Deep-Dream: A Temporal Concept Graph for Agentic Long-Term Memory

An agent's long-term memory grows while the entities it mentions may change substantially. Human recall does not rewrite an earlier observation when later knowledge arrives: it retrieves that observation in the historical context in which it was made. An agent faces the same problem under a finite context window. Copying the whole history is too expensive, while summarizing it before the question is known can discard the qualifiers that make an answer correct. Deep-Dream is designed around these two requirements. It leaves source documents and spans unchanged, and maintains concepts, relations, observations, and versions as a linked access layer. At query time, the agent follows these links across documents, opens the source spans that are needed, and can continue to a multi-hop trail when the first evidence is insufficient. During ingestion, uncertain mentions are kept as candidate entity families instead of being forced into one identity; later observations and relation neighborhoods can align or redirect those candidates without deleting the original evidence. We evaluate this design on MemoryAgentBench, LongMemEval, LoCoMo, and MEME. Source-grounded reading reaches 71.0% on MemoryAgentBench, close to the 78.7% same-model full-context reference, and Deep-Dream reaches 95.19% on LoCoMo. Delayed entity alignment reduces ingest calls by 46–65% while retaining comparable sampled quality. The results show how a revisable access layer and agent-controlled source reading support long-term memory; MEME also shows that deletion requires an explicit suppression mechanism.