Post-training pruning compresses Large Language Models (LLMs) without full-model retraining, but performance degrades sharply at high sparsity. We identify two limitations in existing methods: local importance estimates miss global spectral structure, and uniform within-block sparsity ignores heterogeneous sensitivity across transformer projections. We propose PRISM, a post-training pruning framework with training-free mask construction and optional fixed-mask reconstruction optimization. PRISM augments activation-based scoring with two adaptive-rank SVD-derived signals for principal-subspace and residual-aware importance, combines the factors through parameter-free CV-based fusion, and allocates sparsity non-uniformly across attention and MLP projections using a gradient-weighted matrix-level sensitivity proxy. Across six Qwen3 and LLaMA models, PRISM consistently outperforms existing pruning baselines, yielding up to 16.8% relative improvement in WikiText-2 perplexity under the evaluated sparsity settings.

We present Copyright Detective, the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. The system treats copyright infringement versus compliance as an evidence discovery process rather than a static classification task due to the complex nature of copyright law. It integrates multiple detection paradigms, including content recall testing, paraphrase-level similarity analysis, persuasive jailbreak probing, and unlearning verification, within a unified and extensible framework. Through interactive prompting, response collection, and iterative workflows, our system enables systematic auditing of verbatim memorization and paraphrase-level leakage, supporting responsible deployment and transparent evaluation of LLM copyright risks even with black-box access. In our experiments with GPT-4o-mini, we demonstrate that the specific persuasive strategy “Pathos” shifts the leakage distribution from about 0.1 (ROUGE-L) to 0.7. Our live system is hosted on Streamlit server, with a demonstration video included as supplementary material.