[2603.03233] AI-for-Science Low-code Platform with Bayesian Adversarial Multi-Agent Framework

Computer Science > Artificial Intelligence arXiv:2603.03233 (cs) [Submitted on 3 Mar 2026] Title:AI-for-Science Low-code Platform with Bayesian Adversarial Multi-Agent Framework Authors:Zihang Zeng, Jiaquan Zhang, Pengze Li, Yuan Qi, Xi Chen View a PDF of the paper titled AI-for-Science Low-code Platform with Bayesian Adversarial Multi-Agent Framework, by Zihang Zeng and 4 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) demonstrate potentials for automating scientific code generation but face challenges in reliability, error propagation in multi-agent workflows, and evaluation in domains with ill-defined success metrics. We present a Bayesian adversarial multi-agent framework specifically designed for AI for Science (AI4S) tasks in the form of a Low-code Platform (LCP). Three LLM-based agents are coordinated under the Bayesian framework: a Task Manager that structures user inputs into actionable plans and adaptive test cases, a Code Generator that produces candidate solutions, and an Evaluator providing comprehensive feedback. The framework employs an adversarial loop where the Task Manager iteratively refines test cases to challenge the Code Generator, while prompt distributions are dynamically updated using Bayesian principles by integrating code quality metrics: functional correctness, structural alignment, and static analysis. This co-optimization of tests and code reduces dependence on LLM reliability and addresses evaluation uncertain...