Emergent Causal-Geometric Dynamics Across Depth in Large Language Models

ArXi:2602.04931v2 Announce Type: replace-cross Geometric analyses of large language model (LLM) representations reveal structured variation across depth but remain fundamentally correlational with respect to token prediction formation. Meanwhile, causal interventions expose depth-dependent efficacy profiles without a unifying account of their representational dynamics. A complete account of LLM function requires explaining how representational structure evolves across depth to causally produce predictions.