In asynchronous grant-free access, receivers must detect variable-length transmissions and identify their start and end. Existing decoder-aided receivers use start and tail sequences which are as long as one codeword, causing substantial overhead for short commands. This paper treats the boundary (i.e., start and tail sequences) length as an independent design parameter and proposes a convolutional neural network that detects both boundaries from the received waveform and the decoder soft output. Its waveform input size decreases with the boundary length, while the decoder input keeps the codeword length, so the boundary can be shortened without altering the decoder. Analysis and simulations quantify the resulting tradeoff among payload efficiency, collision exposure, and boundary detection. The results show that the proposed receiver can exploit very short boundaries: in the considered setting, the shortest tested boundary, $L_{\mathrm{b}}=16$, yields the highest effective payload efficiency at all tested loads, whereas a correlation-based benchmark with the same sequences performs best with $L_{\mathrm{b}}=64$ at moderate and high loads.

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