| | --- |
| | 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) |
| | | [arXiv paper](https://arxiv.org/abs/2603.00206) |
| | | [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"> |
| | |
| | <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"> |
| | |
| | <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"> |
| | |
| | <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"> |
| | |
| | <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"> |
| | |
| | <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"> |
| | |
| | <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"> |
| | |
| | <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"> |
| | |
| | <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"> |
| | |
| | <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"> |
| | |
| | <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 |
| |
|
