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LLMs have shown impressive capabilities in reasoning tasks like Chain-of-Thought (CoT), enhancing accuracy and interpretability in complex problem-solving. While researchers are extending these capabilities to multi-modal domains, videos present unique challenges due to their temporal dimension. Unlike static images, videos require understanding dynamic interactions over time. Current visual CoT methods excel with static inputs but…

Autoregressive visual generation models have emerged as a groundbreaking approach to image synthesis, drawing inspiration from language model token prediction mechanisms. These innovative models utilize image tokenizers to transform visual content into discrete or continuous tokens. The approach facilitates flexible multimodal integrations and allows adaptation of architectural innovations from LLM research. However, the field has…

Converting complex documents into structured data has long posed significant challenges in the field of computer science. Traditional approaches, involving ensemble systems or very large foundational models, often encounter substantial hurdles such as difficulty in fine-tuning, generalization issues, hallucinations, and high computational costs. Ensemble systems, though efficient for specific tasks, frequently fail to generalize due…

Multimodal reasoning is an evolving field that integrates visual and textual data to enhance machine intelligence. Traditional artificial intelligence models excel at processing either text or images but often struggle when required to reason across both formats. Analyzing charts, graphs, mathematical symbols, and complex visual patterns alongside textual descriptions is crucial for applications in education,…