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New RA-MoWE framework enhances LLM agentic workflows with query clustering

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RA-MoWE, a new framework designed to improve the efficiency and effectiveness of agentic workflows for large language models (LLMs). RA-MoWE utilizes workflow-affinity embeddings to cluster queries, enabling the generation of reusable expert workflows tailored to specific reasoning strategies. This approach aims to balance the optimization of workflows for task collections with the need for query-specific reasoning, outperforming existing methods in benchmarks. AI

IMPACT This framework could lead to more efficient and specialized LLM agentic workflows, improving performance on complex tasks.

RANK_REASON The cluster contains a research paper detailing a new framework for LLM agentic workflows. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New RA-MoWE framework enhances LLM agentic workflows with query clustering

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The cluster contains a research paper detailing a new framework for LLM agentic workflows. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Qi Cheng, Shengyu Chen, Wei Cheng, Yiqun Xie, Haoyu Wang, Haifeng Chen, Xiaowei Jia ·

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

    arXiv:2610.07851v1 Announce Type: new 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 reasonin…

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