3 papers
cs.AI2026
Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers
Sajad Movahedi, Vera MilovanoviÄ, Shlomo Libo Feigin +5
Looped architectures provide an inductive bias toward learning step-by-step procedures for tasks that require compositional reasoning. The number of effective layers reached by loo…
cs.LG2026
How does the optimizer implicitly bias the model merging loss landscape?
Chenxiang Zhang, Alexander Theus, Damien Teney +3
Model merging combines independent solutions with different capabilities into a single one while maintaining the same inference cost. Two popular approaches are linear interpolatio…
cs.LG2025
Generalized Linear Mode Connectivity for Transformers
Alexander Theus, Alessandro Cabodi, Sotiris Anagnostidis +3
Understanding the geometry of neural network loss landscapes is a central question in deep learning, with implications for generalization and optimization. A striking phenomenon is…