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.

As large language models (LLMs) improve, many applications are moving from a single LLM call to multi-agent systems. These systems often rely on either hand-designed or automatically optimized workflows with multiple verification and testing steps. While those extra steps can improve accuracy, they also increase latency and token costs. In practice, many queries do not need such heavy processing and can be handled well by a single strong agent.To address this inefficiency, we propose LLM-as-Scheduler (LAS), a system that dynamically chooses the right workflow for each query. LAS uses a two-stage cascade: first, a lightweight gate quickly evaluates each agent’s output; then, an LLM-based scheduler uses query features and gate signals to make more detailed routing decisions. Experiments show that LAS cuts token usage by 43% and reduces end-to-end latency by more than 36%, while causing at most a 1.4 percentage-point drop in accuracy compared with a strong fixed workflow.

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.