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October 20, 2025Open Access

Visualizing Thought: Conceptual Diagrams Enable Robust Planning in LMMs

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Proposed framework boosts reasoning capabilities of LMMs using conceptual diagrams, suggesting substantial performance gains.

Method improves reasoning in large multimodal models by integrating conceptual diagrams and textual descriptions.
Performance improved significantly in planning tasks, with GPT-4o achieving 90.2% accuracy in Blocksworld.
Approach utilized optimized graph-of-thought inference, enabling complex multi-step reasoning without human input.
Demonstrates substantial advantages over traditional text-only reasoning methods in challenging planning domains.

Borazjanizadeh et al. (2025) studied this question.

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October 20, 2025Open Access

View Full Paper

Ask AI

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Authors

Proposed framework boosts reasoning capabilities of LMMs using conceptual diagrams, suggesting substantial performance gains.

Method improves reasoning in large multimodal models by integrating conceptual diagrams and textual descriptions.
Performance improved significantly in planning tasks, with GPT-4o achieving 90.2% accuracy in Blocksworld.
Approach utilized optimized graph-of-thought inference, enabling complex multi-step reasoning without human input.
Demonstrates substantial advantages over traditional text-only reasoning methods in challenging planning domains.

Borazjanizadeh et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark