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README.md

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+---
+license: apache-2.0
+tags:
+- math
+- reasoning
+- dynamic-analysis
+- bode-plot
+- llm-evaluation
+- exponential-functions
+pretty_name: "MathBode-ExponentialInterest"
+size_categories:
+- "1K<n<10K"
+---
+
+# MathBode-ExponentialInterest: Exponential Functions Domain
+
+Compound interest and exponential growth problems from the MathBode benchmark.
+
+This dataset is part of the **MathBode** benchmark, which evaluates the dynamic reasoning capabilities of large language models (LLMs) by treating parametric math problems as dynamic systems. Instead of testing static accuracy on fixed problems, MathBode sinusoidally varies a parameter and measures the model's response in terms of **gain** (amplitude tracking) and **phase** (reasoning lag), analogous to a Bode plot in control theory.
+
+## About This Domain
+
+This dataset contains **Exponential Functions** problems specifically, with approximately 9,408 prompts covering:
+
+- **3 question variants** with different constants
+- **Sinusoidal parameter sweeps** at 5 frequencies (1, 2, 4, 8, 16 cycles)
+- **3 phase offsets** (0°, 120°, 240°) for statistical robustness
+- **3 amplitude scales** (0.5x, 1.0x, 2.5x) for non-linearity testing
+- **Chirp signal validation** with continuous frequency sweeps
+
+## Dataset Structure
+
+| Column | Description |
+|--------|-------------|
+| `family` | Problem family name (always `exponential_interest` for this dataset) |
+| `question_id` | Question variant ID (0, 1, or 2) |
+| `signal_type` | Type of parameter variation (`sinusoid` or `chirp`) |
+| `amplitude_scale` | Scaling factor for parameter variation amplitude |
+| `frequency_cycles` | Frequency of parameter variation in cycles per sweep |
+| `phase_deg` | Starting phase of sinusoidal signal in degrees |
+| `time_step` | Step index within the sweep |
+| `p_value` | The dynamic parameter value for this time step |
+| `prompt` | The complete prompt text for the model |
+| `ground_truth` | The correct numerical answer |
+| `symbolic_baseline_answer` | Answer from perfect symbolic solver |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load this specific domain
+dataset = load_dataset("cognitive-metrology-lab/MathBode-ExponentialInterest")
+
+# Access the data
+print(dataset['train'][0])
+```
+
+## Related Datasets
+
+- [MathBode](https://huggingface.co/datasets/cognitive-metrology-lab/MathBode) - Complete benchmark with all families
+- [MathBode-LinearSolve](https://huggingface.co/datasets/cognitive-metrology-lab/MathBode-LinearSolve)
+- [MathBode-RatioSaturation](https://huggingface.co/datasets/cognitive-metrology-lab/MathBode-RatioSaturation)
+- [MathBode-ExponentialInterest](https://huggingface.co/datasets/cognitive-metrology-lab/MathBode-ExponentialInterest)
+- [MathBode-LinearSystem](https://huggingface.co/datasets/cognitive-metrology-lab/MathBode-LinearSystem)
+- [MathBode-SimilarTriangles](https://huggingface.co/datasets/cognitive-metrology-lab/MathBode-SimilarTriangles)
+
+## Citation
+
+If you use this dataset, please cite our work (citation to be added).