cs.MA · 2026-06-17 · No. 26
Multiagent Systems, 2026-06-17.
2 new papers in cs.MA. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
2 entries-
01
A Neuro-Symbolic Approach to Strategy Synthesis for Strategic Logics
Marco Aruta, Vadim Malvone, Aniello Murano, Domenico Parente, Luca Rizzuti
cs.MA · cs.AI
Reasoning about what agents can achieve through strategic interaction is a core challenge in Multi-Agent Systems (MAS). Logics for strategic ability, such as ATL, provide rigorous methods, but their adoption is often hindered by the computational cost of strategy synthesis. We introduce a neuro-symbolic framework that integrates large language models (LLMs) into the model-checking pipeline for MAS. The LLM acts as a strategy-generation...
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02
Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization
Aueaphum Aueawatthanaphisut, Badri Raj Lamichhane
cs.MA · cs.AI · cs.DB · cs.SE
Big-Data-as-a-Service (BDaaS) platforms require re liable automation across data ingestion, cleaning, feature engi neering, model development, deployment, and post-deployment monitoring. However, existing LLM-based data science agents and AutoML systems mainly focus on isolated workflow stages, leaving limited support for lifecycle-level orchestration, artifact governance, human oversight, and drift-aware adaptation. This paper proposes a...
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