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minitorch-graphqa

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code reasoning
graph-based QA
symbolic reasoning
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trm
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minitorch-graphqa
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---
license: mit
task_categories:
  - question-answering
  - code-understanding
language:
  - en
pretty_name: MiniTorch GraphQA
tags:
  - code reasoning
  - graph-based QA
  - symbolic reasoning
  - minitorch
  - trm
size_categories:
  - n<1K
source_datasets:
  - original
---

# MiniTorch GraphQA

A dataset of 100 developer-style questions over the [MiniTorch](https://github.com/atgctg/minitorch) codebase, designed for **structured, graph-based code reasoning**. Each question maps to a precise function or class in the source code and is intended for use with **symbolic reasoning models** like the Tiny Recursion Model (TRM).

## Intended Use

This dataset is designed to evaluate **lightweight, recursive reasoning agents** that operate over **retrieved subgraphs of code** (e.g., 10-node neighborhoods). It is **not** for code generation or language modeling.

## Data Format

Each line in `questions.jsonl` is a JSON object:

```json
{
  "id": "minitorch-001",
  "question": "Which function computes the derivative of scalar addition?",
  "answer_node": "minitorch.scalar.Scalar.add_back",
  "module": 1,
  "reasoning_type": "autodiff"
}
    id: unique identifier
    question: natural language developer question
    answer_node: fully qualified name of the ground-truth function/class
    module: MiniTorch module number (0–6)
    reasoning_type: category of reasoning required
     

License 

MIT License.
Citation 

If you use this dataset, please cite: 

@dataset{minitorch_graphqa_2025,
  author = {Sarosh Quraishi},
  title = {MiniTorch GraphQA: A Symbolic Reasoning Benchmark for Code},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/saroshq/minitorch-graphqa}
}
 
 
Acknowledgements 

    MiniTorch: https://github.com/atgctg/minitorch