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About this paper
Humza Sami, Mubashir ul Islam, Pierre-Emmanuel Gaillardon, Valerio Tenace
Researchers from PrimisAI and the University of Utah developed Nexus Architect, a multi-agent system capable of generating tailored reasoning workflows for off-the-shelf language models. This system consistently outperformed state-of-the-art Large Reasoning Models on a custom benchmark, achieving up to a 66.17% higher pass rate compared to the best standalone LRM.
Key insights
- Robust and generalizable reasoning can be achieved from standard, off-the-shelf LLMs by embedding them within an adversarial multi-agent system framework, without modifying the base models.
- Automated workflow synthesis, combined with iterative prompt refinement, is effective in enhancing the generalization capabilities of multi-agent reasoning systems.
- The approach supports a shift in AI development towards intelligent workflow design and agentic automation rather than solely relying on increasing model complexity for improved reasoning.
Summary by alphaXiv.
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