cs.MA · 2026-09-16 · No. 115

Multiagent Systems, 2026-09-16.

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

01 — The papers

4 entries
  1. 01

    Agentic Societies Need a Social Harness

    Tapan Chugh, Vidushi Singh, Krish Jain, Arvind Krishnamurthy, Ratul Mahajan

    cs.MA · cs.AI · cs.NI

    An agentic society is a collection of AI agents that coordinate autonomously across trust boundaries, on behalf of different principals whose objectives may only partially align. We show experimentally that in agentic societies even honest, competent agents often fail to reach satisfactory outcomes with existing harnesses and messaging primitives, and that faulty or malicious agents can stall collaboration, influence outcomes, and pursue...

    arxiv.org/abs/2609.17527 · PDF

  2. 02

    Decomposition Buys Integrity, Not Yield

    Rong He

    cs.MA · cs.AI · cs.DC

    Multi-agent systems split a task across a tree of agents and justify the split with folklore: smaller contexts, cleaner separation, parallelism. We ask what the split does to how much of what the leaves discover reaches the root. Model a decomposition as a tree in which an agent handed $b$ items keeps any one with probability $r(b)$. If $r(b)=1/b$, every tree delivers exactly one finding, for every task size and every shape; we verify this to...

    arxiv.org/abs/2609.17464 · PDF

  3. 03

    Mo' Models, Mo' Problems: How to best select model pools when designing Multi-Agent Systems

    Sara Vera Marjanović, Jiacheng Xu, Aleksandr Laptev, Grigor Nalbandyan, Erik Arakelyan, Evelina Bakhaturina

    cs.MA · cs.AI

    Multi-agent Systems (MAS) combine multiple model outputs to solve complex reasoning tasks. However, despite rapid growth of available open-source models, there is limited research on how to select optimal model candidates out of this massive pool. We systematically evaluate 8 model selection strategies (including model size, accuracy and answer diversity) across before-generation (routing) and after-generation (majority-voting,...

    arxiv.org/abs/2609.17306 · PDF

  4. 04

    Multi-Agent Learning with Cooperation-Driven Optimization Dynamics

    Jarod Ketcha Kouakep, Sreyvi UANN, Timoteo Carletti

    cs.MA · cs.LG

    Multilayer Artificial Neural Networks trained via backpropagation are the basic blocks of many, more complex, classification algorithms. Their strength lies in the possibility of realizing, with arbitrary precision, any function. This result comes at the cost of the large number of involved parameters to be optimized. In this work, we propose a mechanism for cooperation, i.e., information exchange among several artificial neural networks,...

    arxiv.org/abs/2609.16917 · PDF

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