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
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. 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
Article Content
From source RSS / original summaryarXiv:2606. 18406v1 Announce Type: new Abstract: 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 high-dimensional retrieval and syntactic fragmentation during compression. To overcome these limitations, we propose CoreMem, a resource-efficient edge-cloud memory architecture fundamentally unified by information geometry.
First, Riemannian retrieval replaces cosine matching with a locally adaptive Fisher-Rao metric, effectively penalizing hub memories via Mahalanobis distance with O(Ndr) Woodbury acceleration for real-time search. Second, Fisher-guided discrete token distillation (FDTD) introduces a hierarchical sentence-to-token compression mechanism. It derives sensitivity scores from Fisher information traces, providing a principled compression-KL tradeoff augmented with explicit structural syntax protection.
Evaluated on the LOCOMO and LongMemEval-S benchmarks, CoreMem achieves strong accuracy improvements, yielding substantial gains in Open-domain (+4. 51 pp) and Temporal (+4. 17 pp) reasoning. Extensive profiling confirms that CoreMem operates seamlessly within a strict 8 GB VRAM budget, successfully bridging the gap between resource-constrained edge devices and the demand for theoretically grounded, lifelong memory agents.
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