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The process of predicting answers to difficult questions by decoupling logical justification from the final output.
Distinct from Complex Type Querying: Shortlist candidates focus on database query complexity or security, not AI model inference of answers.
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This project is a multimodal large language model reasoning framework designed to train and evaluate models in performing chain-of-thought reasoning across text and image data. It provides a reasoning engine and training system that enable vision-language models to generate step-by-step logical rationales and final answers for complex queries. The framework utilizes a two-stage training pipeline that decouples the generation of logical justifications from final answer inference. It transforms visual data into descriptive text through image captioning and uses vision-transformer feature extrac
Predicts answers to difficult questions by separating the generation of logical justifications from the final output.