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cs.LG2026
Large Language Models Develop Belief State Geometry In-Context
Daniel Balcells, Andrew Jun Lee, Chirag Rastogi +3
Large language models (LLMs) trained on next-token prediction exhibit remarkable in-context learning (ICL) abilities, yet the representations that support ICL remain poorly underst…
cs.LG2026
Transformers learn factored representations
Adam Shai, Loren Amdahl-Culleton, Casper L. Christensen +6
Transformers pretrained via next token prediction learn to factor their world into parts, representing these factors in orthogonal subspaces of the residual stream. We formalize tw…