The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability
Reliable deployment of language models requires two capabilities that appear distinct but share a common geometric foundation: predicting whether a model will accept targeted behavioral control, and detecting when its internal structure degrades. We show that geometric stability, the consistency of a representation's pairwise distance structure, addresses both. Supervised Shesha variants that measure task-aligned geometric stability predict linear steerability with near-perfect accuracy ($ρ= 0.89$-$0.97$) across 35-69 embedding models and three NLP tasks, capturing unique variance beyond class separability (partial $ρ= 0.62$-$0.76$). A critical dissociation emerges: unsupervised stability...
| Authors | Prashant C. Raju |
|---|---|
| Primary category | cs.LG |
| Published | 20 April 2026 |
| Last updated at source | 25 April 2026 |
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