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

• URL: http://arxiv.org/abs/2509.05651v1
• Date: Sat, 06 Sep 2025 09:03:36 GMT
• Title: Orchestrator: Active Inference for Multi-Agent Systems in Long-Horizon Tasks
• Authors: Lukas Beckenbauer, Johannes-Lucas Loewe, Ge Zheng, Alexandra Brintrup,
• Abstract summary: Complex, non-linear tasks challenge multi-agent systems (MAS) due to partial observability and suboptimal coordination.<n>We propose Orchestrator, a novel MAS framework that leverages attention-inspired self-emergent coordination and reflective benchmarking to optimize global task performance.<n>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.
• Score: 47.3494579474486
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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