CoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Cloud-Edge Systems

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 $\text{textsc}CoMIC$, a parameter-update-free cloud-edge framework for Collaborative Memory and Insights Circulation. $\text{textsc}CoMIC$ follows a $\mathit{\text{Centralized}}\mathit{\text{Reflection,}}\mathit{\text{Decentralized}}\mathit{\text{Execution}}$ design: edge agents execute locally using subgoal-oriented hierarchical memory and selective re-expansion of relevant histories, while a cloud-side LLM critic asynchronously evaluates completed trajectories, filters reusable experience, and aggregates cross-agent guidance keyed by semantic subgoal identifiers. Across five long-horizon agent tasks spanning symbolic planning and text interaction, $\text{textsc}CoMIC$ improves progress rate and action grounding for weak edge agents and yields task-dependent success-rate gains without updating model parameters.