1 citations · 1 across the 3 of their papers we have counts for
3 papers
hep-th2026
Towards Worst-Case Guarantees with Scale-Aware Interpretability
Lauren Greenspan, David Berman, Aryeh Brill +9
Neural networks organize information according to the hierarchical, multi-scale structure of natural data. Methods to interpret model internals should be similarly scale-aware, exp…
cs.LG2025
Exact Learning Dynamics of In-Context Learning in Linear Transformers and Its Application to Non-Linear Transformers
Nischal Mainali, Lucas Teixeira
Transformer models exhibit remarkable in-context learning (ICL), adapting to novel tasks from examples within their context, yet the underlying mechanisms remain largely mysterious…
cs.LG2024★ 1 cited
Transformers represent belief state geometry in their residual stream
Adam S. Shai, Sarah E. Marzen, Lucas Teixeira +2
What computational structure are we building into large language models when we train them on next-token prediction? Here, we present evidence that this structure is given by the m…