Multi-Agent Collaborative Reasoning with Compact Large Language Models
Keywords:
multi-agent systems; compact large language models; collaborative reasoning; orchestration; governance; robustness; sustainabilityAbstract
Recent progress in large language models has been driven by substantial increases in parameter scale, yet the operational, environmental, and governance costs of frontier models have generated growing interest in compact alternatives. This paper examines multi-agent collaborative reasoning as a systems-level strategy in which several small parameter language models jointly propose, critique, verify, and refine reasoning traces through structured interaction. Rather than treating collaborative reasoning as a narrow prompting technique, the analysis positions it as an architectural and socio-technical design problem. The paper discusses conceptual foundations, coordination architectures, deployment infrastructure, robustness and failure management, fairness and accountability, sustainability, and policy implications. Centralized, decentralized, and hierarchical coordination patterns are compared in terms of latency, oversight, and resilience. The analysis emphasizes that compact model ensembles can approach useful reasoning capability only when coordination overhead, correlated failures, and verification costs are explicitly managed. The paper argues that multi-agent systems should be evaluated not solely by benchmark accuracy but also by their auditability, risk-based escalation, energy consumption, and alignment with governance requirements. These considerations have direct consequences for platform operators, regulators, and organizations seeking to deploy capable but resource-constrained language reasoning systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
