Dataset Card for SciTaRC

Dataset Summary

SciTaRC is an expert-authored diagnostic benchmark designed to evaluate Large Language Models (LLMs) on complex question-answering over raw LaTeX tables directly from arXiv scientific papers.

Unlike existing benchmarks that focus on simple cell lookups or clean grids, SciTaRC targets long-horizon composite reasoning—requiring models to chain interdependent operations such as multi-step arithmetic, filtering, and conditional logic.

To enable granular diagnosis, every instance is paired with an expert-annotated pseudo-code plan (spanning 5 core operators) and evaluated across 7 diagnostic complexity dimensions (e.g., plan horizon, control flow). By decoupling planning from execution, our findings reveal that the primary performance bottleneck for LLMs is not high-level strategy formulation, but faithfully executing multi-step computational chains over structured empirical data.

Dataset Structure

The dataset is provided as a single test split containing 371 expert-annotated instances.

Data Instances

A typical instance contains the question, the ground truth answer, the expert-authored pseudo-code plan, the LaTeX representations of the relevant tables, and the full text of the source paper.

Data Fields

Each JSON object in the dataset contains the following fields:

• paper (string): The arXiv ID of the source scientific paper (e.g., "2401.06769").
• question (string): The complex, multi-step question asked about the tabular data.
• answer (string): The ground-truth answer.
• plan (string): The expert-authored pseudo-code blueprint outlining logical and mathematical operations (e.g., SELECT, LOOP, COMPUTE, IF...ELSE, RETURN).
• relevant_tables (list of lists of strings): The exact LaTeX source code for the specific table(s) required to answer the question.
• tables (list of lists of strings): The LaTeX source code for all tables and figures extracted from the paper.
• fulltext (string): The complete LaTeX source text of the original scientific paper, providing full context.

Citation

If you find this work helpful, please cite our paper:

@misc{wang2026scitarc,
      title={SciTaRC: A Plan-Annotated Scientific Tabular QA Benchmark for Language Reasoning and Complex Computation}, 
      author={Hexuan Wang and Yaxuan Ren and Srikar Bommireddypalli and Shuxian Chen and Adarsh Prabhudesai and Rongkun Zhou and Elina Baral and Philipp Koehn},
      year={2026},
      eprint={2603.08910},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={[https://arxiv.org/abs/2603.08910](https://arxiv.org/abs/2603.08910)}, 
}