones, the complete configuration improves over the corresponding LLM-only configuration in all tested cases. These results suggest that maintaining support alongside stored content is a useful design principle for memory systems that must respond to changes beyond the fact directly mentioned in an update.