arXiv subjects

Victor Minchenkov

Publications and source records attributed to Victor Minchenkov.

4 recordsLinked to original sources

arXiv2026

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.

cs.CR↗

arXiv2025

Combined Routing Protocol (CRP) for ad hoc networks: Combining strengths of location-based and AODV-based schemes

The work proposes a new Combined Routing Protocol (CRP) for ad hoc networks that combines the benefits and annihilates the shortcomings of two well-known on-demand routing protocols in ad hoc networks: AODV (which provides a high probability of successfully discovering and maintaining a reliable route) and GPSR (which enables fast on-the-fly transmission based on the geographical coordinates of the destination node). The main idea of the new routing scheme applied in CRP is to use AODV protocol as a solution to the "perimeter problem" of GPSR. And vice versa we apply GPSR for moving the starting point of the AODV route discovering closer to the destination point, decreasing the number of hops and route building time, making the resultant route more stable. As the key result we see decreasing of the average packet delivery time in ad hoc networks with is extremely important for latency-critical applications, such as video streaming or command traffic.

cs.NI↗

arXiv2024

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.

cs.CV↗

arXiv2024

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

Manual operations remain essential in industrial production because of their flexibility and low implementation cost. However, ensuring their quality and monitoring execution in real time remains a challenge, especially under conditions of high variability and human-induced errors. In this paper, we present an AI-based control system for tracking manual assembly and propose a novel methodology to evaluate its overall efficiency. The developed system includes a multicamera setup and a YOLOv8-based detection module integrated into an experimental stand designed to replicate real production scenarios. The evaluation methodology relies on timestamp-level comparisons between predicted and actual execution stages, using three key metrics: Intersection over Union (IoU), Mean Absolute Scaled Error (MASE), Residual Distribution histograms. These metrics are aggregated into a unified efficiency index E_total for reproducible system assessment. The proposed approach was validated on a dataset of 120 assemblies performed at different speeds, demonstrating high segmentation accuracy and identifying stage-specific timing deviations. The results confirm the robustness of the control system and the applicability of the evaluation framework to benchmark similar solutions in industrial settings.

cs.CV↗