CoreMem: Riemannian Retrieval and Fisher-Guided Distillation for Long-Term Memory in Dialogue Agents
Quick Answer
CoreMem introduces a resource-efficient memory architecture for dialogue agents, utilizing Riemannian retrieval and Fisher-guided distillation to enhance long-term memory on 8 GB VRAM devices.
Quick Take
It achieves significant accuracy improvements on LOCOMO and LongMemEval-S benchmarks, with gains of +4.51 pp in Open-domain and +4.17 pp in Temporal reasoning, effectively addressing memory constraints.
Key Points
- CoreMem employs Riemannian retrieval to enhance memory efficiency and reduce hubness issues.
- Fisher-guided discrete token distillation enables hierarchical sentence-to-token compression.
- Achieves strong performance on LOCOMO and LongMemEval-S benchmarks with significant accuracy gains.
- Operates within an 8 GB VRAM budget, suitable for consumer-grade edge devices.
- Addresses the hubness problem and syntactic fragmentation in high-dimensional retrieval.
Paper Resources
Source Excerpt
Personalized dialogue agents require continuous long-term memory to maintain coherent interactions across multiple sessions. However, deploying these capabilities on consumer-grade hardware (e. g. , 8 GB VRAM edge devices) introduces severe memory and compute bottlenecks. Existing systems typically rely on isotropic cosine similarity for retrieval and heuristic rules for context compression. These approaches lack a unified theoretical foundation, frequently suffering from the hubness problem in hi
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