Recent LLMs increasingly rely on explicit reasoning mechanisms such as Chain-of-Thought, but these reasoning traces also create a new attack surface for inference-time backdoors that steer the model toward malicious reasoning paths without modifying its parameters. Such attacks can remain linguistically coherent, making them difficult to detect with surface-, consistency-, or confidence-based signals. We propose STAR, a lightweight detection framework that compares input-conditioned token probabilities with prior-only reasoning probabilities. STAR measures token-level prior-posterior amplification and applies CUSUM to identify persistent anomalies in the generated reasoning process. Experiments across four LLMs (8B-70B), five reasoning benchmarks, and three inference-time backdoor attacks show that STAR achieves strong detection performance, with an average AUROC of 0.980, and substantially lower TFLOPs than high-overhead baselines such as Onion. STAR also remains effective under top-k logit access and the evaluated burn-in-aware adaptive attacks, while exposing a trade-off between evasion attempts and attack success.

Citation context analysis (CCA) is a field of research studying the role and purpose of citation in scientific discourse. While most of the efforts in CCA have been focused on elaborate characterization schemata to assign function or intent labels to individual citations, the citation context as the basis for such a classification has received rather limited attention. This relative neglect, however, has led to the prevalence of vague definitions and restrictive assumptions, limiting the citation context in its expressiveness. It is a common practice, for example, to restrict the context to the citing sentence. While this simple context conceptualization might be sufficient to assign intent or function classes, it fails to cover the rich information of scientific discourse. To address this concern, we analyze the context conceptualizations of previous works and, to our knowledge, construct the first comprehensive context definition based on the semantic properties of the citing text. To evaluate this definition, we construct and publish the FineCite corpus containing 1,056 manually annotated citation contexts. Our experiments on established CCA benchmarks demonstrate the effectiveness of our fine-grained context definition, showing improvements of up to 25% compared to state-of-the-art approaches. We make our code and data publicly available at https://github.com/lab-paper-code/FineCite.