MemoryLOD: Source-Grounded Navigation over Multiresolution Memory

Long-horizon language agents must retrieve useful details from histories that grow beyond what they can read at once. Compact summaries can lose information needed by later questions, while reading entire histories consumes context and computation. We present MemoryLOD, a memory framework that organizes source files into a navigable graph at multiple levels of detail. Inspired by level of detail in computer graphics, the graph combines coarse views for orientation with fine-grained access to source content. An agent explores a bounded view of this graph, follows local and cross-resolution links, and opens sources to gather evidence for its question. The graph is constructed independently of the query, while navigation and stopping are guided by the evidence the question requires. Current source files remain authoritative: derived representations guide retrieval, and only reopened, verified source content is used as answer evidence. We evaluate MemoryLOD on BEAM, LongMemEval-V2, and UltraDomain, covering conversational memory, multimodal interaction histories, and document question answering. The evaluation examines answer quality and evidence provenance, with diagnostic analyses that distinguish failures to find relevant information from failures to use it.