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Pro-Router introduces a token-aware progressive model routing method for efficient multimodal LLM inference, leveraging adaptive edge-cloud collaboration to improve throughput and reduce costs.
CoreMem proposes a resource-efficient edge-cloud memory architecture for dialogue agents, using Riemannian retrieval with a Fisher-Rao metric and Fisher-guided discrete token distillation to achieve strong accuracy improvements within an 8 GB VRAM budget.
MemPrivacy is a research paper introducing a framework for privacy-preserving personalized memory management in edge-cloud AI agents, using type-aware placeholders to protect sensitive data while maintaining semantic utility. It includes a new benchmark dataset and demonstrates superior performance over general-purpose models like GPT-5.2 and Gemini-3.1-Pro.