Orchestrator: Active Inference for Multi-Agent Systems in Long-Horizon Tasks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912574412423168 |
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| author | Beckenbauer, Lukas Loewe, Johannes-Lucas Zheng, Ge Brintrup, Alexandra |
| author_facet | Beckenbauer, Lukas Loewe, Johannes-Lucas Zheng, Ge Brintrup, Alexandra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05651 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Orchestrator: Active Inference for Multi-Agent Systems in Long-Horizon Tasks Beckenbauer, Lukas Loewe, Johannes-Lucas Zheng, Ge Brintrup, Alexandra Multiagent Systems Artificial Intelligence 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. |
| title | Orchestrator: Active Inference for Multi-Agent Systems in Long-Horizon Tasks |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2509.05651 |