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[2602.13594] Hippocampus: An Efficient and Scalable Memory Module for Agentic AI
Summary
The paper introduces Hippocampus, a scalable memory module designed for agentic AI, enhancing retrieval speed and storage efficiency compared to existing systems.
Why It Matters
As AI systems increasingly require persistent memory for user-specific histories, Hippocampus addresses the limitations of current memory solutions, offering significant improvements in retrieval latency and scalability. This advancement is crucial for developing more effective agentic AI applications.
Key Takeaways
- Hippocampus utilizes compact binary signatures for efficient semantic search.
- The Dynamic Wavelet Matrix (DWM) enables ultra-fast search in compressed memory.
- Retrieval latency is reduced by up to 31 times compared to existing systems.
- The memory module scales linearly with size, making it suitable for long-term AI deployments.
- Maintains accuracy on key benchmarks while significantly reducing token footprint.
Computer Science > Artificial Intelligence arXiv:2602.13594 (cs) [Submitted on 14 Feb 2026] Title:Hippocampus: An Efficient and Scalable Memory Module for Agentic AI Authors:Yi Li, Lianjie Cao, Faraz Ahmed, Puneet Sharma, Bingzhe Li View a PDF of the paper titled Hippocampus: An Efficient and Scalable Memory Module for Agentic AI, by Yi Li and 4 other authors View PDF HTML (experimental) Abstract:Agentic AI require persistent memory to store user-specific histories beyond the limited context window of LLMs. Existing memory systems use dense vector databases or knowledge-graph traversal (or hybrid), incurring high retrieval latency and poor storage scalability. We introduce Hippocampus, an agentic memory management system that uses compact binary signatures for semantic search and lossless token-ID streams for exact content reconstruction. Its core is a Dynamic Wavelet Matrix (DWM) that compresses and co-indexes both streams to support ultra-fast search in the compressed domain, thus avoiding costly dense-vector or graph computations. This design scales linearly with memory size, making it suitable for long-horizon agentic deployments. Empirically, our evaluation shows that Hippocampus reduces end-to-end retrieval latency by up to 31$\times$ and cuts per-query token footprint by up to 14$\times$, while maintaining accuracy on both LoCoMo and LongMemEval benchmarks. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.13594 [cs.AI] (or arXiv:2602.13594v1 [cs.AI] f...
