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LLM-based Multi-Agent Systems: Techniques and Business Perspectives
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LLM-based Multi-Agent Systems: Techniques and Business Perspectives
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In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents. The LLM agents can perceive, control, and get feedback from the environment so as to accomplish the given tasks in an autonomous manner. Besides the environment-interaction property, the LLM agents can call various external tools to ease the task completion process. The tools can be regarded as a predefined operational process with private or real-time knowledge that does not exist in the parameters of LLMs. As a natural trend of development, the tools for calling are becoming autonomous agents, thus the full intelligent system turns out to be a LLM-based Multi-Agent System (LaMAS). Compared to the previous single-LLM-agent system, LaMAS has the advantages of i) dynamic task decomposition and organic specialization, ii) higher flexibility for system changing, iii) proprietary data preserving for each participating entity, and iv) feasibility of monetization for each entity. This paper discusses the technical and business landscapes of LaMAS. To support the ecosystem of LaMAS, we provide a preliminary version of such LaMAS protocol considering technical requirements, data privacy, and business incentives. As such, LaMAS would be a practical solution to achieve artificial collective intelligence in the near future.
Forward citations
Cited by 8 Pith papers
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Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems
A survey that unifies prior work on multi-agent LLM systems via the LIFE framework, mapping dependencies across collaboration, failure attribution, and autonomous self-evolution while identifying cross-stage challenges.
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The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
Reasoning messages between heterogeneous VLMs can be routed through the image-token span: a distilled universal codec plus affine alignment transmits latent traces across model families, cutting wall-clock time in sma...
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Latent Collaboration in Multi-Agent Systems
Replacing text inter-agent dialogue with direct transfer of hidden-state (KV-cache) representations cuts output tokens by ~70-84%, speeds inference ~4x, and keeps multi-agent accuracy roughly on par or slightly better.
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Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks
A Bayesian-network monitor built on calibrated LLM log-probabilities gives workflow-level uncertainty scores for an actuarial multi-agent system, reproducing baseline RMSE but not clearly separating normal from pertur...
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Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems
The survey proposes the LIFE framework to unify fragmented research on collaboration, failure attribution, and self-evolution in LLM multi-agent systems into a progression toward self-organizing intelligence.
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Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models
A two-framework testbed comparison claims mem0 is Pareto-optimal over Graphiti for distributed LLM agents because its lower cost is paired with accuracy that is not significantly different.
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Latent Collaboration in Multi-Agent Systems
LatentMAS lets LLM agents reason and communicate in continuous hidden space via latent thoughts and KV-cache transfer, reporting higher accuracy and much lower token use than text-based multi-agent baselines.
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Position: Agentic AI System Is a Foreseeable Pathway to AGI
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.
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