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New framework enables self-evolving agentic workflows for complex tasks

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a framework for self-evolving agentic workflows that decompose complex tasks into smaller, manageable subtasks. This approach aims to reduce inference costs and delays associated with invoking large language models at every step. The framework allows agents to refine task decomposition and update procedures based on execution feedback, with experiments in the GLEE competition demonstrating its effectiveness in areas like bargaining and negotiation. AI

IMPACT This framework could lead to more efficient and cost-effective AI agents for complex problem-solving.

RANK_REASON The cluster contains an academic paper detailing a new framework for agentic workflows.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework enables self-evolving agentic workflows for complex tasks

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The cluster contains an academic paper detailing a new framework for agentic workflows.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Zichuan Fu, Junjia Qi, Xian Wu, Wenlin Zhang, Yimin Deng, Kaifeng Guo, Xiangyu Zhao ·

    Self-Evolving Agentic Workflows via Semantic Decomposition of Complex Tasks

    arXiv:2611.01840v1 Announce Type: cross Abstract: Agentic workflows organize complex tasks into reusable procedures. However, invoking an LLM at every step incurs repeated inference costs and delays. We propose a framework for self-evolving agentic workflows through semantic task…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Xiangyu Zhao ·

    Self-Evolving Agentic Workflows via Semantic Decomposition of Complex Tasks

    Agentic workflows organize complex tasks into reusable procedures. However, invoking an LLM at every step incurs repeated inference costs and delays. We propose a framework for self-evolving agentic workflows through semantic task decomposition. Each subtask has explicit applicab…

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