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IndustrialVLA-Bench is presented, an evidence-aware evaluation of six released VLA and WAM systems under a unified reporting schema that evaluates clean capability on LIBERO, non-language robustness on LIBERO-Plus, instruction sensitivity on LIBERO-Para, and observed execution cost.
Static skip connections leave critical fidelity on the table: dynamically recalibrating high-resolution features using evolving decoder context cuts reconstruction error in 90% of cases for barely a 0.1% parameter overhead.
Discrete differentiation of gradient descent reveals unexpected curvature behaviors that challenge conventional stability assumptions in ReLU training.
