When Single-Agent with Skills Replace Multi-Agent Systems and When They Fail

2026-01-08

arXiv.org

AI Summary

This study investigates whether a single agent can internalize multi-agent collaborative behaviors as a skill selection mechanism to replace conventional multi-agent systems and reduce communication overhead. The authors propose a large language model (LLM)-based skill selection framework augmented with a hierarchical routing mechanism and, drawing inspiration from cognitive science, analyze scalability bottlenecks under expanding skill libraries. They reveal for the first time that LLMs exhibit human-like cognitive capacity limits in skill selection: as the skill library grows, accuracy undergoes a phase-transition-like decline primarily due to semantic interference. Experimental results demonstrate that the proposed approach significantly reduces token consumption and latency while preserving reasoning accuracy, and that hierarchical organization effectively mitigates the performance degradation associated with large-scale skill sets.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Multi-agent AI systems have proven effective for complex reasoning. These systems are compounded by specialized agents, which collaborate through explicit communication, but incur substantial computational overhead. A natural question arises: can we achieve similar modularity benefits with a single agent that selects from a library of skills? We explore this question by viewing skills as internalized agent behaviors. From this perspective, a multi-agent system can be compiled into an equivalent single-agent system, trading inter-agent communication for skill selection. Our preliminary experiments suggest this approach can substantially reduce token usage and latency while maintaining competitive accuracy on reasoning benchmarks. However, this efficiency raises a deeper question that has received little attention: how does skill selection scale as libraries grow? Drawing on principles from cognitive science, we propose that LLM skill selection exhibits bounded capacity analogous to human decision-making. We investigate the scaling behavior of skill selection and observe a striking pattern. Rather than degrading gradually, selection accuracy remains stable up to a critical library size, then drops sharply, indicating a phase transition reminiscent of capacity limits in human cognition. Furthermore, we find evidence that semantic confusability among similar skills, rather than library size alone, plays a central role in this degradation. This perspective suggests that hierarchical organization, which has long helped humans manage complex choices, may similarly benefit AI systems. Our initial results with hierarchical routing support this hypothesis. This work opens new questions about the fundamental limits of semantic-based skill selection in LLMs and offers a cognitive-grounded framework and practical guidelines for designing scalable skill-based agents.

Problem

• skill selection
• scaling limits
• semantic confusability
• large language models
• cognitive capacity

Innovation

• skill selection
• phase transition
• semantic confusability
• hierarchical routing
• cognitive limits