  The design and implementation of error correcting codes has long been
informed by two fundamental results: Shannon's 1948 capacity theorem, which
established that long codes use noisy channels most efficiently; and Berlekamp,
McEliece, and Van Tilborg's 1978 theorem on the NP-hardness of decoding linear
codes. These results shifted focus away from creating code-independent
decoders, but recent low-latency communication applications necessitate
relatively short codes, providing motivation to reconsider the development of
universal decoders.
  We introduce a scheme for employing binarized symbol soft information within
Guessing Random Additive Noise Decoding, a universal hard detection decoder. We
incorporate codebook-independent quantization of soft information to indicate
demodulated symbols to be reliable or unreliable. We introduce two decoding
algorithms: one identifies a conditional Maximum Likelihood (ML) decoding; the
other either reports a conditional ML decoding or an error. For random
codebooks, we present error exponents and asymptotic complexity, and show
benefits over hard detection.
  As empirical illustrations, we compare performance with majority logic
decoding of Reed-Muller codes, with Berlekamp-Massey decoding of
Bose-Chaudhuri-Hocquenghem codes, with CA-SCL decoding of CA-Polar codes, and
establish the performance of Random Linear Codes, which require a universal
decoder and offer a broader palette of code sizes and rates than traditional
codes.
