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

• URL: http://arxiv.org/abs/2605.22142v1
• Date: Thu, 21 May 2026 08:14:36 GMT
• Title: Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
• Abstract summary: We study the transfer process in a temporal knowledge-graph memory setting.<n>For each observed triple, the agent chooses whether to keep or drop it before long-term insertion.<n>On the RoomKG benchmark at long-term memory capacity 128, learned transfer decisions outperform symbolic and neural baselines.
• Score: 7.817226329977399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. To handle variable-sized short-term buffers, we use a per-item Q-learning design with shared parameters and a practical temporal-difference update over matched items across consecutive steps. On the RoomKG benchmark at long-term memory capacity 128, learned transfer decisions outperform symbolic and neural baselines, including symbolic baselines with temporal annotations and history-based LSTM/Transformer baselines. Across transfer-policy ablations, a lightweight local short-term-only variant performs best, and step-level behavior shows that the policy keeps navigation- and query-relevant facts while discarding lower-value candidate facts, supporting explicit and interpretable memory decisions under memory constraints.

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