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CogAI Lab
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VLA models retain basic knowledge but falter on complex semantics, revealing critical gaps in their adaptation from VLMs.
Isolating recurrence in VLA models reveals it can double success rates in partially observable tasks without complicating architecture or introducing auxiliary losses.
