Reasoning large language models that produce explicit chains of thought are a new jailbreak surface: an adversary who shapes intermediate reasoning can hijack safety deliberation and elicit harmful answers the same model would refuse in a single forward pass. Defenses inherited from chat-style alignment monitor only the final response and intervene after the model has already committed to an unsafe trajectory. We argue that defending reasoning models requires looking inside the thinking phase itself, and we show that the refusal direction, previously characterized as a single linear direction in chat models, in fact evolves along the reasoning trajectory: sharp at onset, slowly drifting during long deliberation, and on hijacked traces collapsing at an identifiable step well before any harmful token is emitted. We propose RsMon, a step-level refusal-subspace monitor that tracks the projection of intermediate hidden states onto a calibrated refusal subspace and triggers a minimally invasive geometric intervention once drift exceeds a learned threshold. The monitor is purely inference-time, requires no retraining, and is compatible with any open-weight reasoning model. On three open reasoning models, five attack families, and four utility benchmarks, RsMon cuts mean attack success from 80.1% to 19.2%, a 4.2× reduction, while keeping MATH-500, GPQA-Diamond, and HumanEval pass@1 within 0.7 absolute points of the undefended baseline. The same monitor produces a step-resolved diagnostic that pinpoints when and in which layer safety deliberation breaks down. Our code is available at https://github.com/llmnjust-afk/RsMon.

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which is publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art detection baselines and generates high-quality explanations from diverse evaluation perspectives.

Nested Named Entity Recognition (NNER) has been extensively studied, aiming to identify all nested entities from potential spans (i.e., one or more continuous tokens). However, recent studies for NNER either focus on tedious tagging schemas or utilize complex structures, which fail to learn effective span representations from the input sentence with highly nested entities. Intuitively, explicit span representations will contribute to NNER due to the rich context information they contain. In this study, we propose a Hierarchical Transformer (HiTRANS) network for the NNER task, which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner. Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network. Then a label prediction layer is designed to recognize nested entities hierarchically, which naturally explores semantic dependencies among different spans. Experiments on GENIA, ACE-2004, ACE-2005 and NNE datasets demonstrate that our proposed method achieves much better performance than the state-of-the-art approaches.