Paper shows LLM geometry stems from data statistics, not complex dynamics
burny_tech · x · 2026-08-28
A new paper by Karkada et al. solves a major interpretability puzzle regarding LLM representations. It proves that the geometric shapes formed internally (e.g., spirals for timelines) are not results of deep, complex learning dynamics, but are forced by basic data statistics—specifically translation symmetry—in the training corpus.
More from Research
- MIT's Loop Closure Grasping Balances Strength, Gentleness, and Versatility — lukas_m_ziegler · 2026-08-28
- Training AI to Paint with Code via Reinforcement Learning — CatAstro_Piyush · 2026-08-28
- DeepMind researcher proposes Normative Alignment for principled agentic safety — verena_rieser · 2026-08-28
- Q-Planning enables recursive self-improvement for robot policies: 25% to 80% success — animesh_garg · 2026-08-28
- Open Source Animacy: Maps Any Human Video to Robot Motion Without Retraining — dee_hw · 2026-08-28
- Google Introduces AgentHands: Gesture-Enabled XR Conversational Agents — CurieuxExplorer · 2026-08-28