2 papers
cs.CL2026
Scaling Inherently Interpretable Language Models
Guide Labs Team, Andreas Madsen, Aya Abdelsalam Ismail +7
Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explained after the fact, with methods whose reliability is difficult…
cs.LG2026
Physics-guided spatiotemporal neural models for fuel density prediction
Tolga Caglar, Jaynil Jaiswal, Saqib Azim +3
This paper presents a physics-guided machine learning (PGML) framework for fuel density prediction, integrating physics constraints and domain knowledge into deep learning models t…