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

• URL: http://arxiv.org/abs/2606.00756v1
• Date: Sat, 30 May 2026 14:45:39 GMT
• Title: CoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Cloud-Edge Systems
• Abstract summary: lightweight Large Language Model (LLM) agents on edge servers can reduce latency and move agent services closer to users.<n>Fine-tuning edge models after deployment is costly and difficult to scale across heterogeneous nodes.<n>We propose textscCoMIC, a parameter-update-free cloud-edge framework for Collaborative Memory and Circulation Insights.
• Score: 23.892901534769067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Collaborative Memory and Insights Circulation. \textsc{CoMIC} follows a \textit{Centralized Reflection, Decentralized 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, \textsc{CoMIC} improves progress rate and action grounding for weak edge agents and yields task-dependent success-rate gains without updating model parameters.

Related papers

• End-to-End Latency-Minimizing and Load-Balanced Request Scheduling for Edge LLM Inference in Agentic AI Services [13.04369901641143]
  Large language model (LLM)-powered agentic AI services increasingly demand low-latency inference.<n>We develop a cross-slot inference model that captures transmission, prefill, redistribution-level decoding, and key-value cache evolution for each diverse request.<n>We propose the LYREO approach that transforms the long-term load-balancing constraint via Lyapunov optimization and employs reward with sequencebased return prediction.
  arXiv  Detail & Related papers  (2026-09-15T13:50:54Z)
• Diffusion Language Models for Mobile Edge Agentic AI: Foundations, Applications, and Challenges [58.887152635127165]
  Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial intelligence (AI)<n>DLMs can update multiple uncertain tokens in parallel and exploit bidirectional context throughout the generation process.<n>This survey reviews DLM foundations and analyzes their suitability for edge settings under latency, memory, energy, bandwidth, privacy, and reliability constraints.
  arXiv  Detail & Related papers  (2026-09-04T06:16:49Z)
• SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent [51.274445160155864]
  Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions.<n>Existing approaches address this difficulty by truncating the interaction history, compressing it into shorter surrogates, or retrieving selected parts of it for reuse.<n>We propose State-Adaptive Memory, a standalone framework that consolidates ongoing interaction into compact memory cues while preserving raw trajectory pages for intent-driven recall.
  arXiv  Detail & Related papers  (2026-05-23T08:37:16Z)
• Hierarchical Long-Term Semantic Memory for LinkedIn's Hiring Agent [7.738449954712223]
  Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential.<n>Building industrial-grade long-term memory for LLM agents raises five challenges: scalability, low-latency retrieval, privacy constraints, cross-domain generalizability, and observability.<n>We introduce the Hierarchical Long-Term Semantic Memory (HLTM) framework, which organizes textual data into a schema-aligned memory tree that captures semantic knowledge at multiple levels of granularity.<n>HLTM has been deployed in LinkedIn's Hiring Assistant to power personalization core features in production hiring.
  arXiv  Detail & Related papers  (2026-04-29T00:53:52Z)
• AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents [115.18197165538943]
  Large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking.<n>Existing context management methods typically commit to a single fixed strategy throughout the entire trajectory.<n>We introduce a probabilistic framework that characterizes long-horizon success through two complementary dimensions: search efficiency and terminal precision.
  arXiv  Detail & Related papers  (2026-03-29T02:59:27Z)
• Refer-Agent: A Collaborative Multi-Agent System with Reasoning and Reflection for Referring Video Object Segmentation [50.22481337087162]
  Referring Video Object (RVOS) aims to segment objects in videos based on textual queries.<n>Refer-Agent is a collaborative multi-agent system with alternating reasoning-reflection mechanisms.
  arXiv  Detail & Related papers  (2026-02-03T14:48:12Z)
• AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts [78.33143446024485]
  We introduce textbfAgentLongBench, which evaluates agents through simulated environment rollouts based on Lateral Thinking Puzzles.<n>This framework generates rigorous interaction trajectories across knowledge-intensive and knowledge-free scenarios.
  arXiv  Detail & Related papers  (2026-01-28T16:05:44Z)
• Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
  Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
  arXiv  Detail & Related papers  (2026-01-05T08:24:16Z)
• Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces [5.110309385104824]
  Large Language Models (LLMs) face fundamental challenges in long-context reasoning.<n>Current solutions fail to build the space-time-anchored narrative representations required for tracking entities through episodic events.<n>We propose the textbfGenerative Semantic Workspace (GSW), a neuro-inspired generative memory framework.
  arXiv  Detail & Related papers  (2025-11-10T19:59:34Z)
• Gradientsys: A Multi-Agent LLM Scheduler with ReAct Orchestration [4.66888457790348]
  We present Gradientsys, a next-generation multi-agent scheduling framework.<n>It coordinates diverse specialized AI agents using a typed Model-Context Protocol (MCP) and a ReAct-based dynamic planning loop.<n>Experiments on the GAIA general-assistant benchmark show that Gradientsys achieves higher task success rates with reduced latency and lower API costs.
  arXiv  Detail & Related papers  (2025-07-09T03:40:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.