Sophon: A Procedural Diagnostic for Spatial Reasoning in Vision-Language Models

Open-weight Vision-Language Models (VLMs) score 85--94\% on existing spatial benchmarks (BLINK Spatial, What's Up CLEVR) yet collapse to chance on simple geometric questions when object, scene, and texture priors are stripped away. We introduce and open-source Sophon, a procedurally generated diagnostic of paired colored point-cloud stimuli across three coordinate systems (1D linear, 2D Cartesian, 2D polar) and four question types (binary, categorical, comparative, quantitative). Across four open architectures spanning every major projector design (LLaVA-NeXT, LLaVA-OneVision, Qwen2.5-VL, Idefics3), accuracy clusters at 47--52\% while frontier models (Opus 4.6, Gemini 2.5 Pro, GPT-5.4) clear 77--83\% and humans reach 84--94\%. We trace the open-weight failure end-to-end. The vision tower works: encoder probes recover Sophon spatial direction at 80--97\%. The language tower's reasoning circuit works: a text-only control restores $+40$ pp, and probes preserve the spatial signal through every LLM decoder layer at 80--86\%. Per-layer attention attribution localizes visual integration to mid-stack layers. Yet at the readout, the model elevates the correct answer-set tokens but selects among them at chance. Fine-tuning experiments rule out the unembedding as the mechanism. By moving the unembedding layer through the residual stream, we observe the emergence of a small but discriminative contrast at late-stack residual layers in fine-tuned models, which in practice converts the random selection into a confident one more likely to be correct. However, ablation experiments locate the mechanism upstream: training only the upstream layers with the late stack frozen recovers the full $+29$ pp gain, while training only the late stack captures less than a third. The late-stack contrast is the downstream readout of upstream changes. No layer in either base or fine-tuned models shows geometric alignment between the spatial signal and the unembedding's vocabulary axes, suggesting that 7B-class VLMs have the weights to reason spatially but lack the residual-stream pathways to convert that reasoning into answers.