23 citations · 39 across the 20 of their papers we have counts for
22 papers
Big-Big-SycoBench: A Benchmark to Detect Sycophancy Through Model Responses to Valid vs. Invalid Arguments
Marthe Ballon, Arne Vanhoyweghen, Seorin Kim +11
Sycophancy in large language models risks amplifying unsupported claims or beliefs across the knowledge ecosystem as these systems increasingly contribute to research synthesis, ev…
Shorter Reasoning, Earlier Answers? An Evaluation of Reasoning Interfaces
Francesca Carlon, Vincent Ginis, Andres Algaba
Large language models often reason at length before answering, increasing cost and latency. Prompts and trained settings can shorten this reasoning, but a shorter trace may only sh…
How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models
Andres Algaba, Francesca Carlon, Lynn Delcon +3
Large language models often show users a final response and a short reasoning summary while the full reasoning trace stays hidden. We introduce an observability ladder that holds e…
Geometric Configurations of Perturbed Jailbreak Prompts
Lynn Delcon, Andres Algaba, Vincent Ginis
Perturbation techniques that turn unsuccessful jailbreak prompts into successful ones are continuously evolving, constituting a major security threat to LLM safety. In this paper,…
Rebuttals Move Peer-Review Scores, but Initial-Review Structure Bounds the Movement
Mathieu Louis, Tibo Vanleke, Vincent Ginis +1
Author rebuttals are the main post-submission window in peer review, but their effect on reviewer scores remains hard to measure because score updates mix rebuttal content with ini…
Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods
Francesca Carlon, Brecht Verbeken, Brecht F. Verbeken +2
Large Language Models (LLMs) are increasingly used to guide research methodology, yet their default methodological tendencies under minimal prompting remain unclear. Here, we promp…
On the Spectral Region of 4-Cycle Stochastic Matrices
Brando Vagenende, Brecht Verbeken, Brecht F. Verbeken +2
We study the spectrum of 4-cycle row-stochastic matrices. For real eigenvalues the spectral region is [-1,1]. For nonreal eigenvalues a+ib we derive necessary conditions in terms o…
Scalable Classification of Course Information Sheets Using Large Language Models: A Reusable Institutional Method for Academic Quality Assurance
Brecht F. Verbeken, Brecht Verbeken, Joke Van den Broeck +5
Purpose: Higher education institutions face increasing pressure to audit course designs for generative AI (GenAI) integration. This paper presents an end-to-end method for using la…
Human-in-the-Loop LLM Grading for Handwritten Mathematics Assessments
Arne Vanhoyweghen, Vincent Holst, Melika Mobini +10
Providing timely and individualised feedback on handwritten student work is highly beneficial for learning but difficult to achieve at scale. This challenge has become more pressin…
Early Evidence of Vibe-Proving with Consumer LLMs: A Case Study on Spectral Region Characterization with ChatGPT-5.2 (Thinking)
Brecht Verbeken, Brecht F. Verbeken, Brando Vagenende +3
Large Language Models (LLMs) are increasingly used as scientific copilots, but evidence on their role in research-level mathematics remains limited, especially for workflows access…
Probing the Trajectories of Reasoning Traces in Large Language Models
Marthe Ballon, Brecht Verbeken, Brecht F. Verbeken +2
Large language models (LLMs) increasingly solve difficult problems by producing "reasoning traces" before emitting a final response. However, it remains unclear how accuracy and de…
Structurally Human, Semantically Biased: Detecting LLM-Generated References with Embeddings and GNNs
Melika Mobini, Vincent Holst, Floriano Tori +2
Large language models are increasingly used to curate bibliographies, raising the question: are their reference lists distinguishable from human ones? We build paired citation grap…
Benchmarks Saturate When The Model Gets Smarter Than The Judge
Marthe Ballon, Andres Algaba, Brecht Verbeken +2
Benchmarks are important tools to track progress in the development of Large Language Models (LLMs), yet inaccuracies in datasets and evaluation methods consistently undermine thei…
Estimating problem difficulty without ground truth using Large Language Model comparisons
Marthe Ballon, Andres Algaba, Brecht Verbeken +2
Recent advances in the finetuning of large language models (LLMs) have significantly improved their performance on established benchmarks, emphasizing the need for increasingly dif…
Lexical Hints of Accuracy in LLM Reasoning Chains
Arne Vanhoyweghen, Brecht Verbeken, Brecht F. Verbeken +2
Fine-tuning Large Language Models (LLMs) with reinforcement learning to produce an explicit Chain-of-Thought (CoT) before answering produces models that consistently raise overall…
Decision-centric fairness: Evaluation and optimization for resource allocation problems
Simon De Vos, Jente Van Belle, Andres Algaba +2
Data-driven decision support tools play an increasingly central role in decision-making across various domains. In this work, we focus on binary classification models for predictin…
How Deep Do Large Language Models Internalize Scientific Literature and Citation Practices?
Andres Algaba, Vincent Holst, Floriano Tori +5
The spread of scientific knowledge depends on how researchers discover and cite previous work. The adoption of large language models (LLMs) in the scientific research process intro…
Flexible Counterfactual Explanations with Generative Models
Stig Hellemans, Andres Algaba, Sam Verboven +1
Counterfactual explanations provide actionable insights to achieve desired outcomes by suggesting minimal changes to input features. However, existing methods rely on fixed sets of…
The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longer
Marthe Ballon, Andres Algaba, Vincent Ginis
Large language models have demonstrated remarkable progress in mathematical reasoning, leveraging chain-of-thought and reinforcement learning. However, many open questions remain r…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias
Andres Algaba, Carmen Mazijn, Vincent Holst +3
Citation practices are crucial in shaping the structure of scientific knowledge, yet they are often influenced by contemporary norms and biases. The emergence of Large Language Mod…
Dataset Artefacts are the Hidden Drivers of the Declining Disruptiveness in Science
Vincent Holst, Andres Algaba, Floriano Tori +2
Park et al. [1] reported a decline in the disruptiveness of scientific and technological knowledge over time. Their main finding is based on the computation of CD indices, a measur…