Diagnosing Learner–Codec Mismatch in Low-Bit Retrieval

Retrieval comparisons at a fixed code rate can depend on the export codec as well as the learner. On Stanford Online Products, FIT-only range calibration improves frozen twenty-dimensional four-bit triplet descriptors by 14.22 average-precision points at the same ten-byte payload, changing the allocation effect against ten-dimensional eight-bit descriptors from -5.61 to +8.50 points. At common four-bit output, a historical four-bit-training advantage falls from +9.74 to -0.36 points; learned-scale DINOv2 and SigLIP2 contrasts have conditional intervals including zero. Product quantization fitted separately to each learned representation supplies a further comparison. In one additional paired training realization, native contrastive descriptors trail triplet descriptors by 2.081 points, whereas CAL-selected ten-byte codecs favor contrastive descriptors by 1.854 points; both adjusted intervals exclude zero. Range calibration alone also reverses this pair's learner ordering. The finding concerns codec-dependent rankings on an exposed population, not universal contrastive superiority. Training-step-preserving and range-reset exports remain distinct, and CUB retains adverse allocation comparisons. These controls diagnose learner–codec compatibility without establishing a new quantizer, equivalence, or a minimum-bit rule.