1 citations · 1 across the 4 of their papers we have counts for
5 papers
Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders
Giovanni Marraffini, Victoria Shevchenko, Carlo Alberto Barbano +1
Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise. Representations learned from large unlabelled corp…
The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail
Giovanni Marraffini, Gabriel Mahuas, Trinidad Borrell +2
Brain foundation models (BFMs) are self-supervised Transformers pretrained on fMRI data. We posit that these models should capture each subject's cognitive performance from their f…
Do Large Language Models Show Biases in Causal Learning? Insights from Contingency Judgment
María Victoria Carro, Denise Alejandra Mester, Francisca Gauna Selasco +4
Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process…
Are Optimal Algorithms Still Optimal? Rethinking Sorting in LLM-Based Pairwise Ranking with Batching and Caching
Juan Wisznia, Cecilia Bolaños, Juan Tollo +4
We introduce a novel framework for analyzing sorting algorithms in pairwise ranking prompting (PRP), re-centering the cost model around LLM inferences rather than traditional pairw…
The Greatest Good Benchmark: Measuring LLMs' Alignment with Utilitarian Moral Dilemmas
Giovanni Franco Gabriel Marraffini, Andrés Cotton, Noe Fabian Hsueh +3
The question of how to make decisions that maximise the well-being of all persons is very relevant to design language models that are beneficial to humanity and free from harm. We…