Emergent Symbolic Organization in Sparse Binary Visual Representations

Symbolic organization is often associated with language, but whether it can emerge within visual recognition without linguistic supervision remains unclear. By imposing a sparse binary bottleneck on ResNet-50, we represent each image as a set of active bits from a shared vocabulary of 2048 binary visual units and show that symbolic organization can emerge from image-level supervision alone. The binary representation activates only 24.63 bits per image on average, corresponding to 98.8% sparsity, while retaining 78.54% Top-1 accuracy on ImageNet. This shared vocabulary exhibits systematic reuse, category-structured combinations, and a broad Zipf-like regime in its rank–frequency curve, together with functionally differentiated contributions to recognition. To probe the semantics of individual bits, we map the 2048 bits to learnable embeddings and use them to condition a frozen Stable Diffusion 1.5 model. The experiments show that complete active-bit sets generate semantically recognizable visual content. Some individual bits produce stable semantic effects, including concepts beyond the ImageNet label taxonomy, and independently probed bits can combine into coherent configurations beyond observed ImageNet compositions. At least a subset of the learned visual bits therefore exhibits multiple properties characteristic of symbolic units, providing an empirical setting for studying how symbolic organization can emerge in perceptual neural systems. Code will be made publicly available.