Data for reasoning agents in dynamic environments
Nitrode builds high-quality game data to train and evaluate LLMs and agents on spatial and temporal reasoning. Today’s models are trained on static text and images, but struggle to understand how the world evolves over time. We create small, fully specified game environments that generate ground-truth data on state, transitions, and hidden dynamics. This enables AI systems to move beyond simple pattern matching, incorporating memory, causality, and multi-step reasoning to create more reliable agents in dynamic environments.
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