---
pretty_name: Corral – OSS-120B Trace Logprobs
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
annotations_creators:
- machine-generated
language_creators:
- expert-generated
- machine-generated
tags:
- corral
- benchmark
- llm-agents
- scientific-agents
- logprobs
- evaluation
- chemistry
- materials-science
- reasoning
- process-evaluation
dataset_version: "0.0.1"
dataset_release_date: "2026-04-22"
configs:
- config_name: afm_level_1_subtasks
  data_files:
  - split: train
    path: afm_level_1_subtasks/train-*.parquet
  default: true
- config_name: afm_level_1_tasks
  data_files:
  - split: train
    path: afm_level_1_tasks/train-*.parquet
- config_name: afm_level_2_subtasks
  data_files:
  - split: train
    path: afm_level_2_subtasks/train-*.parquet
- config_name: afm_level_2_tasks
  data_files:
  - split: train
    path: afm_level_2_tasks/train-*.parquet
- config_name: afm_level_3_subtasks
  data_files:
  - split: train
    path: afm_level_3_subtasks/train-*.parquet
- config_name: afm_level_3_tasks
  data_files:
  - split: train
    path: afm_level_3_tasks/train-*.parquet
- config_name: afm_level_4_subtasks
  data_files:
  - split: train
    path: afm_level_4_subtasks/train-*.parquet
- config_name: afm_level_4_tasks
  data_files:
  - split: train
    path: afm_level_4_tasks/train-*.parquet
- config_name: catalyst_subtasks
  data_files:
  - split: train
    path: catalyst_subtasks/train-*.parquet
- config_name: catalyst_tasks
  data_files:
  - split: train
    path: catalyst_tasks/train-*.parquet
- config_name: melting_level_1_subtasks
  data_files:
  - split: train
    path: melting_level_1_subtasks/train-*.parquet
- config_name: melting_level_1_tasks
  data_files:
  - split: train
    path: melting_level_1_tasks/train-*.parquet
- config_name: melting_level_2_tasks
  data_files:
  - split: train
    path: melting_level_2_tasks/train-*.parquet
- config_name: ml_subtasks
  data_files:
  - split: train
    path: ml_subtasks/train-*.parquet
- config_name: ml_tasks
  data_files:
  - split: train
    path: ml_tasks/train-*.parquet
- config_name: quenching_level_1_subtasks
  data_files:
  - split: train
    path: quenching_level_1_subtasks/train-*.parquet
- config_name: quenching_level_1_tasks
  data_files:
  - split: train
    path: quenching_level_1_tasks/train-*.parquet
- config_name: resistor_subtasks
  data_files:
  - split: train
    path: resistor_subtasks/train-*.parquet
- config_name: resistor_tasks
  data_files:
  - split: train
    path: resistor_tasks/train-*.parquet
- config_name: retrosynthesis_level_1_subtasks
  data_files:
  - split: train
    path: retrosynthesis_level_1_subtasks/train-*.parquet
- config_name: retrosynthesis_level_1_tasks
  data_files:
  - split: train
    path: retrosynthesis_level_1_tasks/train-*.parquet
- config_name: retrosynthesis_level_2_subtasks
  data_files:
  - split: train
    path: retrosynthesis_level_2_subtasks/train-*.parquet
- config_name: retrosynthesis_level_2_tasks
  data_files:
  - split: train
    path: retrosynthesis_level_2_tasks/train-*.parquet
- config_name: retrosynthesis_level_3_tasks
  data_files:
  - split: train
    path: retrosynthesis_level_3_tasks/train-*.parquet
- config_name: spectra_level_1_subtasks
  data_files:
  - split: train
    path: spectra_level_1_subtasks/train-*.parquet
- config_name: spectra_level_1_tasks
  data_files:
  - split: train
    path: spectra_level_1_tasks/train-*.parquet
- config_name: spectra_level_2_subtasks
  data_files:
  - split: train
    path: spectra_level_2_subtasks/train-*.parquet
- config_name: spectra_level_2_tasks
  data_files:
  - split: train
    path: spectra_level_2_tasks/train-*.parquet
- config_name: surface_energy_level_1_subtasks
  data_files:
  - split: train
    path: surface_energy_level_1_subtasks/train-*.parquet
- config_name: surface_energy_level_1_tasks
  data_files:
  - split: train
    path: surface_energy_level_1_tasks/train-*.parquet
- config_name: surface_energy_level_2_tasks
  data_files:
  - split: train
    path: surface_energy_level_2_tasks/train-*.parquet
- config_name: wetlab_level_1_subtasks
  data_files:
  - split: train
    path: wetlab_level_1_subtasks/train-*.parquet
- config_name: wetlab_level_1_tasks
  data_files:
  - split: train
    path: wetlab_level_1_tasks/train-*.parquet
- config_name: wetlab_level_2_subtasks
  data_files:
  - split: train
    path: wetlab_level_2_subtasks/train-*.parquet
- config_name: wetlab_level_2_task
  data_files:
  - split: train
    path: wetlab_level_2_task/train-*.parquet
- config_name: wetlab_level_2_tasks
  data_files:
  - split: train
    path: wetlab_level_2_tasks/train-*.parquet
- config_name: wetlab_level_3_subtasks
  data_files:
  - split: train
    path: wetlab_level_3_subtasks/train-*.parquet
- config_name: wetlab_level_3_tasks
  data_files:
  - split: train
    path: wetlab_level_3_tasks/train-*.parquet
---

# *Corral* – OSS-120B Trace Logprobs

<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-oss-trace-logprobs)

*Token-level log-probabilities for GPT-Oss-120B evaluation runs 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 **token-level log-probabilities** recorded during the evaluation runs of **GPT-Oss-120B** across all **8 Corral environments**.

Each configuration (config) of this dataset corresponds to a unique combination of *environment*, *scope* (difficulty level), and *granularity* (tasks or subtasks). For example, a config such as `afm_level_1_subtasks` contains the logprob records for the AFM Experiment Execution environment at scope level 1, broken down at the subtask granularity. The full set of configs spans the Cartesian product of the 8 environments, their respective scope levels, and the tasks/subtasks split.

This resource is designed for **process-level analysis**, interpretability research, and auditing of scientific-agent reasoning — not for general-purpose model pre-training.

### 🎯 Supported Uses

- 🔍 Auditing token-level confidence and uncertainty in scientific-agent completions
- 📊 Studying the relationship between model certainty and task success
- 🔁 Reproducing and extending the log-probability analyses reported in the paper
- 📐 Meta-evaluation and calibration studies of frontier LLMs on scientific tasks

---

## 🧪 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}_{scope}_{granularity}`, 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`).
- `scope` is the difficulty level (e.g., `level_1`, `level_2`, `level_3`).
- `granularity` is either `tasks` or `subtasks`.

### Data Splits

All configs expose a single `train` split.

### Data Instances

Each row corresponds to one **token-level log-probability record** from a GPT-Oss-120B completion produced during an agent evaluation run.

---

## 🏗️ Dataset Creation

### Curation Rationale

This dataset was created as part of *Corral* to enable process-level analysis of LLM-based scientific agents, specifically to study how token-level confidence relates to scientific reasoning quality and task outcomes.

### Source Data

Records are derived from agent evaluation runs on *Corral* benchmark tasks, capturing the log-probabilities returned by GPT-Oss-120B for each generated token across all 8 environments and their scope levels.

---

## 🔗 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.