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When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment

Minrui Xu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shiwen Mao, Zhu Han, Dong In Kim, Khaled B. Letaief

arXiv:2401.07764v2cs.AIcs.NI

TL;DR

Limited mobile capacity and context windows constrain long-horizon LLM-agent interactions. The paper proposes collaborative mobile-edge agents, with a caching algorithm for model use. In its case study, edge inference costs decrease as time slots increase.

  • Problem

    Limited mobile compute, memory, and context windows constrain local LLM agents’ ability to support long-term, complex interactions.

    Source: §I · p. 1

  • Method

    The paper proposes a split-learning system that distributes LLM agents across mobile devices and edge servers, alongside model caching to improve in-context learning and reduce network costs.

    Source: §I · p. 2

  • Results

    As time slots increase, edge inference costs decrease due to lower switching costs and higher model performance.

    Source: §VI · p. 7

  • Takeaways & Limitations

    The proposed caching algorithm evicts global models with the least impactful and relevant thoughts to reduce grounding costs in latency, resource consumption, and performance loss.

    Source: §I · p. 2

    Collaboration between mobile and edge agents raises model-privacy concerns, including possible access to users’ private information by malicious edge servers.

    Source: §VII · p. 8

Abstract

from arXiv · show

AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various domains like healthcare, education, manufacturing, and entertainment. Deploying LLM agents in 6G networks enables users to access previously expensive AI assistant services via mobile devices democratically, thereby reducing interaction latency and better preserving user privacy. Nevertheless, the limited capacity of mobile devices constrains the effectiveness of deploying and executing local LLMs, which necessitates offloading complex tasks to global LLMs running on edge servers during long-horizon interactions. In this article, we propose a split learning system for LLM agents in 6G networks leveraging the collaboration between mobile devices and edge servers, where multiple LLMs with different roles are distributed across mobile devices and edge servers to perform user-agent interactive tasks collaboratively. In the proposed system, LLM agents are split into perception, grounding, and alignment modules, facilitating inter-module communications to meet extended user requirements on 6G network functions, including integrated sensing and communication, digital twins, and task-oriented communications. Furthermore, we introduce a novel model caching algorithm for LLMs within the proposed system to improve model utilization in context, thus reducing network costs of the collaborative mobile and edge LLM agents.

I. INTRODUCTION

Mobile-device compute, memory, and context-window limits make long-horizon LLM-agent interactions difficult. The article proposes collaborative mobile-edge agents and discusses their roles in perception, grounding, alignment, and model caching.

I. INTRODUCTION

  • Limited mobile compute, memory, and LLM context windows challenge long-term, complex agent interactions, motivating collaboration between local mobile and global edge LLM agents.Mobile agents handle real-time perception and alignment; edge agents use global information and historical memory for complex tasks.Source: §I · p. 1
  • Partitioning agents across mobile and edge devices supports heterogeneous deployment, long-horizon collaboration, and adaptability in dynamic open-ended environments.The collaboration bridges local low-level operational plans with global high-level strategic plans.Source: §I · p. 1
  • The proposed split-learning system distributes LLM agents across mobile devices and edge servers to support democratic, flexible, long-horizon services in open-ended environments.The article also describes collaborative accident-report generation, with mobile agents reporting local observations and edge agents producing more detailed plans.Source: §I · p. 2
  • The article discusses integrated sensing and communication for multimodal perception, digital twins for grounding decisions, and task-oriented communications for agent alignment.Source: §I · p. 2
  • The proposed model-caching framework aims to maximize in-context learning while reducing network costs, using Least Age-of-Thought eviction to reduce grounding costs.Age of thought assesses the significance of intermediate reasoning steps; the algorithm evicts global models with the least impactful and relevant thoughts.Source: §I · p. 2

II. COLLABORATIVE END-EDGE-CLOUD COMPUTING FOR LLM AGENTS IN 6G NETWORKS

The paper distinguishes reinforcement-learning agents from LLM agents within collaborative 6G systems. RL agents learn network decisions through feedback but have limited ability to interact with people in open-ended settings.

II. COLLABORATIVE END-EDGE-CLOUD COMPUTING FOR LLM AGENTS IN 6G NETWORKS

  • AI agents in 6G networks are described as computational entities for collaborative tasks, with reinforcement-learning agents and LLM agents identified as two major categories.Source: §II · p. 2

1) RL Agents:

  • RL agents use trial-and-error rewards or penalties to learn policies for decisions such as network access, transmit power, caching, and offloading.Their environment is formulated as a Markov decision process with states, actions, transition probabilities, and rewards.Source: 1) RL Agents · p. 2
  • RL agents cannot interact with humans and other agents using text in open-ended environments, limiting their potential for services requiring instruction understanding and responses.Source: 1) RL Agents · p. 2

2) LLM Agents:

LLM agents combine language-based interaction, tools, and actions with multimodal perception. In the collaborative architecture, mobile-agent execution, edge-agent execution, and communication between them form its main processes.

2) LLM Agents:

  • LLM agents interact with environments through text, API tools, and embodied actions, using memory, reasoning, planning, and tools to tackle complex tasks.Source: 2) LLM Agents · p. 2
  • Multimodal LLM agents process inputs such as visual, audio, tactile, motion-sensor, and 3D-map data, converting modalities into a common textual representation for cross-modal reasoning.Source: 2) LLM Agents · pp. 2–3

B. Construction of Mobile Edge-empowered Agents

1) Mobile LLM Agent Execution:

Mobile agents are initialized and process local instructions and sensory inputs, while edge agents use global models, memory, and reasoning to support complex tasks. The two execution roles reflect the different capacities of mobile devices and edge servers.

1) Mobile LLM Agent Execution:

  • Users download tiny local LLMs to mobile devices and can configure profiles and interaction history for personalized initialization.Mobile agents receive human instructions or multimodal sensory inputs from the physical environment.Source: 1) Mobile LLM Agent Execution · p. 3
  • Mobile agents combine multimodal inputs using modality encoders and projection layers, while limited-capacity local LLMs can generate real-time responses but not handle complex tasks requiring comprehensive consideration and generalization.Source: 1) Mobile LLM Agent Execution · p. 3

2) Global Agent Execution:

  • Edge agents use long-term memory, retrieval-augmented generation, chain-of-thought reasoning, and self-reflection to improve complex-task responses and decisions.Historical interactions can be stored in vector databases; self-reflection verifies reasoning paths.Source: 2) Global Agent Execution · pp. 3–4

C. Inter-agent Communication between Local and Edge Agents

Mobile agents perceive instructions and environments, then offload complex-task intermediates to edge agents for remote execution. Integrated sensing and communication supports simultaneous environmental sensing and transmission of those intermediates.

C. Inter-agent Communication between Local and Edge Agents

  • Mobile agents offload local perceptions and user intentions to edge agents when they cannot complete complex tasks; they then adapt edge-generated general plans into local plans and act locally.Intermediates can be text or embeddings sent over RANs; offloading must account for limited bandwidth and uncertain wireless channels.Source: C. Inter-agent Communication between… · p. 4

III. ISAC FOR WIRELESS PERCEPTION: UBIQUITOUS AND ADAPTABILITY

  • Mobile agents perceive instructions and environments, extract information from noisy observations, and communicate with edge servers to transfer it.These functions require integrated sensing and communication using wireless infrastructure.Source: §III · p. 4

1) Environmental Perception:

  • Mobile agents can sense through vision, text, and light sensitivity, while vehicular agents can use Lidar, GPS, and IMUs for location-based data.Source: 1) Environmental Perception · p. 4

2) Human-language Instruction:

Mobile agents receive text and audio instructions, while edge collaboration supports grounding through interactive digital twins. Continuous inter-agent exchanges consume bandwidth, and integrated sensing and communication is proposed to improve resource efficiency.

2) Human-language Instruction:

  • Text instructions can convey explicit requests as well as implied values and intentions, which mobile agents interpret using contextual interaction and short-term memory.Source: 2) Human-language Instruction · p. 4
  • Audio instructions carry environmental information, and handling them can combine existing models, cascading paradigms, and other modalities.Source: 2) Human-language Instruction · p. 4

3) Inter-agent Interactions:

  • Mobile and edge agents exchange intermediate results, feedback, reasoning, and self-reflection over RANs during long-term multimodal interactions.The continuous messaging consumes substantial bandwidth and network resources.Source: 3) Inter-agent Interactions · p. 4
  • Integrated sensing and communication can let vehicular agents sense with radar while transmitting perception results to edge agents.The passage presents this simultaneous use as a way to use network resources more efficiently for sensing and communication.Source: 3) Inter-agent Interactions · p. 4

IV. DIGITAL TWINS FOR WIRELESS GROUNDING: RELIABILITY AND CONSISTENCY

  • Mobile agents maintain edge digital twins as replicas of physical entities built from perceived data, supporting global grounding with internal memory and external knowledge.The twins are continuously updated with external observations and internal reasoning results.Source: §IV · p. 4
  • Edge digital twins support interactive retrieval-augmented generation, reasoning, planning, and reflection over highly reliable, low-latency communication.They can monitor, analyze, and optimize mobile-agent decisions during complex tasks.Source: §IV · p. 4

1) Memory and Retrieval-Augmented Generation:

Mobile agents use short-term memory, while edge agents maintain long-term memory for grounding responses and actions. Retrieval, multi-step reasoning, and verification can support complex tasks, with grounding performance traded against computing and networking resources.

1) Memory and Retrieval-Augmented Generation:

  • Mobile agents retain short-term memory, which can be converted to edge-hosted long-term memory in resources such as vector databases for rapid retrieval.Source: 1) Memory and Retrieval-Augmented… · pp. 4–5
  • Edge agents use retrieval-augmented generation to incorporate relevant knowledge-base or reference-document content, supporting consistency and complex, long-horizon tasks.Retrieved material can provide specialized knowledge, up-to-date information, and customizable definitions; edge agents can also access mobile agents’ past responses.Source: 1) Memory and Retrieval-Augmented… · p. 5

2) Reasoning and Planning:

  • Edge agents can decompose tasks using chain-of-thought, self-consistent chain-of-thought, tree-of-thoughts, or graph-of-thoughts reasoning and planning.These multi-step mechanisms may consume more edge computing resources than direct output generation.Source: 2) Reasoning and Planning · p. 5

3) Verification and Reflection:

  • Verification reflection can check each chain-of-thought step before response generation; SelfCheck is described as a zero-shot, context-based step checker using confidence scores to weight answers.The passage states that this approach aims to improve question-answering accuracy.Source: 3) Verification and Reflection · p. 5
  • Grounding involves a tradeoff between module performance and computing resources, while inter-agent cross-verification also requires network resources to transmit correction results.Source: 3) Verification and Reflection · p. 5

V. TASK-ORIENTED COMMUNICATIONS FOR WIRELESS ALIGNMENT: TRUSTWORTHY AND GENERALIZABILITY

The paper frames alignment as task-oriented communication: success is measured by application-task completion, and users can contribute feedback for model regularization. It also discusses hallucination risks and tool-use customization, illustrating mobile-edge agents generating crash accident reports.

V. TASK-ORIENTED COMMUNICATIONS FOR WIRELESS ALIGNMENT: TRUSTWORTHY AND GENERALIZABILITY

  • Task-oriented communication measures performance by success in application-related tasks rather than data rate or link reliability, and the passage connects this framing to semantic alignment among agents and humans.Human and agent feedback can form datasets for supervised fine-tuning, reinforcement learning from human feedback, and direct preference optimization of global LLMs.Source: §V · p. 5

1) Text Responses:

  • Training-test distribution shifts in biased pretraining data can cause hallucinations, while wireless alignment can let users contribute effort and computing resources to alignment activities.Source: 1) Text Responses · p. 5

2) Tool Usage and Generation:

  • Mobile agents can be instruction-fine-tuned on API datasets and customized with real-world APIs and practical scenarios for single- and multitool tasks.The figure gives crash accident-report generation as an example of mobile and edge agent use.Source: 2) Tool Usage and Generation · pp. 5–6, Figure 2 · p. 6

3) Embodied Actions:

Mobile agents use embodied actions to interact directly with and manipulate physical surroundings, extending agent capabilities beyond digital environments.

3) Embodied Actions:

  • Embodied actions let mobile agents interact with and manipulate physical surroundings, with vehicles and robots serving as examples.Vehicles can adapt to road conditions; robots can gather information, carry out tasks, and interact with people and other agents.Source: 3) Embodied Actions · p. 6

VI. CASE STUDY OF MODEL CACHING FOR COLLABORATIVE MOBILE AND EDGE LLM AGENTS

The case study uses collaborative mobile and edge agents for vehicular accident reporting and proposes AoT-based model caching. Its LC algorithm improves edge-agent accuracy while reducing total execution costs relative to cloud-only inference, FIFO, and LFU.

Collaborative agent design

  • Mobile agents contain perception, local reasoning, and alignment modules, while edge agents provide global reasoning and planning.Local LLMs support zero-shot environmental perception; global LLMs support more intricate step-by-step reasoning and planning with global information.Source: §VI · p. 6

Perception evaluation

  • Perception is evaluated across image, video, infrared, depth, audio, and IMU modalities using six corresponding datasets.The cited table caption describes evaluation of mobile agents with different perception modules across modalities.Source: §VI · p. 6, Table I · p. 7

Vehicular case study

  • Mobile agents report local crash observations to edge agents, which combine them into general accident reports using global information and high-level plans.The case study uses the Car Crash dataset; the mobile perception, grounding, and alignment modules use Video-LLaMA, LLaMA-7B-Chat, and ToolLLM, respectively, while the edge agent uses GPT-4.Source: §VI · pp. 6–7

Grounding evaluation

  • Age-of-thought (AoT) measures thought value by freshness, with thoughts nearer final decisions treated as more valuable.The edge agent aggregates mobile agents’ local perceptions before multistep reasoning and planning.Source: §VI · p. 7

Model caching algorithm

  • LAoT evicts the model with the least valuable thoughts to manage edge-server memory, which cannot hold all models simultaneously.The caching objective includes edge accuracy loss, model-switching cost, inference cost and latency, and cloud inference cost.Source: §VI · p. 7

Model caching results

  • LC improves edge-agent accuracy while reducing total execution costs versus cloud-only inference, FIFO, and LFU.As time slots increase, edge inference costs decline with lower switching costs and higher model performance.Source: Figure 3 · p. 7, §VI · p. 7

VII. CONCLUSIONS AND FUTURE DIRECTIONS

The paper concludes with a collaborative end-edge-cloud system for LLM agents and a vehicular model-caching use case. It identifies dynamic wireless integration and privacy during mobile-edge collaboration as future concerns.

Conclusions

  • The paper proposes collaborative end-edge-cloud LLM agents for multimodal perception, interactive grounding, and alignment, alongside a vehicular caching use case.The caching algorithm is intended to optimize AI-agent service performance while reducing execution costs.Source: §VII · p. 7

Future directions

  • Future work includes integrating next-generation multiple access, metasurfaces, and over-the-air computation to support agents in dynamic wireless environments..Source: §VII · pp. 7–8
  • The authors flag privacy concerns in mobile-edge collaboration, especially possible access to users’ private information by malicious edge servers.The stated concern is information exposure from running models.Source: §VII · p. 8
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