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VLMs can achieve state-of-the-art Vision-Language Navigation performance by explicitly training them to reason about past actions and predict future visual transitions.
Robots can now navigate complex outdoor environments using only high-level human instructions and readily available GPS/map data, bypassing the need for expensive HD maps or limited short-horizon policies.
The fragmented field of world modeling can now be unified under a "levels x laws" taxonomy, revealing critical gaps in autonomous model revision and decision-centric evaluation.
