New Code-Driven Framework Boosts Multimodal Table Reasoning Accuracy

Global: New Code-Driven Framework Boosts Multimodal Table Reasoning Accuracy

A recent arXiv preprint details the introduction of CoReTab, a code‑driven reasoning framework designed to improve multimodal table understanding by coupling multi‑step reasoning with executable Python code. The work, posted in January 2026, targets the shortcomings of existing datasets such as MMTab, which typically supply brief factual answers without explicit supervision for complex reasoning.

Limitations of Current Datasets

Researchers note that models trained on prior resources often generate concise responses that lack both accuracy and interpretability, making it difficult to trace how a final answer is derived. This gap hampers progress in applications that require transparent decision‑making, such as fact verification and table structure analysis.

Introducing CoReTab

CoReTab addresses these issues by producing scalable, interpretable, and automatically verifiable annotations. The framework embeds multi‑step reasoning directly into Python code, allowing each reasoning step to be executed and validated programmatically.

Dataset Scale and Annotation Process

Using the CoReTab pipeline, the authors curated a dataset comprising 115,000 verified samples, each averaging 529 tokens per response. The annotations are generated automatically, ensuring consistency while reducing the manual effort typically required for large‑scale supervision.

Training Pipeline

The study fine‑tuned open‑source multimodal large language models (MLLMs) through a three‑stage training process that leverages the enriched CoReTab annotations. This approach aims to teach models not only to answer questions but also to produce executable reasoning traces.

Evaluation and Performance Gains

Evaluation across 17 MMTab benchmarks—covering table question answering, fact verification, and table structure understanding—revealed notable improvements. The CoReTab‑trained model outperformed MMTab‑trained baselines by +6.2% on question answering, +5.7% on fact verification, and +25.6% on structure understanding, while also delivering transparent reasoning paths.

Implications for Future Research

The authors argue that CoReTab establishes a robust supervision framework capable of enhancing multi‑step reasoning in multimodal contexts. By providing verifiable code‑based explanations, the framework may facilitate more reliable deployment of MLLMs in domains where auditability is essential.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via arXiv.