The recent success of large vision language models shows great potential in driving the agent system operating on user interfaces.
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To create rich visualizations, data analysts often need to iterate back and forth among data processing and chart specification to achieve their goals.
We scale a proof-of-concept model to 3. 5 billion parameters and 800 billion tokens.
Despite notable advancements in Retrieval-Augmented Generation (RAG) systems that expand large language model (LLM) capabilities through external retrieval, these systems often struggle to meet the complex and diverse needs of real-world industrial applications.
Second, leveraging the physical principle of light transport independence, we apply linear blending between the source video's appearance and the relighted appearance, using a Progressive Light Fusion (PLF) strategy to ensure smooth temporal transitions in illumination.
The key idea is simple: factorize the text-to-video generation task into two separate easier tasks for diffusion step distillation, namely text-to-image generation and image-to-video generation.
We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents.
Reasoning is a fundamental capability of Large Language Models.
Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains.
We implement a custom kernel that performs the matrix multiplications and the log-sum-exp reduction over the vocabulary in flash memory, making global memory consumption for the cross-entropy computation negligible.