| --- |
| license: mit |
| dataset_info: |
| - config_name: distributions |
| features: |
| - name: image |
| dtype: image |
| - name: id |
| dtype: int64 |
| - name: data |
| list: float64 |
| - name: true_params |
| dtype: string |
| splits: |
| - name: dist_easy |
| num_bytes: 1190665 |
| num_examples: 50 |
| - name: dist_hard |
| num_bytes: 2389492 |
| num_examples: 100 |
| - name: dist_astro |
| num_bytes: 824106 |
| num_examples: 50 |
| download_size: 4367800 |
| dataset_size: 4404263 |
| - config_name: timeseries |
| features: |
| - name: image |
| dtype: image |
| - name: id |
| dtype: int64 |
| - name: dates |
| list: timestamp[s] |
| - name: data |
| list: float64 |
| splits: |
| - name: ts_easy |
| num_bytes: 3202262 |
| num_examples: 50 |
| - name: ts_hard |
| num_bytes: 7169969 |
| num_examples: 110 |
| - name: ts_astro |
| num_bytes: 5424118 |
| num_examples: 50 |
| download_size: 15807487 |
| dataset_size: 15796349 |
| configs: |
| - config_name: distributions |
| data_files: |
| - split: dist_easy |
| path: distributions/dist_easy-* |
| - split: dist_hard |
| path: distributions/dist_hard-* |
| - split: dist_astro |
| path: distributions/dist_astro-* |
| - config_name: timeseries |
| data_files: |
| - split: ts_easy |
| path: timeseries/ts_easy-* |
| - split: ts_hard |
| path: timeseries/ts_hard-* |
| - split: ts_astro |
| path: timeseries/ts_astro-* |
|
|
| --- |
| |
| # DAWN Dataset |
|
|
| DAWN benchmarks AI agents on two core data science tasks: distribution fitting and time series modeling. Both domains are organized into Easy, Hard, and Astro splits, with synthetically generated data to allow robust measurement of model fit. |
|
|
| ## VESTA: Visual Exploration with Statistical Tool Agents |
| We propose and benchmark AI agents on DAWN in our arxiv paper: https://arxiv.org/abs/2606.00384 |
|
|
| Fitting quantitative models to data is a central step in scientific workflows, yet it |
| remains one of the least automated. Recent agent-based systems leverage language |
| and vision-language models (VLMs) to iteratively propose and refine statistical |
| models, but these systems struggle on more challenging modeling tasks. To address |
| these limitations, we introduce VESTA (Visual Exploration with Statistical Tool |
| Agents), a framework that equips VLMs with a dynamically growing exploration |
| toolkit to guide model refinement through data transformations, hypothesis-driven |
| visualizations, and robust statistical tests. Unlike prior systems that rely on iterative |
| critique alone, VESTA actively explores data before and during refinement by |
| selecting or creating diagnostic tools, which accumulate in the model’s context and |
| can be reused later. We evaluate VESTA against established baselines in three toolkit |
| configurations: no tools, static expert-written tools, and dynamic model-written |
| tools. To support this evaluation, we introduce DAWN (Dataset for Automated |
| Workflows and Numerical Modeling), a benchmark targeting distribution fitting |
| and time series modeling with varying difficulty tiers, and culminating in real-world |
| astronomy tasks including modeling initial mass functions and gravitational-wave |
| chirp signals. We find that VESTA’s dynamic tool creation outperforms prior agentic |
| pipelines, with the largest gains on complex and domain-specific tasks. We further |
| show that dynamically generated tools are substantially more sophisticated than |
| those produced by existing visual tool-creation systems, covering more diagnostic |
| categories per function and strongly preferring visual outputs that the VLM critic |
| can reason over directly. |
|
|
| ## Distribution Fitting |
|
|
| - **dist_easy**: Identify the family and parameters of a unimodal distribution. Families include gaussian, lognormal, student-t, exponential, uniform, weibull, laplace, cauchy, and pareto, with priors and sample sizes randomly sampled. |
| - **dist_hard**: Mixtures of exactly two distributions from the families above, reflecting real-world phenomena such as gaussian-laplace mixtures (speech signal processing) and lognormal-exponential mixtures (survival analysis). |
| - **dist_astro**: Initial mass functions (IMF), describing the distribution of stellar masses at birth. Includes five functional forms: Salpeter (single power law), Kroupa (piecewise power law), Chabrier (log-normal plus power law), and two freeform variants (tight and wide breaks). |
| |
| ## Time Series Modeling |
| |
| - **ts_easy**: Sequences with simple linear trends (increasing, decreasing, flat) and standard periodic seasonal components, with minimal noise. |
| - **ts_hard**: More complex dynamics including random walks (ARIMA processes), sigmoidal (S-curve) functions, ECG heartbeat signals, and sales-forecasting-style seasonality with increasing averages. Requires multi-periodic and Gaussian process modeling (e.g. Matérn, RBF kernels). |
| - **ts_astro**: Time series inspired by gravitational wave chirps, where frequency continuously increases as binary star systems converge, including a variant where amplitude additionally decays over time. |
|
|
| ## Dataset Statistics |
|
|
| Each dataset $D_i$ contains $n \in [600, 1500]$ points sampled from a ground-truth distribution or time series, with parameters drawn uniformly from fixed ranges. For each domain and difficulty split, 50 (Easy/Astro) or 100 (Hard) examples are generated, for a total of 200 distributions and 200 time series. |
| |
| ## Citation |
| |
| If you use DAWN, please cite: |
| |
| ```bibtex |
| @misc{rudman2026vestavisualexplorationstatistical, |
| title={VESTA: Visual Exploration with Statistical Tool Agents}, |
| author={William Rudman and Abhishek Divekar and Kanishk Jain and Sebastian Joseph and Stella S. R. Offner and Matthew Lease and Kyle Mahowald and Greg Durrett and Junyi Jessy Li}, |
| year={2026}, |
| eprint={2606.00384}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.AI}, |
| url={https://arxiv.org/abs/2606.00384}, |
| } |
| ``` |
