cs.MA · 2026-06-18 · No. 27

Multiagent Systems, 2026-06-18.

3 new papers in cs.MA. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents

    Anoushka Vyas, Aarushi Dhanuka, Sina Khoshfetrat Pakazad, Henrik Ohlsson

    cs.MA · cs.AI · cs.DB

    Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterprise data. We present Data Intelligence Agents (DIA), a system of three agents (Data Interpreter, Schema Creator, and Query Generator) that compresses this workflow by treating autonomous coding agents (ACAs) as a first-class abstraction: rather than emitting text,...

    arxiv.org/abs/2606.19319 · PDF

  2. 02

    A Technical Taxonomy of LLM Agent Communication Protocols

    Linus Sander, Habtom Kahsay Gidey, Alexander Lenz, Alois Knoll

    cs.MA · cs.AI · cs.NI

    As large language models (LLMs) advance and multi-agent systems aim to overcome the limits of standalone agents, robust communication protocols are becoming essential infrastructure for distributed agent networks. Nonetheless, the fragmented protocol landscape presents a significant interoperability challenge. This study develops a technical taxonomy to classify and analyze LLM agent communication protocols. Following an established iterative...

    arxiv.org/abs/2606.19135 · PDF

  3. 03

    Skill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems

    Hehai Lin, Qi Yang, Chengwei Qin

    cs.MA · cs.AI · cs.LG

    Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks. However, existing methods face a dilemma between model capability and experience retention. Inference-time MAS leverages frozen frontier LLMs but repeats identical searches without learning from past experience. Conversely, Training-time MAS internalizes experience via gradient updates but is constrained by...

    arxiv.org/abs/2606.18837 · PDF

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