Author

Paulo Tabuada

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#machine learning Preprint Sep 2026

Preventing Model Collapse: A Fisher-Rao Perspective on the Dynamics of Training with Synthetic Data

Large Language Models (LLMs) are now routinely trained using synthetic data, since high-quality human data has been exhausted by the ever increasing needs of larger and larger models. However, recursive training on synthetic data frequently induces model collapse, a degenerative feedback loop where models progressively...

Matteo Marchi, João Pedro Silvestre, Bahman Gharesifard et al. · 0 citations

#artificial intelligence Preprint Sep 2026

The Attention Within: Consensus Dynamics in Selective State Space Models

Selective state space models (SSMs) have recently emerged as a compelling alternative to transformers, combining competitive performance with substantially improved inference efficiency. At each SSM layer, a sequence of hidden states are propagated by a recurrence, mixing information of different tokens. Despite using...

João Pedro Silvestre, Álvaro Rodríguez Abella, Paulo Tabuada · 1 citation