RA-MoWE: Workflow-Affinity Embeddings for Query Clustering and Agentic Workflow Generation

Abstract:Agentic workflows enable large language models (LLMs) to solve complex tasks by coordinating reasoning, tool use, and verification. However, a workflow optimized for an entire task collection can overlook differences in the reasoning strategies that individual queries need, while searching for a new workflow for every query repeats costly optimization. To address this tradeoff, we introduce RA-MoWE, a framework that uses workflow-affinity embeddings to cluster queries and guide the generation of reusable expert workflows. Each embedding records how well a fixed set of reference workflows solves a query, revealing similarities in which reasoning strategies are effective. RA-MoWE uses each cluster's queries and average embedding to initialize and refine a specialized workflow through execution feedback. An embedding encoder predicts these embeddings from query text, allowing new queries to select a generated expert without first executing the reference workflows. On a 300-query test set drawn from four benchmarks spanning mathematics, science, and programming, RA-MoWE improves average task score by 4.04 percentage points over selecting among the reference workflows, while using 27.7% fewer language-model calls at inference.

Artificial Intelligence (cs.AI)

arXiv:2610.07851 [cs.AI]

(or arXiv:2610.07851v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.07851 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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