Do Foundation Models Know Geometry? Probing Frozen Features for Continuous Physical Measurement

Vision-language models encode continuous geometry that their text pathway fails to express: a 6,000-parameter linear probe extracts hand joint angles at 6.1 degrees MAE from frozen features, while the best text output achieves only 20.0 degrees -- a 3.3x bottleneck. LoRA fine-tuning (r=16, 2,000 images) narrows this gap to 6.5 degrees, providing evidence for a pathway-training deficit rather than a representational one. Training objective determines accuracy more than architecture: five encoders

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ethics.ai (6 March 2026), “Do Foundation Models Know Geometry? Probing Frozen Features for Continuous Physical Measurement,” evidence record 7586, https://ethics.ai/record/7586 (originally published by arXiv).

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