jablonkagroup/corral-environment-tasks

---
pretty_name: Corral – Environment Tasks
language:
- en
license:
- mit
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
annotations_creators:
- expert-generated
language_creators:
- expert-generated
tags:
- corral
- benchmark
- llm-agents
- scientific-agents
- tasks
- task-descriptions
- chemistry
- materials-science
- tools
- scoring
- environments
dataset_version: 0.0.1
dataset_release_date: '2026-04-22'
---

# *Corral* – Environment Tasks

<div align="center">


[](https://lamalab-org.github.io/corral/)
[](https://lamalab-org.github.io/corral/docs/)
[](https://github.com/lamalab-org/corral)
[](https://opensource.org/licenses/MIT)
[](https://arxiv.org/abs/2604.18805)
[](https://huggingface.co/datasets/jablonkagroup/corral-environment-tasks)

Task definitions across the 8 Corral environments, including descriptions, allowed tools, scoring functions, and submission formats

</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 **task definitions** for the **8 environments** included in the *Corral* benchmark.

The dataset is organized into **multiple configurations**, with **one config per environment, scope, and granularity combination** (`tasks` or `subtasks`). Together, these configs expose the task catalog used throughout *Corral* across difficulty settings and task granularities.

Each row corresponds to a distinct task specification and includes the **task name**, **description**, **allowed tools**, **scoring function**, and **submission format**. This resource is intended for benchmark inspection, reproducibility, environment understanding, and development of agents that interact with *Corral* tasks rather than for general-purpose model pre-training.

### 🎯 Supported Uses

- 🧠 Inspecting the task definitions available in each Corral environment and scope
- 🧰 Auditing which tools are permitted for a given task or subtask
- 📊 Understanding how tasks are scored and what submission format they require
- 🔁 Building agents, prompts, and evaluations against the published Corral task catalog

---

## 🧪 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 corresponds to one unique combination of **environment**, **scope**, and **granularity** (`tasks` or `subtasks`).

### Data Splits

All configs expose a single `train` split.

### Data Instances

Each row corresponds to one **task definition** within a given environment/scope/granularity config. Rows include the task name, task description, the tools allowed for solving it, the scoring function used for evaluation, and the expected submission format.

---

## 🏗️ Dataset Creation

### Curation Rationale

This dataset was created as part of *Corral* to make the benchmark's **task specifications** directly accessible across environments, scopes, and granularities. It supports reproducibility, environment inspection, and agent development by exposing the exact task definitions used in the benchmark.

### Source Data

The task records were derived from the *Corral* environment specifications. For each environment, scope, and task or subtask granularity, the corresponding config contains the published task definitions together with their execution constraints and evaluation metadata.

---

## 🔗 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.