jablonkagroup/questions4manual_annotation

Corral – Traces for Manual Annotation

Selected Corral traces for manual annotation of epistemic patterns across environments, models, and QA dimensions

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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 evaluation traces selected for manual annotation of epistemic patterns across the Corral benchmark.

The dataset is organized into 68 configurations, with one config per selected model, environment, scope, and agent trace setting. Within each config, rows contain traces sampled in a stratified manner from the full Corral trace collection.

The included traces were selected because an LLM annotator identified them as cases where the agents do not reason scientifically, making them a targeted subset for downstream human review and epistemic-pattern analysis. This resource is intended for annotation, qualitative analysis, and process-level study of scientific-agent behaviour rather than for general-purpose model pre-training.

🎯 Supported Uses

• 🧠 Manually annotating epistemic patterns in scientific-agent traces
• 📊 Auditing cases where an automatic annotator flagged non-scientific reasoning
• 📐 Comparing trace-level failure modes across environments, models, and QA dimensions
• 🔁 Building qualitative analysis sets for reasoning-process studies in scientific agents

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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 selected trace setting, such as claude_sonnet_45-afm-level_1-tasks-ReActAgent-workflow-traces or gpt_4o-wetlab-level_3-tasks-ToolCallingAgent-workflow-traces.

Data Splits

All configs expose a single train split.

Data Instances

Each row corresponds to one selected trace associated with a specific combination of Corral environment, model, and QA dimension (knowledge or reasoning). These rows are intended as annotation units for epistemic-pattern review.

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🏗️ Dataset Creation

Curation Rationale

This dataset was created as part of Corral to support targeted inspection of scientific reasoning failures beyond end-task success. By collecting traces flagged as non-scientific by an automatic annotator, it provides a focused subset for manual annotation of epistemic patterns.

Source Data

The traces were derived from Corral evaluation runs across environments and models. A downstream LLM annotator identified cases suggesting that the agent did not reason scientifically, and those traces were collected here for subsequent manual review. Each retained row corresponds to one environment-model-dimension combination, where the dimension reflects the associated knowledge or reasoning QA setting.

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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.