activity
20242026
most citedHumanity's Last Exam

23 citations · 39 across the 20 of their papers we have counts for

collaborators
Showing 2025 · cs.LGShow all

5 papers · 2 filters

cs.LG2025

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…

cs.LG2025

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…

cs.LG2025★ 1 cited

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…

cs.LG2025★ 3 cited

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…

cs.LG2025★ 23 cited

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…