Relational Rank Geometry in Transformers: Detecting and Steering Hidden-State Relation Frames
arXiv:2605.29634v1 Announce Type: new Abstract: Transformer hidden states are often interpreted through local or low-order objects: neurons, sparse features, attention heads, residual-stream directions, or activation patches. This paper studies a complementary object: the rank-indexed geometry of relations among token tuples. I use Plucker sign entropy to test whether r-argument relations leave arity-matched orientation signatures in hidden-state space. Across Llama-family 8B, 70B, and 405B checkpoints, true relation tuples show stronger orientation-sign consistency at the expected rank k=r fo
This research emerges as the AI community seeks deeper interpretability and mechanistic understanding of large language models, driven by both commercial and safety concerns.
Understanding how Transformers encode relational information at a geometric level can unlock more robust, steerable, and potentially more efficient AI models.
This provides a new analytical lens, moving beyond individual components to examine higher-order relational geometries within Transformer hidden states, potentially leading to novel architectural improvements or fine-tuning techniques.
- · AI researchers
- · Large language model developers
- · AI interpretability tools
- · Black box AI solutions
- · Developers reliant solely on local feature analysis
Improved understanding of Transformer internal workings.
Development of new methods to control and optimize relational understanding in LLMs.
Enhanced AI safety and alignment through precise steering of complex conceptual relationships.
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Read at arXiv cs.LG