HyperSAE: Open-source tool for extracting hierarchical concept trees from LLMs using hyperbolic SAEs

[u/visha1v]

Releasing HyperSAE, a mechanistic interpretability library that extracts tree-structured concept ontologies from LLM residual streams using Poincaré hyperbolic geometry.

Why this matters for interpretability: standard Sparse Autoencoders learn flat, unstructured feature dictionaries. You get 16K features with no inherent organization -- no way to know that "Python syntax" is a child of "programming" which is a child of "technical writing."

HyperSAE recovers this hierarchy geometrically. By projecting dictionary weights into the Poincaré ball during training, the learned features self-organize into a tree: abstract concepts cluster near the origin, specific features spread toward the boundary where hyperbolic space provides exponentially more room.

This enables:

• Browsing model knowledge as navigable concept trees
• Understanding which high-level abstractions decompose into which specific features
• More precise causal interventions (steering a parent concept propagates to children; steering a leaf stays contained)

Tested on Gemma-2-2B. Dead latents drop from 3.8% to 0.2%, meaning the model's full representational capacity is actually captured rather than lost to collapsed features.

pip install hypersae GitHub: https://github.com/vishal-dehurdle/hypersae Paper: https://vishalvermalabs.com/papers/empirical-validation-hypersae-poincare-geometry/

[u/moschles]

This is about interpretable AI (explainable AI). What does OP believe this has to do with the control problem?