Anonymousblind/svs-subspace-validity-suite

Subspace Validity Suite (SVS)

Diagnostic toolkit for validating "visual directions" in Vision-Language Models.

Paper: "What PCA-Based Visual Directions in VLMs Actually Capture" (WACV 2027)

Installation

git clone https://huggingface.co/datasets/Anonymousblind/svs-subspace-validity-suite
cd svs-subspace-validity-suite
pip install .

Quick Start

from svs import SubspaceValiditySuite

svs = SubspaceValiditySuite()
report = svs.full_report(
    directions=your_directions,       # (k, d) numpy array
    h_visual=visual_hidden_states,    # list of (d,) arrays
    h_gibberish=gibberish_states,     # list of (d,) arrays
    h_factual=factual_states,         # optional
    h_math=math_states,               # optional
)
svs.print_report(report)

Repository Contents

• svs/ — Pip-installable toolkit (6 diagnostic tests)
• experiments/ — All experiment scripts (Colab-ready)
• checkpoints/ — Raw results for reviewer verification
• directions/ — Extracted subspace directions

Checkpoints

Load any checkpoint to verify paper numbers:

import json
with open("checkpoints/gibberish_test/statistical_summary.json") as f:
    stats = json.load(f)
for method, r in stats.items():
    print(f"{method}: Gib/Vis={r['gv_ratio']:.2f}, d={r['cohens_d']:.3f}")