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Agents can now reliably navigate evolving environments by maintaining a probabilistic 4D belief that accounts for hidden target movements and revises predictions based on intermittent, visibility-aware observations.
Scaling task complexity across 280 standardized manipulation variants reveals that today's leading VLA models fundamentally collapse under fine-grained spatial ambiguity and memory-intensive sequential planning.
Treating robot skill calls as runtime-verified execution proposals rather than blind action predictions enables self-correcting VLA autonomy that reaches 97.4% success on LIBERO.