[2512.03476] ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms

Computer Science > Machine Learning arXiv:2512.03476 (cs) [Submitted on 3 Dec 2025 (v1), last revised 8 May 2026 (this version, v2)] Title:ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms Authors:Juan Diego Toscano, Daniel T. Chen, George Em Karniadakis View a PDF of the paper titled ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms, by Juan Diego Toscano and 2 other authors View PDF HTML (experimental) Abstract:Bridging the gap between theoretical conceptualization and computational implementation is a major bottleneck in Scientific Computing (SciC) and Scientific Machine Learning (SciML). We introduce ATHENA (Agentic Team for Hierarchical Evolutionary Numerical Algorithms), an agentic framework designed as an Autonomous Lab to manage the end-to-end computational research lifecycle. Its core is the HENA loop, a knowledge-driven diagnostic process framed as a Contextual Bandit problem. Acting as an online learner, the system analyzes prior trials to select structural `actions' ($A_n$) from combinatorial spaces guided by expert blueprints (e.g., Universal Approximation, Physics-Informed constraints). These actions are translated into executable code ($S_n$) to generate scientific rewards ($R_n$). ATHENA transcends standard automation: in SciC, it autonomously identifies mathematical symmetries for exact analytical solutions or derives stable numerical solvers where foundation models fail. In SciML, it performs deep diagnosis to t...