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Ego-motion ambiguity in VLMs can lead to severe spatial reasoning failures, but a new benchmark and framework show how to ground visual representations in 3D space effectively.
TransPhy achieves unprecedented accuracy in physically grounded image editing, outperforming traditional methods by improving rule adherence and generalization to novel transformations.
LLMs can substantially improve their ability to follow complex instructions and constraints by explicitly auditing their own context adherence before answering.
Autonomous vehicles can drive more safely and reliably by grounding LLM reasoning in a "Commonsense World" that quantifies and leverages the trustworthiness of LLM outputs.
