In self-consuming generative models that train on their own outputs, alignment with user preferences becomes a recursive rather than one-time process. In this paper, we provide the first formal foundation for analyzing the long-term effects of such recursive retraining on alignment. Under a two-stage curation mechanism based on the Bradley–Terry (BT) model, we model alignment as an interaction between two factions: the Model Owner, who filters which outputs should be learned by the model, and the Public User, who determines which outputs are ultimately shared and retained through interactions with the model. Our analysis reveals three structural convergence regimes: consensus collapse, compromise on shared optima, and asymmetric refinement, depending on the degree of preference alignment. We prove a fundamental impossibility theorem: no recursive BT-based curation mechanism can simultaneously preserve diversity, ensure symmetric influence, and eliminate dependence on initialization. Framing the process as dynamic social choice, we show that alignment is not a static goal but an evolving equilibrium shaped by power asymmetries and path dependence.

Diffusion models have become essential generative tools for tasks such as image generation, video creation, and inpainting, but their high computational and memory demands pose challenges for efficient deployment. Contrary to the traditional belief that full-precision computation ensures optimal image quality, we demonstrate that a fine-grained mixed-precision strategy can surpass full-precision models in terms of image quality, diversity, and text-to-image alignment. However, directly implementing such strategies can lead to increased complexity and reduced runtime performance due to the overheads of managing multiple precision formats and casting operations. To address this, we introduce DM-Tune, which replaces complex mixed-precision quantization with a unified low-precision format, supplemented by noise-tuning, to improve both image generation quality and runtime efficiency. The proposed noise-tuning mechanism is a type of fine-tuning that reconstructs the mixed-precision output by learning adjustable noise through a parameterized nonlinear function consisting of Gaussian and linear components. Key steps in our framework include identifying sensitive layers for quantization, modeling quantization noise, and optimizing runtime with custom low-precision GPU kernels that support efficient noise-tuning. Experimental results across various diffusion models and datasets demonstrate that DM-Tune not only significantly improves runtime but also enhances diversity, quality, and text-to-image alignment compared to FP32, FP8, and state-of-the-art mixed-precision methods. Our approach is broadly applicable and lays a solid foundation for simplifying complex mixed-precision strategies at minimal cost.

With the emergence of technologies like smart cities, Internet of things (IoT), and 5G, the amount of produced visual data at the edges and remote nodes has exploded. Since for a considerable portion of the captured video the target is a machine learning task, rather than a human audience, transmission of videos in such applications requires efficient video compression tailored for machine vision. However, existing compression solutions are optimized for human vision. This paper presents a methodology to optimize an existing video compression standard, HEVC, for a machine vision task, Object Detection (OD). To this end, (1) a dataset of compressed videos, including several compression-ratios and their corresponding OD performance is collected to enable modeling, (2) A trade-off point (knee-point) between bitrate and OD performance is defined, that finds the point after which no major improvements will be achieved, (3) a set of features were extracted and studied to model this point, via a practical machine learning method. The resulting solution can predict the knee-point with$\boldsymbol{\text{MAE}=1.28}$, resulting in a ∆Recall of only 0.012 and bitrate reduction of 86.56%, compared to OD with very high-quality video.