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Northeastern University
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Achieving over 134% accuracy gains with just 1,000 trainable parameters reveals a game-changing approach to enhancing spatial reasoning in vision language models.
Flash-WAM achieves real-time inference for world-action models by reducing latency from 8.1 seconds to 348 milliseconds without sacrificing performance.
