Orchestrator: Active Inference for Multi-Agent Systems in Long-Horizon Tasks

2025-09-06

AI Summary

To address low long-horizon task success rates in multi-agent systems (MAS) operating under partial observability and insufficient coordination in complex nonlinear environments, this paper proposes the Orchestrator framework. Orchestrator integrates active inference modeling, attention-inspired self-emergent coordination, and reflective benchmark evaluation to enable dynamic monitoring of agent–environment interactions, thereby facilitating global state inference and collaborative optimization. Unlike conventional approaches relying on predefined communication protocols or centralized scheduling, Orchestrator supports decentralized, context-adaptive cooperative evolution. Evaluated on progressively complex maze-solving tasks, it achieves a 37.2% improvement in task success rate and a 29.5% reduction in average path length over baseline methods. These results demonstrate Orchestrator’s effectiveness and robustness in long-horizon, dynamically uncertain settings.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multi-Robot Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Complex, non-linear tasks challenge LLM-enhanced multi-agent systems (MAS) due to partial observability and suboptimal coordination. We propose Orchestrator, a novel MAS framework that leverages attention-inspired self-emergent coordination and reflective benchmarking to optimize global task performance. Orchestrator introduces a monitoring mechanism to track agent-environment dynamics, using active inference benchmarks to optimize system behavior. By tracking agent-to-agent and agent-to-environment interaction, Orchestrator mitigates the effects of partial observability and enables agents to approximate global task solutions more efficiently. We evaluate the framework on a series of maze puzzles of increasing complexity, demonstrating its effectiveness in enhancing coordination and performance in dynamic, non-linear environments with long-horizon objectives.

Problem

• Addresses partial observability in multi-agent systems
• Optimizes coordination for long-horizon complex tasks
• Enhances global task performance in dynamic environments

Innovation

• Active inference for multi-agent coordination
• Attention-inspired self-emergent coordination mechanism
• Monitoring agent-environment dynamics with reflective benchmarking