# Swap Analysis: Minimal Pair Probing for Spatial Representations

This repository contains `swap_analysis.py`, a comprehensive pipeline for evaluating and visualizing how Vision-Language Models (VLMs) represent spatial relationships. 

The script works by creating **minimal pairs** from spatial questions. It takes an original query and swaps the target and reference objects, measuring how the model's hidden states and predictions change in response.

**Example Minimal Pair:**
* *Original:* "Is A to the left or right of B?" ➔ Expected: `left`
* *Swapped:* "Is B to the left or right of A?" ➔ Expected: `right`

## 🌟 Key Features & Analyses

The script runs inference, extracts hidden states across model layers, and performs the following analyses:
1.  **Difference Vectors (Deltas):** Computes $\Delta = \text{feature(swapped)} - \text{feature(original)}$.
2.  **Within-Category Delta Consistency:** Measures if all swaps of a specific category (e.g., left $\rightarrow$ right) point in the same direction in the latent space.
3.  **Sign-Corrected Group Consistency:** Aligns opposite categories by flipping their vectors to check global axis consistency.
4.  **Cross-Group Delta Alignment:** Compares orthogonal dimensions (e.g., $\Delta_{vertical}$ vs. $\Delta_{distance}$) to detect perspective bias.
5.  **Similarity Heatmaps:** Generates $6 \times 6$ cross-category cosine similarity matrices based on mean deltas.
6.  **Prediction Statistics:** Tracks and visualizes original, swapped, and "both-correct" accuracy trajectories across different data scales.
7.  **PCA Visualizations:** Plots 2D and 3D PCA projections of per-sample embeddings and delta vectors.
8.  **Robust Filtering:** Isolates analyses to "both-correct" samples to ensure representations are tied to successful spatial understanding.

## 🤖 Supported Models

The script natively supports hidden-state extraction for multiple model architectures, segmented into legacy base models, new large models, and merge-only configurations (for cross-scale plotting).

* **Legacy (Qwen2.5-VL-3B scale experiments):**
    * `molmo` (Molmo-7B-O variants)
    * `nvila` (NVILA-Lite-2B variants)
    * `nvila_synthetic` (NVILA mixed-data variants)
    * `qwen` (Qwen2.5-VL-3B variants)
* **New Large Models:**
    * `molmo_big` (Molmo2-8B)
    * `qwen_big` (Qwen3-VL-32B-Instruct)
    * `qwen_super` (Qwen3-VL-235B-A22B-Instruct)
    * `big_trio` (Molmo2-8B + RoboRefer + Qwen3-VL-32B)
* **Merge-Only (Requires `--merge`):**
    * `molmo_all` (Combines `molmo` and `molmo_big` outputs)
    * `qwen_all` (Combines `qwen` and `qwen_big` outputs)
    * `nvila_synth_compare` (Compares NVILA baselines against synthetic-mix checkpoints)

## 🚀 Usage

### 1. Standard Inference
Extract features and generate single-scale analyses for a specific model family.

```bash
# Legacy model evaluation
python swap_analysis.py --model_type qwen

# New large model evaluation
python swap_analysis.py --model_type qwen_big

```

### 2. Merge Mode (Cross-Scale Analysis)

Once you have run inference on individual scales or models, use the `--merge` flag to aggregate the JSON/NPZ data and generate cross-scale trajectory plots.

```bash
# Combine qwen base scales with qwen_big (Qwen3-32B) results
python swap_analysis.py --model_type qwen_all --merge

```

### Command Line Arguments

| Argument | Description | Default |
| --- | --- | --- |
| `--model_type` | **(Required)** The model architecture/family to run. | None |
| `--data_path` | Path to the `EmbSpatial-Bench.tsv` dataset. | `/data/.../EmbSpatial-Bench.tsv` |
| `--scales` | Specific scales to process (e.g., `vanilla`, `80k`). If omitted, runs all default scales for the model. | *Model-dependent* |
| `--output_dir` | Base directory for saving CSVs, JSONs, NPZs, and plots. | `/data/.../results` |
| `--merge` | Generates cross-scale/cross-model comparison plots from saved data instead of running inference. | `False` |
| `--question-type` | `mcq` for A/B letter answers or `short` for single-word generation. | `mcq` |
| `--max-samples-per-category` | Limit samples per spatial category for faster debugging/runs. | `200` |
| `--no-filtering` | Disables filtering of 'Unknown' reference objects in distance queries. | `False` |

## 📂 Output Structure

Results are saved in your specified `--output_dir` under a subfolder named after the `--model_type`.

```text
results/{model_type}/
├── csv/          # Delta heatmaps, prediction rows, and cross-scale summaries
├── json/         # Consistency metrics, alignments, and validity checks per scale
├── npz/          # Raw hidden states and delta vectors for offline analysis
└── plots/        # Visualizations
    ├── all/               # Unfiltered analysis plots (PCA, bar charts, heatmaps)
    ├── both_correct/      # Strict analysis plots (only pairs where model got both right)
    └── accuracy/          # Grouped and per-category accuracy bar/line charts

```

## 🛠 Prerequisites

* `torch`
* `transformers`
* `pandas`, `numpy`, `scikit-learn`
* `matplotlib`, `seaborn`, `tqdm`
* `Pillow`
* *Model-specific libraries:* `qwen_vl_utils`, `llava`, `olmo` (depending on the models being tested).