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cs.LG2026
At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization
Praneet Suresh, Jack Stanley, Sonia Joseph +2
Pre-trained transformers have demonstrated remarkable generalization abilities, at times extending beyond the scope of their training data. Yet, real-world deployments often face u…
cs.CL2026
HRM-Text: Efficient Pretraining Beyond Scaling
Guan Wang, Changling Liu, Chenyu Wang +6
The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foundational research. In contra…