Problem analysis
Physics problems often require logical reasoning by combining images (e.g., force diagrams, circuit diagrams) and formulas, but many multimodal models suffer from the problem that visual features are severed from semantic understanding, leading to problem solving errors.PhysUniBenchmark can be targeted to locate such flaws.
prescription
- Use of standardized test sets
(of a computer) runevaluate.pyWhen scripting, focus on cases of errors that contain a mixture of graphical questions (e.g., field distribution graphs + Maxwell's equations in electromagnetism) - Enhanced Feature Alignment
pass (a bill or inspection etc)preprocess.pyConverting images to structured descriptions (e.g. SVG vector data) to be fed into the model in parallel with text features - comparative verification
expense or outlayvisualize.pyGenerate accuracy comparison plots for different modal inputs to identify weaknesses
Implementation of recommendations
A step-by-step testing strategy is suggested: test text-only and image-only topics individually, then test multimodal topics, and determine the direction of improvement through error pattern analysis. Reference code for the fusion architecture of LSTM+CNN is provided in the project document.
This answer comes from the articlePhysUniBenchmark: benchmarking tool for multimodal physics problemsThe
May not be reproduced without permission:AI productivity tools " How to address the disconnect between visual and logical reasoning in physics problems with multimodal large models?
