CoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Cloud-Edge Systems
arXiv:2606.00756v1 Announce Type: new Abstract: Deploying lightweight Large Language Model (LLM) agents on edge servers can reduce latency and move agentic services closer to users, but resource-constrained edge models often struggle with long-horizon tasks that require persistent memory, subgoal tracking, and reflection. Fine-tuning edge models after deployment is costly and difficult to scale across heterogeneous nodes, while purely local memory leaves agents with isolated experience and growing prompt context. We propose \textsc{CoMIC}, a parameter-update-free cloud-edge framework for Colla
The proliferation of LLM agents and the demand for low-latency, personalized AI services at the edge are driving innovation in distributed AI architectures.
This development addresses a critical limitation of current edge AI deployments by enabling more sophisticated, memory-enabled LLM agents, expanding the potential applications for localized autonomous systems.
LLM agents deployed on resource-constrained edge devices can now handle long-horizon tasks, maintain persistent memory, and collaboratively learn without costly fine-tuning, making edge AI more practical and efficient.
- · Edge computing providers
- · Developers of LLM agents
- · Users of localized AI services
- · Cloud-edge infrastructure providers
- · Centralized LLM service providers (retaining all intelligence)
- · Traditional edge device manufacturers (without AI focus)
- · Companies reliant solely on prompt engineering for agent evolution
Widespread adoption of complex LLM agents in edge environments for various industrial and consumer applications.
Increased demand for specialized hardware at the edge capable of supporting collaborative memory and inference for these advanced agents.
The emergence of new business models for distributed AI services, potentially shifting economic value towards local, context-aware AI operations over pure cloud-based models.
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Read at arXiv cs.AI