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This work introduces a novel quantum incremental learning framework based on trainable mixed-state prototypes that achieves high-dimensional feature concentration using a minimal number of qubits, while demonstrating lower computational complexity and robust representation in incremental learning tasks compared with cl...
FibVLA is introduced, an efficient framework featuring temporal perception of long-context history and logarithmic hindsight sampling to both proprioceptive states and visual frames to capture long-term temporal dependencies with minimal redundancy.
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