Silent Alarm: A J-Space Protocol for Comparing Danger Recognition Across Models and Quantization Levels

Jailbreak-robustness research typically evaluates safety through generated responses using an LLM-as-judge approach. Such evaluations, however, are sensitive to the benchmark's grading procedure and capture only observed behavior on a given set of attacks, without directly revealing the hidden fragility of the underlying safety mechanisms. This work proposes JADR (Jacobian Assessment of Danger Recognition), a protocol that measures a model's internal representation through Jacobian space (J-space, a recently proposed workspace of verbalizable concepts) before the first response token is generated. For every prompt and layer we record the top-k J-space tokens; these are grouped into six behavioral scenario axes and compared between a danger sample based on StrongREJECT and a safe control drawn from XSTest and OKTest. The method does not call on an external judge model: the computation runs entirely locally, on the activations of the model under evaluation, which lets us compare both different models against each other and modifications of a single model -- quantization and fine-tuning in particular -- on the same terms. The final comparison rests on the proposed SafetyAUC metric, complemented with bootstrap confidence intervals. The protocol is applied to six models (Qwen3-1.7B, Qwen3-4B, Qwen3-8B, Qwen3-Uncensored-4B, Qwen3-SafeRL-4B, Gemma 2 9B) across three weight-representation regimes -- BF16, INT8, and INT4 -- and checked against an independent behavioral evaluation with the StrongREJECT grader. The metric separates models with a strong versus a weak internal safety mechanism with statistical significance and captures substantively different effects across quantization regimes.

Outliers resistant image classification by anomaly detection

Various technologies, including computer vision models, are employed for the automatic monitoring of manual assembly processes in production. These models detect and classify events such as the presence of components in an assembly area or the connection of components. A major challenge with detection and classification algorithms is their susceptibility to variations in environmental conditions and unpredictable behavior when processing objects that are not included in the training dataset. As it is impractical to add all possible subjects in the training sample, an alternative solution is necessary. This study proposes a model that simultaneously performs classification and anomaly detection, employing metric learning to generate vector representations of images in a multidimensional space, followed by classification using cross-entropy. For experimentation, a dataset of over 327,000 images was prepared. Experiments were conducted with various computer vision model architectures, and the outcomes of each approach were compared.

Determination of efficiency indicators of the stand for intelligent control of manual operations in industrial production

Systems of intelligent control of manual operations in industrial production are being implemented in many industries nowadays. Such systems use high-resolution cameras and computer vision algorithms to automatically track the operator's manipulations and prevent technological errors in the assembly process. At the same time compliance with safety regulations in the workspace is monitored. As a result, the defect rate of manufactured products and the number of accidents during the manual assembly of any device are decreased. Before implementing an intelligent control system into a real production it is necessary to calculate its efficiency. In order to do it experiments on the stand for manual operations control systems were carried out. This paper proposes the methodology for calculating the efficiency indicators. This mathematical approach is based on the IoU calculation of real- and predicted-time intervals between assembly stages. The results show high precision in tracking the validity of manual assembly and do not depend on the duration of the assembly process.