arXiv:2512.03476v3 Announce Type: replace-cross Abstract: Progress in computational science depends on complex numerical workflows that must faithfully encode physical laws, yet translating conceptual insight into reliable code remains a major bottleneck. Although large language models can generate isolated code fragments, they lack the structured reasoning required to design, verify, and iteratively refine complete scientific pipelines. Here we introduce ATHENA, an agentic framework explicitly designed to emulate scientific research modeled as a knowledge-driven contextual bandit process. Its
The increasing sophistication of large language models is enabling the development of more complex agentic frameworks for scientific research, moving beyond isolated code generation to structured reasoning.
This development represents a significant step towards autonomous scientific discovery and could dramatically accelerate progress in computational science and engineering.
The process of translating conceptual scientific insight into reliable, complex numerical workflows could become significantly automated and more efficient, reducing human bottleneck.
- · Computational Scientists
- · AI/ML Research & Development
- · Engineering Industries
- · High-Performance Computing
- · Manual Code Developers
- · Traditional Numerical Algorithm Design Process
- · Early-stage AI Code Generation Tools
ATHENA directly enhances the ability to design, verify, and refine complex numerical simulations autonomously.
This could lead to accelerated breakthroughs in fields requiring advanced computational modeling, such as materials science, drug discovery, and climate modeling.
The widespread adoption of such agentic systems might redefine the role of human researchers, shifting focus from code generation to higher-level problem formulation and interpretation of AI-driven results.
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Read at arXiv cs.AI