---
license: mit
tags:
  - hallucination
  - vlm
  - evaluation
  - diagnostic
viewer: false
---

# 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

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

## Quick Start

```python
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:

```python
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}")
```