Collaborative Optimization for Agent Swarms through Flow Networks

❗ Important Notice ❗
This is not a seminar! Participants are encouraged to read the material in advance to engage effectively with the technical and methodological aspects during the session.

If you possess any research, experiences, or have come across relevant content online (such as blog posts or articles) that could enhance the discussion, please do share!

Suggested Pre-Reading:

• Main Paper: MANGO
• Lineage Papers:
  • MaAS
  • AFlow
  • InstructRAG

Multi-agent systems enhance large language models (LLMs) by breaking down complex tasks into specialized subtasks handled by various agents. However, their effectiveness can be hindered by error propagation from suboptimal workflows and imperfect intermediate outputs.
Zheng Wang will introduce MANGO (Multi-Agent Network Gradient Optimization), a data-driven framework that depicts agent collaboration as a flow network based on successful historic workflows. MANGO marries reinforcement learning with textual gradient signals to optimize workflow structures and agent behaviors collectively, and introduces a skipping mechanism to boost optimization efficiency.

More About the Host

Zheng Wang serves as Assistant Chief Expert at Huawei Singapore Research Center under the TopMinds program. He earned his PhD from Nanyang Technological University, Singapore. His research interests revolve around AI Agents, encompassing retrieval-augmented generation, agent planning, and multimodality. With over 40 publications in prestigious AI and data science conferences and journals, his work has received accolades such as the ACM SIGSPATIAL China Rising Star Award, Outstanding PhD Thesis Award, WAIC Yunfan Award, Google PhD Fellowship, and AISG PhD Fellowship.

More About the Series

Paper Club is Lorong AI’s community-driven initiative where members convene to discuss and analyze academic papers, research articles, or significant advancements in artificial intelligence.

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