One-for-All Multi-Agent Collaboration: Adaptive Cross-Task Topology Design

The paper introduces OFA-MAS, a one‑for‑all multi‑agent system that learns a universal topology designer using task‑aware graph encoding and a Mixture‑of‑Experts generator, achieving superior performance, OOD generalization, robustness, and efficiency across six major benchmarks.

LLMMixture of ExpertsTask-Aware Graph Encoder

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One-for-All Multi-Agent Collaboration: Adaptive Cross-Task Topology Design

Architect

Jun 7, 2025 · Artificial Intelligence

Mass Framework: Boosting Multi‑Agent Design with Smarter Prompts & Topologies

The Mass framework, developed by Google and Cambridge University, automates multi‑agent system design by jointly optimizing prompts and topologies through three staged processes, demonstrating significant performance gains over existing methods across various tasks while highlighting the importance of coordinated prompt‑topology optimization.

AI researchMass frameworkTopology Design

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Mass Framework: Boosting Multi‑Agent Design with Smarter Prompts & Topologies

dbaplus Community

Nov 27, 2016 · Big Data

Building and Optimizing Distributed Real‑Time Processing Systems with Machine Learning

This article explains how to apply machine learning to real‑time distributed processing, covering model basics, data collection, parameter learning, topology design, message flow control, and the Hurricane framework architecture.

Distributed SystemsHurricane frameworkReal-time Processing

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Building and Optimizing Distributed Real‑Time Processing Systems with Machine Learning