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CogAI Lab
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VLA models retain basic knowledge but falter on complex semantics, revealing a critical gap 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.
