arXiv subjects

Da Xing

Publications and source records attributed to Da Xing.

3 recordsLinked to original sources

arXiv2026

UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement

Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified capacity, we introduce UniEvo-VL, a self-evolving framework for multimodal models to learn from this constructive self-correction feedback during test-time compute. Instead of relying on a separate, often larger, teacher, we leverage their self-critiques as privileged information and ask a single multimodal model to act as both teacher and student with different contexts. The student only sees the vanilla question, while the teacher conditions on the privileged critique. Then training minimizes the per-state divergence between their denoising diffusion distributions over the student's own sampling trajectories. Experiments demonstrate that UniEvo-VL improves the image generation capabilities of multimodal models, while maintaining their sensitivity to additional reflection information. Specifically, we build on top of the open-source Qwen-image-2512 and observe a significant performance gain from 0.747 to 0.808 on GenEval and from 32.97 to 35.53 on GenEval2 Soft-TIFA. Moreover, attempts with more powerful external critics (e.g., GPT5.6-Luna) show that multimodal models with strong judge capabilities can anticipate a higher self-evolving ceiling. Last but not least, mixed text-rendering outcomes show that our self-improvements may not be uniform across different tasks. Our study aims to shed light on the current hot recursive self-improvement research line to enhance the user experience when using multimodal models without external supervision or guidance.

cs.AI↗

arXiv2026

NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training

Training a diffusion model involves two sources of randomness for each data sample: the timestep and the Gaussian noise realization. The timestep has been studied extensively through scheduling and weighting, whereas the impact of the noise realization at a given timestep is still underexplored. In this work, we examine whether different noise instances are equally informative. We introduce NoiseRater, a network that scores an individual noise instance conditioned on the data sample and timestep. The rater is learned through bilevel optimization, where its scores reweight the diffusion loss in the inner loop, and it is updated to reduce validation loss after the inner-loop updates. Using the trained rater to select training noise, we observe three properties of training noise. First, noise realizations at the same timestep are not equally useful: the rater's top-scored noise improves performance over i.i.d.\ sampling, while its bottom-scored noise degrades it. Second, this utility is contextual, depending jointly on the image, the class, and the timestep. Third, noise selection is complementary to timestep-level design, retaining most of its gain when combined with existing scheduling and weighting schemes. These findings establish instance-level noise valuation as a new axis for understanding and improving diffusion training. Code is available at https://github.com/JoeZhao527/Noise-Rater.

cs.LG↗

arXiv2022

Label free visualization of amyloid plaques in Alzheimer's disease with polarization-sensitive photoacoustic Mueller matrix tomography

The formation of amyloid plaques in the cortical and hippocampal brain regions caused by abnormal deposition of extracellular amyloid \b{eta}-protein (A\b{eta}) is a characteristic pathological hallmark of early Alzheimer's disease (AD), while label-free graphic rendering of diseased amyloid plaques in vivo is still a highly challenging task. Herein, by ingeniously extracting the polarization-sensitive optical absorption of amyloid plaques via photoacoustic (PA) technique, a novel PA Mueller matrix (PAMM) tomography that capable of providing three new conformational parameters of molecules is developed to realize depth-resolved label-free imaging of amyloid plaques. Whole brain PAMM imaging on different stages of APP/PS1 transgenic AD mice has been performed to demonstrate its ability for in situ/in vivo quantitative three-dimensional (3D) detection of amyloid plaques and its great potential for monitoring early AD pathological development without labeling.

physics.med-ph↗