Large language models (LLMs) exhibit significant layer-wise redundancy, motivating non-uniform pruning for efficient compression. However, existing sparsity allocation methods rely on indirect statistical heuristics derived from weight magnitudes or activation distributions, without directly assessing how robustly each layer preserves semantic information under input variation. We propose PerturbPrune, a framework that allocates layer-wise sparsity by probing the intrinsic stability of hidden representations. PerturbPrune estimates the principal subspace of each layer’s activations via PCA and injects structured perturbations along these directions. Layer sensitivity is quantified through matrix difference entropy (DiME): stable layers are identified as encoding compact, essential features and thus preserved, while highly sensitive layers are deemed over-parameterized and pruned more aggressively. Extensive experiments across diverse LLM families (LLaMA, LLaMA-2, Qwen, OPT) demonstrate that PerturbPrune broadly matches or outperforms existing non-uniform methods. Compared to uniform pruning, it prevents catastrophic collapse, reducing perplexity by up to 99.7% and improving zero-shot accuracy by up to 22.7%. The method is also highly efficient, completing sparsity allocation on Qwen3.5-9B in 7 minutes—achieving 24× and 17.4× speedups over AlphaPruning and MRP. Finally, PerturbPrune generalizes to Mixture-of-Experts architectures, achieving up to 19.3% perplexity reduction on Qwen3.5-35B-A3B.

Large Language Models (LLMs) often fail to recognize fallacious reasoning in real-world interactions, despite strong performance on static fallacy detection tasks. We define this ability as fallacy awareness, the capacity to autonomously perceive and resist fallacies in dynamic, pragmatic contexts. To study this, we introduce ISFallacy, a large-scale Chinese benchmark of 50K interactive scenarios spanning six fallacy types, five social interaction settings, diverse role relationships, and personality traits. We further propose FATE, a two-stage evaluation framework that assesses fallacy awareness without explicit cues, combining natural dialogue responses and reasoning-based decisions. Experiments on five representative LLMs reveal a substantial gap between fallacy classification and awareness, with models particularly vulnerable to emotion-driven fallacies and scenarios involving cooperative or trust-based relationships. Deeper analysis uncovers a cognition–behavior gap and fragile internal representations underlying awareness failures. Our work establishes a foundation for evaluating and enhancing the robustness of LLMs against fallacious reasoning in interactive settings.