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README.md

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-# PARAMETR-Bench Dataset
+
+<div style="overflow: hidden; height: 390px; text-align: center;">
+  <img src="https://otheiner.github.io/PARAMETR-Bench-blog/assets/images/PARAMETR-Bench/heading.png" width="100%">
+</div>
+<br>
+
+# 📊 PARAMETR-Bench 
+
+<div style="text-align: justify">
+  
+PARAMETR-Bench is a procedurally generated benchmarking framework for evaluating AI agents on multi-step scientific data 
+analysis tasks. Each task instance is produced by a seeded generator, so every run yields fresh input data — addressing 
+the dataset contamination and saturation problems that affect static benchmarks. The key methodological contribution is 
+[**metarubrics**](https://otheiner.github.io/PARAMETR-Bench-blog/#metarubrics-and-rubrics): rubric templates that are auto-populated by the same generator that produces the task data, so grading criteria
+stay aligned with the ground truth without any manual effort per run.
+
+To learn more about the framework, read the [blog post](https://otheiner.github.io/PARAMETR-Bench-blog/).
+</div>
+
+# About This Dataset
+
+<div style="text-align: justify">
+Seeded procedural task generation mitigates the risk of benchmark data leaking into model training sets. The blog post describes
+a contamination detection experiment built on this property. The experiment compares model performance on two sets of seeds — 
+public and private — evaluated at the same difficulty level. At the time of initial evaluation, both sets are statistically 
+equivalent. By publishing the public seed set together with the generated input data and ground-truth artifacts, we maximize 
+the probability that this data is crawled and ingested by future model training pipelines. If a subsequent model generation has 
+been trained on this data, it should show measurably higher performance on the public seeds than on the withheld private seeds. 
+A statistically significant gap between the two would suggest contamination.
+
+Even though the probability of detectable leakage is uncertain, and PARAMETR-Bench is currently a personal proof-of-concept 
+project rather than an established benchmark, the experiment is worth running — the cost is low and the potential signal is 
+informative regardless of outcome.
+</div>
+
+# Dataset Structure
+
+<div style="text-align: justify">
+The dataset contains evaluation instances for four tasks across four public seeds (10, 11, 12, 13) at hard difficulty. Each 
+instance consists of:
+  
+  - **Input data** — multimodal files (images, CSV tables, text files) provided to the agent
+  - **Task prompt** — the task definition presented to the model
+  - **Rubrics** — populated grading criteria (JSON) with correct answers, instantiated from metarubrics using the generator's
+    ground truth. To understand metarubric, check the [blog post](https://otheiner.github.io/PARAMETR-Bench-blog/#metarubrics-and-rubrics)
+
+The dataset is organized by task and seed:
+
+```
+task_name/
+    seed_N/
+        input/        ← multimodal input files provided to the agent
+        prompt.md     ← task definition
+        rubrics.json  ← populated rubrics with correct answers
+```
+
+This dataset is not intended to be loaded programmatically as a training dataset. It is an evaluation artifact meant to be used 
+with the PARAMETR-Bench framework or inspected manually.
+</div>
+
+# Limitations
+
+This dataset covers four physics tasks in one domain. It is a proof-of-concept release. See the 
+[limitations section](https://otheiner.github.io/PARAMETR-Bench-blog/#limitations-and-whats-next) of the blog post for a full discussion.