When Should We Orchestrate Multiple Agents?
Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration. We design a framework to orchestrate agents under realistic conditions, such as inference costs or availability constraints. We show theoretically that orchestration is only effective if there are performance or cost differentials between agents. We then empirically demonstrate how orchestration between multiple agents can be helpful for selecting agents in a simulated environment, picking a learning strategy in the infamous Rogers' Paradox from social science, and outsourcing tasks to other agents during a question-answer task in a user study.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs
Intent-based management will play a critical role in achieving customers' expectations in the next-generation mobile networks. Traditional methods cannot perform efficient resource management since they tend to handle ea…
ManagementMulti-agent Reinforcement LearningPosition: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives
Decomposing hard problems into subproblems often makes them easier and more efficient to solve. With large language models (LLMs) crossing critical reliability thresholds for a growing slate of capabilities, there is an …
PositionCOSMIR: Chain Orchestrated Structured Memory for Iterative Reasoning over Long Context
Reasoning over very long inputs remains difficult for large language models (LLMs). Common workarounds either shrink the input via retrieval (risking missed evidence), enlarge the context window (straining selectivity), …
EE-MCP: Self-Evolving MCP-GUI Agents via Automated Environment Generation and Experience Learning
Computer-use agents that combine GUI interaction with structured API calls via the Model Context Protocol (MCP) show promise for automating software tasks. However, existing approaches lack a principled understanding of …
Wavefield Networked Sensing: Principles, Algorithms and Applications
Networked sensing refers to the capability of properly orchestrating multiple sensing terminals to enhance specific figures of merit, e.g., positioning accuracy or imaging resolution. Regarding radio-based sensing, it is…
valid