Post-training quantization (PTQ) of instruction-tuned LLMs to 3 bits often destroys safety alignment, motivating safety-protection methods that keep a small budget of "safety-critical" weight blocks in full precision. We give a theoretical account of the many scoring rules proposed to choose those blocks. We prove that under symmetric group-wise PTQ the Taylor (SSMP) scorer concentrates to the gradient magnitude times a per-group constant; the gradient-times-weight (SNIP) scorer is empirically near-identical, so the entire compound family 𝛼|g⋅ w|+𝛽|g⋅𝛿 w| collapses to a single block ranking (worst-case Spearman 0.9971), and only pure magnitude or pure Fisher selects different blocks. We further show that recovered refusal behavior under small budgets is decoupled from the underlying safety mechanism: refusal can be restored while capability, adversarial robustness, and over-refusal balance stay at the unprotected level; at 3 bits heuristic attack metrics conflate incoherence with harm; and on weakly-aligned models protection raises classifier-judged attack success above the unprotected floor. Experiments on five open-weight 8–9B models across 110 conditions, including classifier-judged GCG and PAIR attacks and activation-level analysis, confirm every prediction; a four-category classification predictable from one baseline metric explains the cross-model variation. Code and configurations: https://github.com/jc-su/Safepress.

The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image (T2I) models. However, to generate high-quality images, users must still craft detailed prompts specifying scene, style, and context—often through multiple rounds of refinement. We propose PromptSculptor, a novel multi-agent framework that automates this iterative prompt optimization process. Our system decomposes the task into four specialized agents that work collaboratively to transform a short, vague user prompt into a comprehensive, refined prompt. By leveraging Chain-of-Thought (CoT) reasoning, our framework effectively infers hidden context and enriches scene and background details. To iteratively refine the prompt, a self-evaluation agent aligns the modified prompt with the original input, while a feedback-tuning agent incorporates user feedback for further refinement. Experimental results demonstrate that PromptSculptor significantly enhances output quality and reduces the number of iterations needed for user satisfaction. Moreover, its model-agnostic design allows seamless integration with various T2I models, paving the way for industrial applications.