jablonkagroup/corral-environment-tasks

Corral – Environment Tasks

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

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📋 Dataset Summary

This dataset is part of the Corral collection accompanying the paper AI scientists produce results without reasoning scientifically. 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

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🧪 About Corral

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

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🗂️ 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.

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🏗️ 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.

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🔗 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.

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📄 Citation

@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.

Changelog

2026-04-22

• Initial release of the dataset card.