---
title: corral-traces
canonical_url: "https://www.modelscope.cn/datasets/jablonkagroup/corral-traces"
md_url: "https://www.modelscope.cn/datasets/jablonkagroup/corral-traces.md"
repository: jablonkagroup/corral-traces
last_updated: 2026-07-29
license: "[mit]"
storage_size: "101 MB"
downloads: 1263
stars: 0
---

# corral-traces

> corral-traces - jablonkagroup 在 ModelScope 开源的数据集。Corral – Evaluation Traces

jablonkagroup/corral-traces 是 ModelScope 魔搭社区上的数据集，存储大小 101 MB，采用 [mit] 许可。

- **Repository**: jablonkagroup/corral-traces
- **License**: [mit]
- **Storage size**: 101 MB
- **Downloads**: 1263
- **Stars**: 0
- **Last updated**: 2026-07-29

Source: https://www.modelscope.cn/datasets/jablonkagroup/corral-traces

---

# *Corral* – Evaluation Traces

<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-traces)

Full evaluation traces across Corral environments, models, agents, and task granularities

</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 **full evaluation traces** collected across all **8 Corral environments**.

Each configuration (config) corresponds to a unique combination of *model*, *environment*, *scope* (difficulty level), *agent*, *tool verbosity*, and *granularity* (*tasks* or *subtasks*). The full set of configs spans the Cartesian product of these dimensions across the benchmark.

This resource is intended for **process-level analysis**, reproducibility, and the study of scientific-agent behaviour across environments and execution settings rather than for general-purpose model pre-training.

### 🎯 Supported Uses

- 🔍 Studying agent behaviour across models, environments, scopes, and agent scaffolds
- 📊 Auditing tool-use patterns and reasoning trajectories under different tool-verbosity settings
- 🔁 Reproducing and extending trace-level analyses reported for *Corral*
- 📐 Comparing execution dynamics at both task and subtask granularities

---

## 🧪 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 *model*, *environment*, *scope*, *agent*, *tool verbosity*, and *granularity* (*tasks* or *subtasks*).

### Data Splits

All configs expose a single `train` split.

### Data Instances

Each row corresponds to one **agent evaluation run** and contains the full **message history** produced during that run, together with metadata identifying the model, environment, scope, agent, tool verbosity, and whether the run is organized at the task or subtask granularity.

---

## 🏗️ Dataset Creation

### Curation Rationale

This dataset was created as part of *Corral* to make the benchmark's **full evaluation trajectories** available for analysis. It supports process-level study of LLM-based scientific agents, including how behaviour varies across environments, scopes, agent scaffolds, and execution settings.

### Source Data

The traces were derived from *Corral* evaluation runs across all 8 environments. Each record captures the message history of an agent run under a specific combination of model, environment, scope, agent, tool verbosity, and task or subtask granularity.

---

## 🔗 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.