arXiv:2602.04931v2 Announce Type: replace Abstract: 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. We synthesize these perspectives by combining geometric analysis with mechanistic i
This research provides a deeper, mechanistic understanding of how large language models function internally, moving beyond correlational analyses to causal dynamics.
Understanding the causal-geometric dynamics within LLMs is crucial for developing more robust, interpretable, and controllable AI, impacting future model design and application.
The focus is shifting from simply observing LLM behavior to dissecting and engineering its internal causal mechanisms, leading to more predictable AI development.
- · AI researchers
- · Deep learning engineers
- · AI safety researchers
- · AI development platforms
- · Black-box AI approaches
Improved interpretability and debugging for complex large language models.
Accelerated development of more efficient and less resource-intensive AI architectures.
New paradigms for AI training and optimization based on causal rather than purely statistical relationships.
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Read at arXiv cs.LG