From generative AI to the brain: five takeaways

2025-11-20 · Claudius Gros arxiv

The big strides seen in generative AI are not based on somewhat obscure algorithms, but due to clearly defined generative principles. The resulting concrete implementations have proven themselves in large numbers of applications. We suggest that it is imperative to thoroughly investigate which of these generative principles may be operative also in the brain, and hence relevant for cognitive neuroscience. In addition, ML research led to a range of interesting characterizations of neural information processing systems. We discuss five examples, the shortcomings of world modelling, the generation of thought processes, attention, neural scaling laws, and quantization, that illustrate how much neuroscience could potentially learn from ML research.

📄 PDF Abstract BibTeX arXiv:2511.16432

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EvalGIM: A Library for Evaluating Generative Image Models

2024-12-13 · Melissa Hall, Oscar Mañas, Reyhane Askari-Hemmat, Mark Ibrahim 외

As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound, there are few unified benchmarking lib…

Benchmarking Diversity

AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles

2022-10-01 · SMM4H (COLING) 2022 10 · Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni, Enrico Santus 외

This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task. We explored the limits of Transformer based models on text classification, entity extraction and entity normalization, tackl…

Ensemble Learning text-classification Text Classification

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model

2024-02-12 · Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle 외

We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative model (up to logarithmic factors), reso…

Distributional Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem

2026-06-27 · Yutian Tang, Yuming Zhou, Huaming Chen arxiv

Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs. LL…

Natural Language Understanding

Forecasting Future Anatomies: Longitudinal Brain Mri-to-Mri Prediction

2025-11-04 · Ali Farki, Elaheh Moradi, Deepika Koundal, Jussi Tohka arxiv

Predicting future brain state from a baseline magnetic resonance image (MRI) is a central challenge in neuroimaging and has important implications for studying neurodegenerative diseases such as Alzheimer's disease (AD).…