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Best-in-class open omni-modal reasoning model delivers the highest efficiency and accuracy to power agentic workflows such as computer use, document intelligence and audio-video reasoning. AI agent systems today juggle separate models for vision, speech and language — losing time and context as they pass data from one model to the other. Unveiled today, NVIDIA Nemotron […]
We propose a system for synthetic data generation to train AI systems to visually follow any route on any map, finally teaching language models to navigate our world. Look at a map of a shopping mall or a theme park. Within seconds, your brain processes the visual information, identifies your location, and traces the optimal […]
Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for maintaining consistency over prolonged multi-session dialogues. We introduce Mem0, a scalable memory-centric architecture that addresses this issue by dynamically extracting, consolidating, and retrieving salient information from ongoing conversations. Building on this foundation, we further propose an […]
We introduce ConvApparel, a new human-AI conversation dataset and a comprehensive evaluation framework designed to quantify the “realism gap” in LLM-based user simulators and improve the training of robust conversational agents. Modern conversational AI agents can typically handle complex, multi-turn tasks like asking clarifying questions and proactively assisting users. However, they frequently struggle with long […]