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PINNForge is an LLM-driven evolutionary framework for automating the design of Physics-Informed Neural Networks (PINNs) for solving partial differential equations (PDEs), achieving state-of-the-art performance through execution feedback.
EvoRank is an LLM-guided evolutionary system for discovering multi-objective learning-to-rank pipelines in e-commerce, which demonstrates transferable improvements over baselines and includes auditing tools.
This paper proposes candidate-fate accounting, a framework for auditing automated machine learning pipeline searches in sensor diagnostics to enhance transparency by tracking all candidates and their outcomes.
This paper systematically evaluates five train-test splitting strategies for AutoML, showing that geometry-based methods are less effective than random/stratified splitting in preserving distributional similarity, and proposes an Optimised-Distribution method that achieves 89% similarity.
The paper extends the LLaMEA framework to automatically design multi-objective Bayesian optimization algorithms using large language models as mutation and crossover operators within evolutionary strategies, achieving state-of-the-art accuracy with significantly lower computational cost on synthetic and real-world problems.
Introduces AgentNAS, a mechanism that uses an LLM to generate a seed architecture and decompose it into a slotted architecture, defining a task-specific search space for conventional NAS to explore, achieving state-of-the-art on 11 of 17 tasks.
MLEvolve is a self-evolving LLM-based multi-agent framework for automated ML algorithm discovery that extends tree search to Progressive MCGS with graph-based cross-branch information flow and retrospective memory. It achieves state-of-the-art performance on MLE-Bench and outperforms AlphaEvolve on mathematical algorithm optimization tasks.