---
pretty_name: Corral – QAs
language:
- en
license:
- mit
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- question-answering
annotations_creators:
- machine-generated
language_creators:
- expert-generated
- machine-generated
tags:
- corral
- benchmark
- llm-agents
- scientific-agents
- qa
- evaluation
- chemistry
- materials-science
- knowledge
- reasoning
- irt
- item-response-theory
dataset_version: "0.0.1"
dataset_release_date: "2026-04-22"
configs:
- config_name: afm_qa
  data_files:
  - split: train
    path: afm_qa/train-*.parquet
  default: true
- config_name: afm_reasoning_qa
  data_files:
  - split: train
    path: afm_reasoning_qa/train-*.parquet
- config_name: catalyst_qa
  data_files:
  - split: train
    path: catalyst_qa/train-*.parquet
- config_name: catalyst_reasoning_qa
  data_files:
  - split: train
    path: catalyst_reasoning_qa/train-*.parquet
- config_name: md_qa
  data_files:
  - split: train
    path: md_qa/train-*.parquet
- config_name: md_reasoning_qa
  data_files:
  - split: train
    path: md_reasoning_qa/train-*.parquet
- config_name: ml_qa
  data_files:
  - split: train
    path: ml_qa/train-*.parquet
- config_name: ml_reasoning_qa
  data_files:
  - split: train
    path: ml_reasoning_qa/train-*.parquet
- config_name: resistor_qa
  data_files:
  - split: train
    path: resistor_qa/train-*.parquet
- config_name: resistor_reasoning_qa
  data_files:
  - split: train
    path: resistor_reasoning_qa/train-*.parquet
- config_name: retro_qa
  data_files:
  - split: train
    path: retro_qa/train-*.parquet
- config_name: retro_reasoning_qa
  data_files:
  - split: train
    path: retro_reasoning_qa/train-*.parquet
- config_name: spectra_qa
  data_files:
  - split: train
    path: spectra_qa/train-*.parquet
- config_name: spectra_reasoning_qa
  data_files:
  - split: train
    path: spectra_reasoning_qa/train-*.parquet
- config_name: wetlab_qa
  data_files:
  - split: train
    path: wetlab_qa/train-*.parquet
- config_name: wetlab_reasoning_qa
  data_files:
  - split: train
    path: wetlab_reasoning_qa/train-*.parquet
---

# *Corral* – QAs

<div align="center">

![Corral Logo](corral_logo_final.png)

[![Website](https://img.shields.io/badge/🌐-Website-green)](https://lamalab-org.github.io/corral/)
[![Docs](https://img.shields.io/badge/📚-Docs-blue)](https://lamalab-org.github.io/corral/docs/)
[![GitHub](https://img.shields.io/badge/💻-Code-black?logo=github)](https://github.com/lamalab-org/corral)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Paper](https://img.shields.io/badge/📄-Paper-red)](https://arxiv.org/abs/2604.18805)
[![Dataset](https://img.shields.io/badge/🤗%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/jablonkagroup/corral-QAs)

Question-answer pairs probing factual knowledge and reasoning across all 8 Corral environments

</div>

---

## 📋 Dataset Summary

This dataset is part of the *Corral* collection accompanying the paper [*AI scientists produce results without reasoning scientifically*](https://arxiv.org/abs/2604.18805). It contains the **question-answer pairs (QAs)** used to test the **factual knowledge** and **reasoning ability** of models across all **8 Corral environments**.

The dataset is organized into **16 configurations**, corresponding to the Cartesian product of the **8 environments** and the two evaluation dimensions: **knowledge** and **reasoning**. For example, a config such as `afm_knowledge` contains QAs targeting factual knowledge in the AFM Experiment Execution environment, whereas `afm_reasoning` contains QAs designed to test reasoning over the same domain.

These QAs are the items used in the **Item Response Theory (IRT)** analyses reported in the *Corral* study, where they serve as indicators for the latent **knowledge** and **reasoning** factors. This resource is intended for evaluation, psychometric modeling, and analysis of scientific-agent capabilities rather than for general-purpose model pre-training.

### 🎯 Supported Uses

- 🧠 Evaluating factual knowledge and scientific reasoning across Corral environments
- 📊 Reproducing and extending the IRT analyses reported in the paper
- 📐 Studying latent knowledge and reasoning factors in scientific-agent benchmarks
- 🔁 Building meta-evaluation datasets for model comparison and capability analysis

---

## 🧪 About *Corral*

[*Corral*](https://lamalab-org.github.io/corral/) is a framework for the *science of agents and agents for science*. It provides a microservice architecture that **decouples agents from environments** via a client–server design (REST API), ensuring flexibility, reproducibility, and robust isolation.

- 🌍 **Environments** define the task space, available tools, and observable feedback — from chemistry labs to HPC clusters.
- 🤖 **Agents** are modular LLM-based entities supporting scaffolds such as ReAct, ToolCalling, LLMPlanner, and Reflection.
- 📝 **Tasks** define problems to solve, complete with scoring functions. Tasks can be chained into TaskGroups for complex multi-stage challenges.

*Corral* currently ships **8 environments**, **97 tools**, **115 tasks**, and **786 subtasks** spanning chemistry, physics, and materials science.

### 🌍 Environments

| Environment | Description | 🔧 Tools | 📝 Tasks/scope | 🔭 Scopes | ⏱️ Avg. trace length |
|---|---|:---:|:---:|:---:|:---:|
| 🧫 **Inorganic Qualitative Analysis** | Identify unknown cations in solution through systematic wet-lab procedures (reagent addition, flame tests, pH measurement, centrifugation, etc.). Observations are computed from thermodynamic data. Three scopes progressively increase the number of candidate ions. | 14 | 10 | 3 | 39.4 |
| ⚡ **Circuit Inference** | Recover the topology and component values of a hidden resistor network from pairwise resistance measurements. Tools provide series/parallel calculations, delta-wye transforms, and circuit validation. | 9 | 6 | 1 | 15.0 |
| 🔭 **Spectroscopic Structure Elucidation** | Determine the molecular structure of an unknown compound by requesting and interpreting spectroscopic data (MS, NMR, HSQC, IR) alongside reference databases for chemical shifts and isotope distributions. | 16 | 20 | 2 | 15.1 |
| 🧬 **Retrosynthetic Planning** | Design multi-step synthetic routes to target molecules under cost, step-count, and commercial-availability constraints, using a template catalogue and functional-group detection tools. | 15 | 8 | 3 | 25.5 |
| 🤖 **ML-based Property Prediction** | Assemble a complete ML pipeline to predict formation energies of material polymorphs using data from the Materials Project, covering feature engineering, XGBoost training, and cross-validation. | 14 | 3 | 1 | 16.6 |
| 🔬 **AFM Experiment Execution** | Analyze and interpret atomic force microscopy data for nanoscale surface characterization, including topographical and mechanical property measurements. | 6 | 1 | 4 | 26.3 |
| ⚛️ **Molecular Simulation** | Design and execute molecular dynamics simulations with LAMMPS to predict materials properties, covering the full workflow from crystal structure retrieval to force-field queries and log analysis. | 8 | 2–3 | 2 | 30.4 |
| 🏗️ **Adsorption Surface Construction** | Build adsorbate–slab configurations from bulk crystal structures for heterogeneous catalysis studies, integrating Materials Project retrieval, slab generation, and adsorption-site enumeration. | 15 | 3 | 1 | 19.6 |

---

## 🗂️ Dataset Structure

### Configs

Each config name encodes `{environment}_{dimension}`, where:

- `environment` is a short identifier for one of the 8 *Corral* environments (e.g., `afm`, `circuit_inference`, `spectroscopic`, `retrosynthesis`, `ml_property`, `molecular_simulation`, `adsorption`).
- `dimension` is either `knowledge` or `reasoning`.

This yields **16 total configs**, one for each environment and knowledge/reasoning combination.

### Data Splits

All configs expose a single `train` split.

### Data Instances

Each row corresponds to one **question-answer item** associated with a specific Corral environment and one of the two evaluation dimensions: knowledge or reasoning.

---

## 🏗️ Dataset Creation

### Curation Rationale

This dataset was created as part of *Corral* to measure scientific-agent capabilities beyond end-task success, separating **factual knowledge** from **reasoning ability** through targeted QA items that can be modeled with IRT.

### Source Data

The QAs were derived from the task content, domain knowledge, and reasoning demands of the *Corral* benchmark environments. They were constructed to probe environment-specific factual understanding and multi-step reasoning, and were then used in IRT modeling as indicators of the latent knowledge and reasoning factors.

---

## 🔗 Relation to Other Corral Artifacts

This dataset is one component of the broader *Corral* release and is best interpreted together with the matching task definitions, execution traces, reports, aggregate results, and reasoning annotations available in the [*Corral* collection](https://huggingface.co/collections/jablonkagroup/corral).

---

## 📄 Citation

```bibtex
@article{ríos-garcía2026ai,
  title   = {AI scientists produce results without reasoning scientifically},
  author  = {Martiño Ríos-García and Nawaf Alampara and Chandan Gupta and Indrajeet Mandal and Sajid Mannan and Ali Asghar Aghajani and N. M. Anoop Krishnan and Kevin Maik Jablonka},
  year    = {2026},
  journal = {arXiv preprint arXiv: 2604.18805}
}
```

## 📜 License

This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).

## Changelog

### 2026-04-22

- Initial release of the dataset card.