docs: add comprehensive dataset card

README.md

@@ -1,3 +1,203 @@
----
-license: apache-2.0
----
+---
+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/2602.07061)
+&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
+
+## 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