{"total":16,"items":[{"citing_arxiv_id":"2607.00063","ref_index":94,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Spectral Geometry and Bosonic-Bloch Probes: Explorations in Quantum Learning","primary_cat":"quant-ph","submitted_at":"2026-06-30T11:06:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Training in graph-regularized quantum networks increases spectral dimension by 0.23 and enables anomaly detection via Bloch drift (ROC-AUC ≥0.9) while bosonic enhancement correlates with Fiedler splits (r=-0.50).","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20079","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"How to spot outliers: an Ensemble Anomaly Detection Framework","primary_cat":"q-fin.RM","submitted_at":"2026-06-18T10:54:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Ensemble anomaly detection framework for real-time risk calculation monitoring outperforms single methods with F1 scores of 61-79% on proprietary credit-derivatives data using injected anomalies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17555","ref_index":4,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts","primary_cat":"cs.CR","submitted_at":"2026-06-16T05:58:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Presents a three-component fusion AI agent for multi-vector fraud and AML detection in retail/corporate banking using LSTM, statistical, and graph modules on synthetic data, reporting F1 scores of 0.787 (transactions) and 0.867 (sessions).","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.13244","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection","primary_cat":"quant-ph","submitted_at":"2026-06-11T11:56:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10170","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning Entropy and Spatial Adaptation Dynamics of Multilayer Perceptrons for Structural Point Extraction","primary_cat":"cs.LG","submitted_at":"2026-06-08T21:05:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Spatial Learning Entropy Maps derived from MLP weight adaptations during spatial pixel prediction tasks highlight image points with high learning impact.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.08270","ref_index":3,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response","primary_cat":"cs.CR","submitted_at":"2026-06-06T17:33:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"An AI agent for ACMIS uses supervised anomaly detection, behavioral analytics, and an NLP chatbot to report 0.966 macro F1 on simulated threat data, outperforming rule-based and LSTM baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09874","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Disjoint or Overlapping? 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The data modalities contain the data structured as described in this paper in .csv and .xml files format. When publishing results based on the simulation data (data modalities13 ... Timeseries Simulation,14 ... Simulation Configuration), users should cite this paper, and the dataset DOI of the used version. 17 References [1] V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: A survey, ACM Comput. Surv. 41 (3) (2009) 1-58. doi:10.1145/1541880.1541882. [2] V. Venkatasubramanian, R. Rengaswamy, K. Yin, S. N. Kavuri, A review of process fault detection and diagnosis part i: Quantitative model-based methods, Comput. Chem. Eng. 27 (3) (2003) 293-311.doi: 10.1016/s0098-1354(02)00160-6."},{"citing_arxiv_id":"2510.18935","ref_index":183,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications","primary_cat":"cs.CV","submitted_at":"2025-10-21T17:45:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A systematic review that introduces a framework for feature extraction in remote sensing, traces its evolution in the data value chain, and synthesizes trends toward unified representations and foundation models.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"(GTVLRR), incorporate sparse coding and structured low- rank constraints to enhance anomaly separation, making them highly effective in HS imagery [134], [135]. AE-based approaches detect anomalies by learning compact representations and highlighting deviations via reconstruction errors [182]. However, standard AEs often generalize too well, reducing the reconstruction error for anomalies [183]. To mitigate this, several variants introduce constraints to improve separation. Sparse and manifold-constrained AEs enforce fea- ture selectivity and preserve local geometric structures, reduc- ing redundant background reconstruction [136]. Transformer- based AEs model long-range dependencies through self- attention, improving feature representation in complex spectral"},{"citing_arxiv_id":"2305.18593","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"On Diffusion Modeling for Anomaly Detection","primary_cat":"cs.LG","submitted_at":"2023-05-29T20:19:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}