README.md

---
license: apache-2.0
task_categories:
  - visual-question-answering
  - image-classification
language:
  - en
  - zh
tags:
  - benchmark
  - visual-reasoning
  - puzzle
  - multimodal
  - evaluation
  - generative
  - discriminative
  - deterministic
size_categories:
  - 100K<n<1M
pretty_name: "TACIT Benchmark"
configs:
  - config_name: default
    data_files:
      - split: test
        path: "task_*/**/*.png"
---

# TACIT Benchmark v0.1.0

**Transformation-Aware Capturing of Implicit Thought**

A programmatic visual reasoning benchmark for evaluating generative and discriminative capabilities of multimodal models across 10 tasks and 6 reasoning domains.

**Author:** [Daniel Nobrega Medeiros](https://www.linkedin.com/in/daniel-nobrega-187272124)
&nbsp;|&nbsp; [arXiv paper](https://arxiv.org/abs/2603.00206)
&nbsp;|&nbsp; [GitHub](https://github.com/danielxmed/tacit-benchmark)

## Overview

TACIT presents visual puzzles that require genuine spatial, logical, and structural reasoning — not pattern matching on text. Each puzzle is generated programmatically with deterministic seeding, ensuring full reproducibility. Evaluation is **programmatic** (no LLM-as-judge): solutions are verified through computer vision algorithms (pixel sampling, SSIM, BFS path detection, color counting).

### Key Features

- **6,000 puzzles** across 10 tasks and 3 difficulty levels
- **Dual-track evaluation**: generative (produce a solution image) and discriminative (select from candidates)
- **Multi-resolution**: every puzzle rendered at 512px, 1024px, and 2048px
- **Deterministic**: seeded generation (seed=42) for exact reproducibility
- **Programmatic verification**: CV-based solution checking, no subjective evaluation

## Task Examples

All examples below show **medium difficulty** puzzles at 512px resolution.

### 01 — Multi-layer Mazes
<p align="center">
<img src="assets/task_01_maze_puzzle.png" width="400" alt="Maze puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_01_maze_solution.png" width="400" alt="Maze solution">
</p>
<p align="center"><em>Navigate through multiple maze layers connected by portals (colored dots).</em></p>

### 02 — Raven's Progressive Matrices
<p align="center">
<img src="assets/task_02_raven_puzzle.png" width="300" alt="Raven puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_02_raven_solution.png" width="300" alt="Raven solution">
</p>
<p align="center"><em>Identify the missing panel in a 3×3 matrix governed by transformation rules.</em></p>

### 03 — Cellular Automata Forward Prediction
<p align="center">
<img src="assets/task_03_ca_forward_puzzle.png" width="300" alt="CA Forward puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_03_ca_forward_solution.png" width="300" alt="CA Forward solution">
</p>
<p align="center"><em>Given a rule and initial state, predict the next state of a cellular automaton.</em></p>

### 04 — Cellular Automata Inverse Inference
<p align="center">
<img src="assets/task_04_ca_inverse_puzzle.png" width="300" alt="CA Inverse puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_04_ca_inverse_solution.png" width="300" alt="CA Inverse solution">
</p>
<p align="center"><em>Given an initial and final state, identify which rule was applied.</em></p>

### 05 — Visual Logic Grids
<p align="center">
<img src="assets/task_05_logic_grid_puzzle.png" width="300" alt="Logic Grid puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_05_logic_grid_solution.png" width="300" alt="Logic Grid solution">
</p>
<p align="center"><em>Complete a constraint-satisfaction grid using visual clues.</em></p>

### 06 — Planar Graph k-Coloring
<p align="center">
<img src="assets/task_06_graph_coloring_puzzle.png" width="300" alt="Graph Coloring puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_06_graph_coloring_solution.png" width="300" alt="Graph Coloring solution">
</p>
<p align="center"><em>Color graph nodes so no adjacent nodes share the same color.</em></p>

### 07 — Graph Isomorphism Detection
<p align="center">
<img src="assets/task_07_graph_isomorphism_puzzle.png" width="300" alt="Graph Isomorphism puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_07_graph_isomorphism_solution.png" width="300" alt="Graph Isomorphism solution">
</p>
<p align="center"><em>Determine whether two graphs have the same structure despite different layouts.</em></p>

### 08 — Unknot Detection
<p align="center">
<img src="assets/task_08_unknot_puzzle.png" width="300" alt="Unknot puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_08_unknot_solution.png" width="300" alt="Unknot solution">
</p>
<p align="center"><em>Determine whether a knot diagram can be untangled into a simple loop.</em></p>

### 09 — Orthographic Projection Identification
<p align="center">
<img src="assets/task_09_ortho_projection_puzzle.png" width="300" alt="Ortho Projection puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_09_ortho_projection_solution.png" width="300" alt="Ortho Projection solution">
</p>
<p align="center"><em>Match a 3D object to its correct orthographic projection views.</em></p>

### 10 — Isometric Reconstruction
<p align="center">
<img src="assets/task_10_iso_reconstruction_puzzle.png" width="300" alt="Iso Reconstruction puzzle">
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="assets/task_10_iso_reconstruction_solution.png" width="300" alt="Iso Reconstruction solution">
</p>
<p align="center"><em>Reconstruct a 3D isometric view from orthographic projections.</em></p>

## Tasks

| # | Task | Domain | Easy | Medium | Hard |
|---|------|--------|------|--------|------|
| 01 | **Multi-layer Mazes** | Spatial Reasoning | 8×8, 1 layer | 16×16, 2 layers, 2 portals | 32×32, 3 layers, 5 portals |
| 02 | **Raven's Progressive Matrices** | Abstract Reasoning | 1 rule | 2 rules | 3 rules, compositional |
| 03 | **Cellular Automata Forward** | Causal Reasoning | 8×8, 1 step | 16×16, 3 steps | 32×32, 5 steps |
| 04 | **Cellular Automata Inverse** | Causal Reasoning | 8×8, 4 rules | 16×16, 8 rules | 32×32, 16 rules |
| 05 | **Visual Logic Grids** | Logical Reasoning | 4×4, 6 constraints | 5×5, 10 constraints | 6×6, 16 constraints |
| 06 | **Planar Graph k-Coloring** | Graph Theory | 6 nodes, k=4 | 12 nodes, k=4 | 20 nodes, k=3 |
| 07 | **Graph Isomorphism** | Graph Theory | 5 nodes | 8 nodes | 12 nodes |
| 08 | **Unknot Detection** | Topology | 3 crossings | 6 crossings | 10 crossings |
| 09 | **Orthographic Projection** | Spatial Reasoning | 6 faces | 10 faces, 1 concavity | 16 faces, 3 concavities |
| 10 | **Isometric Reconstruction** | Spatial Reasoning | 6 faces | 10 faces, 1 ambiguity | 16 faces, 2 ambiguities |

Each task has **200 puzzles per difficulty level** (easy / medium / hard) = **600 per task**, **6,000 total**.

## Evaluation Tracks

### Track 1 — Generative

The model receives a puzzle image and must **produce a solution image** (e.g., a solved maze, colored graph, completed matrix). Verification is fully programmatic using computer vision:

| Task | Verification Method |
|------|-------------------|
| Maze | BFS path detection on rendered solution |
| Raven | SSIM comparison (threshold 0.997) |
| CA Forward / Inverse | Pixel sampling of cell states |
| Logic Grid | Pixel sampling of grid cells |
| Graph Coloring | Occlusion-aware node color sampling |
| Graph Isomorphism | Color counting + structural validation |
| Unknot | Color region counting |
| Ortho Projection | Pixel sampling of projection views |
| Iso Reconstruction | SSIM comparison (threshold 0.99999) |

### Track 2 — Discriminative

The model receives a puzzle image plus **4 distractor images** and **1 correct solution**, and must identify the correct answer. This is a 5-way multiple-choice visual task.

## Dataset Structure

```
snapshot/
├── metadata.json              # Generation config and parameters
├── README.md                  # This file
├── task_01_maze/
│   ├── task_info.json         # Task parameters
│   ├── easy/
│   │   ├── 512/               # 512px resolution
│   │   │   ├── puzzle_0000.png
│   │   │   ├── solution_0000.png
│   │   │   ├── distractors_0000/
│   │   │   │   ├── distractor_00.png
│   │   │   │   ├── distractor_01.png
│   │   │   │   ├── distractor_02.png
│   │   │   │   └── distractor_03.png
│   │   │   ├── puzzle_0001.png
│   │   │   ├── solution_0001.png
│   │   │   ├── distractors_0001/
│   │   │   │   └── ...
│   │   │   └── ... (200 puzzles)
│   │   ├── 1024/              # 1024px resolution
│   │   │   └── ... (same structure)
│   │   └── 2048/              # 2048px resolution
│   │       └── ... (same structure)
│   ├── medium/
│   │   └── ... (same structure)
│   └── hard/
│       └── ... (same structure)
├── task_02_raven/
│   └── ...
└── ... (10 tasks total)
```

### File Naming Convention

- `puzzle_NNNN.png` — the input puzzle image
- `solution_NNNN.png` — the ground-truth solution (Track 1 target)
- `distractors_NNNN/distractor_0X.png` — 4 wrong answers (Track 2 candidates)

### Statistics

| Metric | Value |
|--------|-------|
| Total puzzles | 6,000 |
| Total PNG files | 108,008 |
| Resolutions | 512, 1024, 2048 px |
| Difficulties | easy, medium, hard |
| Distractors per puzzle | 4 |
| Dataset size | ~3.9 GB |
| Generation seed | 42 |

## Usage

### Loading with Hugging Face

```python
from datasets import load_dataset

# Load full dataset
ds = load_dataset("tylerxdurden/TACIT-benchmark")

# Or download specific files
from huggingface_hub import hf_hub_download

puzzle = hf_hub_download(
    repo_id="tylerxdurden/TACIT-benchmark",
    filename="task_01_maze/easy/1024/puzzle_0000.png",
    repo_type="dataset",
)
```

### Using the Evaluation Harness

```python
from tacit.registry import GENERATORS

# Regenerate a specific puzzle (deterministic)
gen = GENERATORS["maze"]
puzzle = gen.generate(seed=42, difficulty="easy", index=0)

# Verify a candidate solution (Track 1)
is_correct = gen.verify(puzzle, candidate_png=model_output_bytes)
```

See the [GitHub repository](https://github.com/danielxmed/tacit-benchmark) for full evaluation documentation.

## Reasoning Domains

The 10 tasks span **6 reasoning domains**, chosen to probe different aspects of visual cognition:

1. **Spatial Reasoning** — Mazes, orthographic projection, isometric reconstruction
2. **Abstract Reasoning** — Raven's progressive matrices
3. **Causal Reasoning** — Cellular automata (forward prediction and inverse inference)
4. **Logical Reasoning** — Visual logic grids
5. **Graph Theory** — Graph coloring, graph isomorphism
6. **Topology** — Unknot detection

## Citation

```bibtex
@misc{medeiros_2026,
    author       = {Daniel Nobrega Medeiros},
    title        = {TACIT-benchmark},
    year         = 2026,
    url          = {https://huggingface.co/datasets/tylerxdurden/TACIT-benchmark},
    doi          = {10.57967/hf/7904},
    publisher    = {Hugging Face}
}
```

## License

Apache 2.0