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

2026-05-30 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, quick

Summary

The CoMIC framework addresses the challenges of deploying lightweight Large Language Model (LLM) agents on resource-constrained edge servers for long-horizon tasks. These agents typically struggle with persistent memory, subgoal tracking, and reflection, while fine-tuning is costly and local memory isolates experiences. CoMIC, a parameter-update-free cloud-edge framework, employs a "Centralized Reflection, Decentralized Execution" design. Edge agents perform local execution using subgoal-oriented hierarchical memory and selectively re-expand relevant histories. Concurrently, a cloud-side LLM critic asynchronously evaluates completed trajectories, filters reusable experience, and aggregates cross-agent guidance, keyed by semantic subgoal identifiers. This approach improves progress rate and action grounding for weak edge agents and delivers task-dependent success-rate gains across five long-horizon agent tasks, including symbolic planning and text interaction, without requiring model parameter updates.

Key takeaway

For AI Engineers deploying LLM agents on edge devices, CoMIC offers a compelling solution to overcome resource constraints for long-horizon tasks. You can achieve improved progress rates and action grounding without costly model fine-tuning or parameter updates. Consider implementing this "Centralized Reflection, Decentralized Execution" framework to enhance agent performance in applications like symbolic planning and complex text interaction, leveraging existing weak edge models more effectively.

Key insights

CoMIC enables resource-constrained edge LLM agents to tackle long-horizon tasks by circulating memory and insights between decentralized execution and centralized cloud reflection.

Principles

• Centralized reflection guides decentralized execution.
• Asynchronous cloud-edge collaboration enhances agent performance.
• Subgoal-oriented hierarchical memory improves task handling.

Method

Edge agents execute locally with hierarchical memory and selective history re-expansion. A cloud LLM critic asynchronously evaluates trajectories, filters experience, and aggregates cross-agent guidance via semantic subgoal identifiers.

In practice

• Deploy LLM agents on edge without fine-tuning.
• Improve weak edge agents on long-horizon tasks.
• Enhance symbolic planning and text interaction.

Topics

• LLM Agents
• Cloud-Edge Computing
• Collaborative Memory
• Distributed AI
• Long-Horizon Tasks
• Edge AI

Best for: Research Scientist, AI Scientist, AI Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.