Multimodal large language models (MLLMs) are now used as zero-shot judges of image quality, but their color judgments are rarely tested with colorimetric ground truth. We present a five-task benchmark based on the CIE colorimetric framework: AWB pair judgment, skin-tone bias direction, CCT estimation, CIE Delta E 2000 pair ordering, and color naming under changing illuminants. We test eight MLLM endpoints, including four Qwen3-VL variants, GLM-4.5V, GPT-5.4, Claude-Opus-4.7, and Gemini-3.1-Pro. The models often solve coarse binary AWB judgments, but they remain weak on numeric CCT estimation and on some fine color-difference judgments. On ColorChecker-24 stimuli, the three closed-weight frontier models reach CCT MAE of 715-1056 K, compared with 1358-2133 K for the five open-weight models. A non-ColorChecker control shows that this advantage is partly template-dependent. The results suggest that current MLLMs have useful color words and coarse visual priors, but their color judgments are not reliably calibrated to CIE-computable color differences.
Modern cameras correct color via two or three pre-calibrated 3x3 matrices interpolated by Correlated Color Temperature (CCT). This collapses the illuminant SPD to a scalar. LEDs, fluorescents, and mixed sources sit off the Planckian locus, and metameric illuminants with the same chromaticity demand different optimal CCMs that a CCT pipeline cannot distinguish. We recover the illuminant SPD instead using non-negative least squares against a basis dictionary, conditioned on the camera white point and a multi-channel ambient-light sensor (ALS). A closed-form 3x3 CCM is then fit per shot. On a synthetic benchmark of 300 illuminants and 3,100 reflectances, mean angular error drops from 1.89 to 1.20 degrees, with a 1.7x tail reduction on the LED-like off-Planckian group (n=45) where metameric ambiguity is tangible. The gain holds under realistic ALS imperfections and observation noise, and the same off-Planckian advantage carries over to 1,684 real measured SPDs.
Zhiyuan Fan Hong Kong University of Science and Technology, Guanqiao Chen University of Science and Technology of China, Yanyi Huang, Mingkuan Zhao, Dadi Guo, Yi R. Fung
Large language models (LLMs) have shown strong performance on hard reasoning and general instruction-following tasks. However, when sampling multiple outputs for the same prompt, they often produce highly homogeneous, repetitive responses, resulting in inefficient exploration. This limits the gains from test-time scaling and constrains the upper bound of RL training. We attribute this issue in part to supervised fine-tuning (SFT): when a single prompt is paired with multiple reference responses, the model is trained to generate diverse outputs under the same prior condition, which induces optimization interference and can lead to diversity collapse. To address this, we propose Prefix-Conditioned SFT (P-SFT), a simple yet effective method that constructs semantically consistent yet distributionally distinct prior contents to different responses, thereby projecting the instruction into distinct latent regions to establish diverse prior distributions and decouple the one-to-many mapping. Experiments on large reasoning language models show that our approach improves absolute performance by 5.3% and increases generation diversity by 198.3% on average, while substantially enhancing output diversity and test-time scaling. Notably, even without any additional training, our prefixing strategy can be applied at inference time alone and still yields significant gains in both diversity and reasoning performance for instruction-tuned LLMs and reasoning-enhanced models.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表