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README.md

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 ---
 license: apache-2.0
+datasets:
+- thermbench
+tags:
+- benchmark
+- heatmap
+- source-localization
+- synthetic-data
+pretty_name: ThermBench
 ---
+
+# ThermBench 🔥: Heat Source Localization Benchmark
+
+## Summary
+**ThermBench** is a synthetic benchmark dataset for evaluating algorithms that infer hidden **heat sources** from a **2D heatmap**.
+Each sample consists of an observed grid (matrix of thermal intensities) and the true underlying source positions and strengths.
+The diffusion rule is based on inverse Manhattan distance:
+
+\[
+H(i,j) \;=\; \sum_{s=1}^K \frac{I_s}{d_{s,(i,j)}+1}
+\]
+
+where \(I_s\) is the source intensity and \(d\) is the Manhattan distance from source \(s\) to grid cell \((i,j)\).
+
+## Dataset Structure
+- **`input_text`**: Heatmap input following problem format:
+  - First line: grid size `N M`
+  - Second line: number of sources `K`
+  - Then `N` rows of grid heat values
+- **`output_text`**: Ground-truth sources (row, col, intensity)
+- **`level`**: Difficulty tier (`very_easy`, `easy`, `medium`, `hard`, `extreme`)
+
+Example:
+```json
+{
+  "level": "easy",
+  "input_text": "5 5\n2\n10 8 6 5 4\n8 10 7 6 5\n...",
+  "output_text": "1 1 10.0\n5 5 10.0"
+}
+```
+
+## Difficulty Levels
+- `very_easy`: 3×3 grid, 1 source
+- `easy`: 5×5 grid, 2 sources
+- `medium`: 10×10 grid, 3 sources
+- `hard`: 20×20 grid, 5 sources
+- `extreme`: 30×30 grid, 7 sources
+
+Each level contains **100 samples** (total: 500 entries).
+A **fuzzy variant** (`thermbench-fuzzy`) introduces noise, source jitter, and decay variations to simulate real-world uncertainty.
+
+## Usage
+```python
+from datasets import load_dataset
+
+ds = load_dataset("username/thermbench", split="train")
+print(ds[0])
+```
+
+## Applications
+- Algorithmic evaluation of source localization
+- Benchmarking robustness against noise
+- Teaching problem-solving with synthetic physics-based data
+
+## License
+Apache License, Version 2.0

heatmap.jsonl

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