Visually Interpretable Subtask Reasoning for Visual Question Answering
Authors: name: Yu Cheng, name: Arushi Goel, name: Hakan Bilen

Answering complex visual questions like `Which red furniture can be used for
sitting?' requires multi-step reasoning, including object recognition,
attribute filtering, and relational understanding. Recent work improves
interpretability in multimodal large language models (MLLMs) by decomposing
tasks into sub-task programs, but these methods are computationally expensive
and less accurate due to poor adaptation to target data. To address this, we
introduce VISTAR (Visually Interpretable Subtask-Aware Reasoning Model), a
subtask-driven training framework that enhances both interpretability and
reasoning by generating textual and visual explanations within MLLMs. Instead
of relying on external models, VISTAR fine-tunes MLLMs to produce structured
Subtask-of-Thought rationales (step-by-step reasoning sequences). Experiments
on two benchmarks show that VISTAR consistently improves reasoning accuracy
while maintaining interpretability. Our code and dataset will be available at
https://github.com/ChengJade/VISTAR.