59 citations · 144 across the 61 of their papers we have counts for
70 papers
Detecting and Repairing Hallucinations in Retrieval-Augmented Generation
Sai Krishna Reddy Mulakkayala, Niki van Stein, Aske Plaat
Language models increasingly answer questions by consulting retrieved documents rather than memory alone, a design now common in search assistants and enterprise knowledge tools. G…
Evolving Executable Pipeline Programs for AutoML with Language Models
Sofoklis Kitharidis, Cor J. Veenman, Jan N. van Rijn +2
Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, learners, and hyper-parameters specified in advance: they can select and…
LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms
Georgios Laskaris, Reuben Brasher, Niki van Stein +3
Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent a…
3DMorph: Single-Image-Guided Local 3D Shape Editing and Morphing
Tobias Preintner, Yunfei Deng, Phillip Müller +5
Despite recent progress in 3D generation, intuitive editing of existing shapes remains limited. Unlike images, which benefit from well-established inpainting tools, general 3D obje…
Every Component Is a Lookup: One Linear Graph for Interaction, Composition and Attribution
Po-Kai Chen, Aske Plaat, Niki van Stein
Interpretability methods for transformers are typically built around separate questions: which components interact, how information routes to the output, and which input tokens con…
Block-Bench: A Framework for Controllable and Transparent Discrete Optimization Benchmarking
Furong Ye, Frank Neumann, Thomas Bäck +1
We present a novel approach for constructing discrete optimization benchmarks that enables fine-grained control over problem properties, and such benchmarks can facilitate analyzin…
From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors
Qi Huang, Furong Ye, Ananta Shahane +2
Large Language Models (LLMs) have already been widely adopted for automated algorithm design, demonstrating strong abilities in generating and evolving algorithms across various fi…
Structural bias in multi-objective optimisation
Jakub Kudela, Niki van Stein, Thomas Bäck +1
Structural bias (SB) refers to systematic preferences of an optimisation algorithm for particular regions of the search space that arise independently of the objective function. Wh…
Assessing Reproducibility in Evolutionary Computation: A Case Study using Human- and LLM-based Assessment
Francesca Da Ros, Tarik Začiragić, Aske Plaat +2
Reproducibility is an important requirement in evolutionary computation, where results largely depend on computational experiments. In practice, reproducibility relies on how algor…
Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
Haoran Yin, Shuaiqun Pan, Zhao Wei +5
The advent of Large Language Models (LLMs) has opened new frontiers in automated algorithm design, giving rise to numerous powerful methods. However, these approaches retain critic…
Investigating the Interplay of Parameterization and Optimizer in Gradient-Free Topology Optimization: A Cantilever Beam Case Study
Jelle Westra, Iván Olarte Rodríguez, Niki van Stein +2
Gradient-free black-box optimization (BBO) is widely used in engineering design and provides a flexible framework for topology optimization (TO), enabling the discovery of high-per…
Lens-descriptor guided evolutionary algorithm for optimization of complex optical systems with glass choice
Kirill Antonov, Teus Tukker, Tiago Botari +3
Designing high-performance optical lenses entails exploring a high-dimensional, tightly constrained space of surface curvatures, glass choices, element thicknesses, and spacings. I…
LLaMEA-SAGE: Guiding Automated Algorithm Design with Structural Feedback from Explainable AI
Niki van Stein, Anna V. Kononova, Lars Kotthoff +1
Large language models have enabled automated algorithm design (AAD) by generating optimization algorithms directly from natural-language prompts. While evolutionary frameworks such…
LLM Driven Design of Continuous Optimization Problems with Controllable High-level Properties
Urban Skvorc, Niki van Stein, Moritz Seiler +3
Benchmarking in continuous black-box optimisation is hindered by the limited structural diversity of existing test suites such as BBOB. We explore whether large language models emb…
State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models
Annie Wong, Aske Plaat, Thomas Bäck +2
As large language models (LLMs) move from static reasoning tasks toward dynamic environments, their success depends on the ability to navigate and respond to an environment that ch…
Mechanistic Interpretability for Transformer-based Time Series Classification
Matīss Kalnāre, Sofoklis Kitharidis, Thomas Bäck +1
Transformer-based models have become state-of-the-art tools in various machine learning tasks, including time series classification, yet their complexity makes understanding their…
From Performance to Understanding: A Vision for Explainable Automated Algorithm Design
Niki van Stein, Anna V. Kononova, Thomas Bäck
Automated algorithm design is entering a new phase: Large Language Models can now generate full optimisation (meta)heuristics, explore vast design spaces and adapt through iterativ…
Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
Anna V. Kononova, Niki van Stein, Olaf Mersmann +14
Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC iso…
Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space
Sofoklis Kitharidis, Cor J. Veenman, Thomas Bäck +1
In the context of algorithmic decision-making, fair machine learning methods often yield multiple models that balance predictive fairness and performance in varying degrees. This d…
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Zhong Li, Qi Huang, Yuxuan Zhu +4
We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generat…
EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
Tobias Preintner, Weixuan Yuan, Adrian König +3
Combining large language models with evolutionary computation algorithms represents a promising research direction leveraging the remarkable generative and in-context learning capa…
Automated Algorithm Design for Auto-Tuning Optimizers
Floris-Jan Willemsen, Niki van Stein, Ben van Werkhoven
Automatic performance tuning (auto-tuning) is essential for optimizing high-performance applications, where vast and irregular search spaces make manual exploration infeasible. Whi…
CoComposer: LLM Multi-agent Collaborative Music Composition
Peiwen Xing, Aske Plaat, Niki van Stein
Existing AI Music composition tools are limited in generation duration, musical quality, and controllability. We introduce CoComposer, a multi-agent system that consists of five co…
How does Chain of Thought Think? Mechanistic Interpretability of Chain-of-Thought Reasoning with Sparse Autoencoding
Xi Chen, Aske Plaat, Niki van Stein
Chain-of-thought (CoT) prompting boosts Large Language Models accuracy on multi-step tasks, yet whether the generated "thoughts" reflect the true internal reasoning process is unre…