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- PaperarXiv — Language & NLP (cs.CL)12 May 2026
Allegory of the Cave: Measurement-Grounded Vision-Language Learning
Kepeng Xu, Li Xu, Gang He, Wenxin Yu
This paper proposes measurement-grounded vision-language learning, using RAW camera data instead of standard RGB images to improve model grounding. The method, PRISM-VL, achieves higher BLEU, ROUGE-L, and LLM-Judge accuracy by preserving sensor evidence lost during RGB rendering. While not directly about education, the approach could inform multimodal learning tools for language teaching.
Original abstract
Vision-language models typically reason over post-ISP RGB images, although RGB rendering can clip, suppress, or quantize sensor evidence before inference. We study whether grounding improves when the visual interface is moved closer to the underlying camera measurement. We formulate measurement-grounded vision-language learning and instantiate it as PRISM-VL, which combines RAW-derived Meas.-XYZ inputs, camera-conditioned grounding, and Exposure-Bracketed Supervision Aggregation for transferring supervision from RGB proxies to measurement-domain observations. Using a quality-controlled 150K instruction-tuning set and a held-out benchmark targeting low-light, HDR, visibility-sensitive, and hallucination-sensitive cases, PRISM-VL-8B reaches 0.6120 BLEU, 0.4571 ROUGE-L, and 82.66\% LLM-Judge accuracy, improving over the RGB Qwen3-VL-8B baseline by +0.1074 BLEU, +0.1071 ROUGE-L, and +4.46 percentage points. These results suggest that part of VLM grounding error arises from information lost during RGB rendering, and that preserving measurement-domain evidence can improve multimodal reasoning.