CoreMem: Riemannian Retrieval and Fisher-Guided Distillation for Long-Term Memory in Dialogue Agents
arXiv: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
The increasing sophistication and personalized requirements of AI dialogue agents are pushing the limits of current memory and compute architectures, particularly for edge devices.
This research addresses a fundamental bottleneck in AI agent development, enabling more powerful and ubiquitous personalized AI experiences on constrained hardware.
The ability to run continuous, intelligent dialogue agents on consumer-grade hardware shifts the landscape for AI product deployment and accessibility.
- · AI developers
- · Edge device manufacturers
- · Consumer electronics
- · Cloud AI providers
- · Companies relying solely on large-scale data centers for AI
- · Legacy AI solutions with high computational demands
More sophisticated and personalized AI assistants become feasible on personal devices.
Increased demand for efficient AI algorithms and specialized edge AI hardware.
Accelerated adoption of AI agents across various consumer and industrial sectors, reducing reliance on constant cloud connectivity for basic functions.
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Read at arXiv cs.CL