Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability

ArXi:2605.22142v1 Announce Type: cross Reinforcement learning under partial observability requires deciding what information to retain, yet most memory-based approaches do not explicitly model short-term-to-long-term transfer of symbolic observations. We study this transfer process in a temporal knowledge-graph memory setting and cast it as a neuro-symbolic value-based decision problem: for each observed triple, the agent chooses whether to keep or drop it before long-term insertion.