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cs.AI2026
Prototype Transformer: Towards Language Model Architectures Interpretable by Design
Yordan Yordanov, Matteo Forasassi, Bayar Menzat +6
While state-of-the-art language models (LMs) surpass most humans in certain domains, their reasoning remains largely opaque, reducing trust and increasing the risk of deception and…
cs.LG2026
Faster Predictive Coding Networks via Better Initialization
Luca Pinchetti, Simon Frieder, Thomas Lukasiewicz +1
Research aimed at scaling up neuroscience inspired learning algorithms for neural networks is accelerating. Recently, a key research area has been the study of energy-based learnin…