arXiv:2606.26749v1 Announce Type: new Abstract: Neural Collapse predicts that balanced one-hot classification pushes model representations to be equally far from each other; a symmetric configuration that depends only on the output label and ignores any semantic similarity in the inputs. This creates a puzzle: next-token prediction language models are trained predominantly (as context length increases) with one-hot labels: the same context is very unlikely to appear twice in training with different labels. However, they clearly learn latent structural features. That is, despite the one-hot tra
The paper, published in 2026, details a new theoretical understanding of how large language models learn semantic structure despite training methods that would seemingly prevent it, emerging as AI development continues to accelerate.
This research provides a deeper mechanistic understanding of how LLMs acquire and represent knowledge, which is critical for their future explainability, robust design, and advanced capabilities beyond current limits.
Our understanding of latent structural features in LLMs shifts from empirical observation to a theoretically grounded prediction, potentially enabling more targeted and efficient model architectures that explicitly leverage these 'geometry' principles.
- · AI researchers
- · LLM developers
- · Deep learning framework providers
- · Black-box AI critics (without new counter-arguments)
Improved interpretability tools for understanding LLM internal representations.
Development of new training paradigms that explicitly encourage desired semantic geometries.
More efficient and less 'brute-force' methods for training foundation models, reducing compute requirements.
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Read at arXiv cs.LG