MMathCoT-1M: Multimodal Mathematics Chain-of-Thought Reasoning Data

A collection of multimodal mathematics problems and reasoning chains presented in the URSA research paper. The dataset was created by the URSA-MATH organization and was last updated on February 18, 2025. It likely contains over one million examples integrating visual and textual data for training and evaluating AI models.

Use Cases

• Training multimodal models for mathematical problem-solving based on integrated image and text data.
• Benchmarking AI performance on chain-of-thought reasoning tasks in mathematics.
• Studying the alignment between model-generated reasoning and human-like step-by-step solutions.
• Developing verification methods for the logical correctness of AI reasoning chains.

Strengths

• Dataset is associated with a peer-reviewed research paper (URSA), suggesting a research-grade foundation.
• Last update timestamp is precise (2025-02-18 08:29:51), indicating recent maintenance.
• Aggregates data from multiple established sources (MAVIS, Multimath, Geo170k), suggesting breadth of coverage.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

URSA-MATH organization, aggregating data from MAVIS, Multimath-300k, and Geo170k.

Collection Method

Created for research on understanding and verifying chain-of-thought reasoning in multimodal mathematics.

Freshness

Last updated 2025-02-18 08:29:51.