MAS-on-the-Fly: Dynamic Adaptation of LLM-based Multi-Agent Systems at Test Time

• URL: http://arxiv.org/abs/2602.13671v1
• Date: Sat, 14 Feb 2026 08:38:13 GMT
• Title: MAS-on-the-Fly: Dynamic Adaptation of LLM-based Multi-Agent Systems at Test Time
• Authors: Guangyi Liu, Haojun Lin, Huan Zeng, Heng Wang, Quanming Yao,
• Abstract summary: We introduce MASFly, a novel multi-agent framework enabling dynamic adaptation at test time.<n>For adaptive execution, MASFly incorporates an experience-guided supervision mechanism, where a dedicated Watcher agent monitors system behaviors.<n>Experiments demonstrate that MASFly achieves state-of-the-art performance, most notably a 61.7% success rate on the TravelPlanner benchmark.
• Score: 32.22206915939924
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Model (LLM)-based multi-agent systems (MAS) have emerged as a promising paradigm for solving complex tasks. However, existing works often rely on manual designs or "one-size-fits-all" automation, lacking dynamic adaptability after deployment. Inspired by how biological systems adapt, we introduce MASFly, a novel multi-agent framework enabling dynamic adaptation at test time. To adapt system generation, MASFly employs a retrieval-augmented SOP instantiation mechanism that leverages a self-constructed repository of successful collaboration patterns, enabling the LLM to assemble customized MASs for new queries. For adaptive execution, MASFly incorporates an experience-guided supervision mechanism, where a dedicated Watcher agent monitors system behaviors with reference to a personalized experience pool and provides real-time interventions. Extensive experiments demonstrate that MASFly achieves state-of-the-art performance, most notably a 61.7% success rate on the TravelPlanner benchmark, while exhibiting strong task adaptability and robustness.

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