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17 papers in the last four weeks, up 55% on the four weeks before. 0.2% of all new papers.

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Deja Vu in Plots: Leveraging Cross-Session Evidence with Retrieval-Augmented LLMs for Live Streaming Risk Assessment
Jan 22, 2026Yiran Qiao, Xiang Ao, Jing Chen +3Streaming Video Realistic Threat Model
ByteDance China · Institute of Computing Technology, Chinese Academy of Sciences
AgenTRIM: Tool Risk Mitigation for Agentic AI
Jan 18, 2026Roy Betser, Amit Giloni, Shamik Bose +4Agentic Deployments Access Control
Fujitsu Research of Europe (FRE) · Fujitsu Research of India Pvt. Ltd. (FRIPL)
Data Security in Large Language Models: Risks, Defense, and Directions
Aug 4, 2025Kang Chen, Xiuze Zhou, Yuanhui Yu +4Large Language Model Safety Cybersecurity
School of Computer Engineering, Jimei University, Xiamen, 361021, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · School of Informatics, Xiamen University, Xiamen, 361102, China +1
GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratification
Aug 3, 2025Cheng Huang, Zeyu Han, Weizheng Xie +4Glaucoma Detection Unimodal Metrics
Department of Computer Science, Southern Methodist University, Dallas, TX 75205, USA · Department of Ophthalmology, UT Southwestern Medical Center, Dallas, TX 75390, USA · High Performance Computing Program, National Aeronautics and Space Administration, Washington, DC 20546, USA
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
Jul 24, 2023Spencer Giddens, Yiwang Zhou, Kevin R. Krull +3Empirical Risk Minimization Privacy-Preserving Machine Learning
University of Notre Dame, Notre Dame, IN 46556, USA · St. Jude Children’s Research Hospital, Memphis, TN 38138, USA · University of Michigan, Ann Arbor, MI 48109, USA
Universality of empirical risk minimization
Feb 17, 2022Andrea Montanari, Basil SaeedEmpirical Risk Minimization Neural Tangent Kernel
Department of Statistics and Department of Mathematics, Stanford University · Department of Electrical Engineering, Stanford University
Emergent Risks in Generative Multi-Agent Systems
Date pendingYue Huang, Yu Jiang, Wenjie Wang +12Collective Behaviors Collusion
University of Notre Dame · LMU Munich · University of Washington +7
Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective
Date pendingErfan LoghmaniLarge Language Model Fine-Tuning Large Language Models Fail
Foster School of Business University of Washington Seattle, WA 98195
Longitudinal Risk Prediction in Mammography with Privileged History Distillation
Date pendingBanafsheh Karimian, Soufiane Belharbi, Alexis Guichemerre +3Mammography Clinical Prediction
LIVIA, Dept. of Systems Engineering, ETS Montreal, Canada · Goodman Cancer Institute, Dept. of Oncology, McGill University, Canada

Jan 22, 2026, cs.AIJ/K move · Enter open · S save

Deja Vu in Plots: Leveraging Cross-Session Evidence with Retrieval-Augmented LLMs for Live Streaming Risk Assessment

Yiran Qiao, Xiang Ao, Jing Chen, Yang Liu+2

ByteDance China · Institute of Computing Technology, Chinese Academy of Sciences

Streaming Video Realistic Threat Model Risk Unsupervised Detection

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The rise of live streaming has transformed online interaction, enabling massive real-time engagement but also exposing platforms to complex risks such as scams and coordinated malicious behaviors. Detecting these risks is challenging because harmful actions often accumulate gradually and recur across seemingly unrelated streams. To address this, we propose CS-VAR (Cross-Session Evidence-Aware Retrieval-Augmented Detector) for live streaming risk assessment. In CS-VAR, a lightweight, domain-specific model performs fast session-level risk inference, guided during training by a Large Language Model (LLM) that reasons over retrieved cross-session behavioral evidence and transfers its local-to-global insights to the small model. This design enables the small model to recognize recurring patterns across streams, perform structured risk assessment, and maintain efficiency for real-time deployment. Extensive offline experiments on large-scale industrial datasets, combined with online validation, demonstrate the state-of-the-art performance of CS-VAR. Furthermore, CS-VAR provides interpretable, localized signals that effectively empower real-world moderation for live streaming.

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