Papers › RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language Models
RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language Models
Brandon Kynoch, Hugo Latapie, Dwane van der Sluis
Large Language Models (LLMs) have made extraordinary progress in the field of Artificial Intelligence and have demonstrated remarkable capabilities across a large variety of tasks and domains. However, as we venture closer to creating Artificial General Intelligence (AGI) systems, we recognize the need to supplement LLMs with long-term memory to overcome the context window limitation and more importantly, to create a foundation for sustained reasoning, cumulative learning and long-term user interaction. In this paper we propose RecallM, a novel architecture for providing LLMs with an adaptable and updatable long-term memory mechanism. Unlike previous methods, the RecallM architecture is particularly effective at belief updating and maintaining a temporal understanding of the knowledge provided to it. We demonstrate through various experiments the effectiveness of this architecture. Furthermore, through our own temporal understanding and belief updating experiments, we show that RecallM is four times more effective than using a vector database for updating knowledge previously stored in long-term memory. We also demonstrate that RecallM shows competitive performance on general question-answering and in-context learning tasks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | DuoRC | Vector Database (ChromaDB) | Accuracy | 55.71 | #1 of 3 | Archive leaderboard | report |
| Question Answering | DuoRC | Hybrid-RecallM | Accuracy | 52.68 | #2 of 3 | Archive leaderboard | report |
| Question Answering | DuoRC | RecallM | Accuracy | 48.13 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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