Large Language Models over Networks: Collaborative Intelligence under Resource Constraints

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large-language-models collaborative-intelligence distributed-systems device-cloud-collaboration multi-agent-systems resource-constraints inference

Summary

This paper explores collaborative intelligence paradigms where distributed Large Language Models work together across devices and clouds to handle resource constraints. It covers vertical device-cloud collaboration, horizontal multi-agent collaboration, routing policies, and open research challenges in scalable and trustworthy cooperative AI.

Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a growing class of applications, including those operating under intermittent connectivity, sub-second latency budgets, data-residency constraints, or sustained high-volume inference. On-device deployment is in turn constrained by limited computation and memory. No single endpoint can deliver high-quality service across this spectrum. This article focuses on collaborative intelligence, a paradigm in which multiple independent LLMs distributed across device and cloud endpoints collaborate at the task level through natural language or structured messages. Such collaboration strives for superior response quality under heterogeneous resource constraints spanning computation, memory, communication, and cost across network tiers. We present collaborative inference along two complementary and composable dimensions: vertical device-cloud collaboration and horizontal multi-agent collaboration, which can be combined into hybrid topologies in practice. We then examine learning to collaborate, addressing the training of routing policies and the development of cooperative capabilities among LLMs. Finally, we identify open research challenges including scaling under resource heterogeneity and trustworthy collaborative intelligence.
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Paper page - Large Language Models over Networks: Collaborative Intelligence under Resource Constraints

Source: https://huggingface.co/papers/2605.08626

Abstract

Collaborative intelligence enables multiple distributed LLMs to work together across devices and clouds to provide high-quality responses under diverse resource constraints.

Large language models(LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a growing class of applications, including those operating under intermittent connectivity, sub-second latency budgets, data-residency constraints, or sustained high-volume inference. On-device deployment is in turn constrained by limited computation and memory. No single endpoint can deliver high-quality service across this spectrum. This article focuses oncollaborative intelligence, a paradigm in which multiple independent LLMs distributed across device and cloud endpoints collaborate at the task level through natural language or structured messages. Such collaboration strives for superior response quality under heterogeneous resource constraints spanning computation, memory, communication, and cost across network tiers. We presentcollaborative inferencealong two complementary and composable dimensions: verticaldevice-cloud collaborationand horizontalmulti-agent collaboration, which can be combined into hybrid topologies in practice. We then examine learning to collaborate, addressing the training ofrouting policiesand the development of cooperative capabilities among LLMs. Finally, we identify open research challenges including scaling underresource heterogeneityand trustworthycollaborative intelligence.

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