mlsanityData to 7 Oct 2026

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

arXiv 2603.03233 ↗First posted 3 Mar 2026cs.AIcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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 uncertainty inherent to scientific tasks. LCP also streamlines human-AI collaboration by translating non-expert prompts into domain-specific requirements, bypassing the need for manual prompt engineering by practitioners without coding backgrounds. Benchmark evaluations demonstrate LCP's effectiveness in generating robust code while minimizing error propagation. The proposed platform is also tested on an Earth Science cross-disciplinary task and demonstrates strong reliability, outperforming competing models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 34 of its 43 references that are in the map. First posted on arXiv 3 Mar 2026.

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34 of 43 references.

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Same direction, newest first

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Other references · 31

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  10. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  11. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  12. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
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  2. 2023Teaching Large Language Models to Self-Debugcited by 963
  3. 2021Measuring Coding Challenge Competence With APPScited by 879
  4. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
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  6. 2025The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Searchcited by 375
  7. 2023Self-collaboration Code Generation via ChatGPTcited by 255
  8. 2024ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discoverycited by 208
  9. 2023Is Self-Repair a Silver Bullet for Code Generation?cited by 191
  10. 2024MapCoder: Multi-Agent Code Generation for Competitive Problem Solvingcited by 177
  11. 2024SciCode: A Research Coding Benchmark Curated by Scientistscited by 172
  12. 2023Structured Chain-of-Thought Prompting for Code Generationcited by 136
  13. 2023Self-planning Code Generation with Large Language Modelscited by 121
  14. 2023Self-Edit: Fault-Aware Code Editor for Code Generationcited by 110
  15. 2024Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineeringcited by 100
  16. 2023CodeScore: Evaluating Code Generation by Learning Code Executioncited by 66
  17. 2025CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generationcited by 33
  18. 2024LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesiscited by 13
  19. 2025Agent4S: The Transformation of Research Paradigms from the Perspective of Large Language Modelscited by 2

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