Quantization is widely used to deploy large language models, but its effect on uncertainty behavior, such as confidence, margins, and abstention, is rarely treated as a primary objective. We frame calibration-data selection for quantization as a *target-dependent uncertainty-preservation problem*. Different deployments emphasize different regions of the input distribution, yet prior work mainly optimizes accuracy-oriented compression metrics or adjusts scores after quantization. We formalize this goal with distributional and boundary preservation risks, and provide a simple mixture-mismatch argument explaining why no single calibration recipe should be expected to fit all targets. We introduce Doubt-Preserving Quantization (DPQ), a lightweight pre-quantization recipe family that uses full-precision predictions to construct target-aligned calibration mixtures of high-doubt examples and generic anchors. Across 8 language models, 9 NLP benchmarks, and 22 comparison methods, the leading recipe changes with the preservation target: DPQ-r75 leads on SQuAD2 answerability-boundary preservation, while broad multiple-choice QA favors a different set of milder or single-signal recipes, with confidence-only leading and variants such as entropy-only and DPQ-r50 outperforming the boundary-heavy DPQ-r75. These results show that calibration data should be selected for the specific full-precision score behavior a deployment needs to preserve, rather than treated as a fixed quantization detail. Code is available at https://github.com/xi-xiaoran/DPQ.

We present UNISON, a latent flow-matching framework that unifies speech generation, sound generation, and audio editing within a single model. A single model handles text-to-audio, text-to-speech, zero-shot speaker cloning, mixed speech-and-sound generation, scene-level audio editing, speech-in-scene editing, and timed audio composition, all of which share a single set of weights. Our architecture features two core designs: (1) Layer-wise deep LLM fusion, which injects hidden states from uniformly sampled layers of a frozen MLLM into corresponding MM-DiT blocks via learned projections, providing depth-varying semantic conditioning. This configuration achieves better T2A FD and CLAP than a persistent penultimate-layer conditioning baseline; and (2) a unified multi-task architecture where task identity is encoded solely by a channel-wise mask and source audio is provided through VAE-encoded channel concatenation. Training is stabilized by an online GPU-side multi-task data synthesis pipeline with task-homogeneous batching and a two-stage curriculum. With 621M–732M trainable generative parameters, UNISON achieves competitive results across the evaluated domains. Its trainable generative backbone uses 1.8–4.9x fewer parameters than the closest unified systems considered, while deployment additionally requires a frozen 7B text encoder. Audio samples and source code are publicly available.