Data for reasoning agents in dynamic environments
LLMs and AI agents trained on static data struggle with spatial-temporal understanding, memory, causality, and multi-step reasoning in dynamic environments.
Nitrode builds fully specified game environments that generate ground-truth state-transition data to train and evaluate reasoning agents on spatial and temporal dynamics.
AI research teams and developers building or evaluating LLMs and autonomous agents.
Co-founder at Nitrode. Infosci @ Cornell. Previously Data and VC/Corporate @ Wilson Sonsini.
Co-founder at Nitrode. CS & Product Design @ Stanford. Top 500 GeoGuessr globally in moving and builder of unnecessarily ambitious Minecraft cities.
Co-founder at Nitrode. CS @ Cornell. Previously Data Scientist @ Nexon