A multimodal deep reinforcement learning approach for IoT-driven adaptive scheduling and robustness optimization in global logistics networks - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5MZ3FoNVhEdkJzbTkyUGt1TDdoX1dtVzhPSmJNNHNzM3IwSHZYRW1zTWt3bjVYT0ZNeE9DZ0NGYUU4Z3hGUkt0cWc1M3Y1MThFdV9pVGE0SFk1N3l3azA0?oc=5" target="_blank">A multimodal deep reinforcement learning approach for IoT-driven adaptive scheduling and robustness optimization in global logistics networks</a> <font color="#6f6f6f">Nature</font>
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Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry
arXiv:2604.00319v1 Announce Type: new Abstract: We develop algorithms for collaborative control of AI agents and critics in a multi-actor, multi-critic federated multi-agent system. Each AI agent and critic has access to classical machine learning or generative AI foundation models. The AI agents and critics collaborate with a central server to complete multimodal tasks such as fault detection, severity, and cause analysis in a network telemetry system, text-to-image generation, video generation, healthcare diagnostics from medical images and patient records, etcetera. The AI agents complete their tasks and send them to AI critics for evaluation. The critics then send feedback to agents to improve their responses. Collaboratively, they minimize the overall cost to the system with no inter-
OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning
arXiv:2604.00270v1 Announce Type: new Abstract: Recent large multimodal models (LMMs) have made rapid progress in visual grounding, document understanding, and diagram reasoning tasks. However, their ability to convert Printed Circuit Board (PCB) schematic diagrams into machine-readable spatially weighted netlist graphs, jointly capturing component attributes, connectivity, and geometry, remains largely underexplored, despite such graph representations are the backbone of practical electronic design automation (EDA) workflows. To bridge this gap, we introduce OmniSch, the first comprehensive benchmark designed to assess LMMs on schematic understanding and spatial netlist graph construction. OmniSch contains 1,854 real-world schematic diagrams and includes four tasks: (1) visual grounding f
Aligning Recommendations with User Popularity Preferences
arXiv:2604.01036v1 Announce Type: new Abstract: Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items. This not only results in "rich-get-richer" dynamics and a homogenization of visible content, but can also lead to misalignment of recommendations with individual users' preferences for popular or niche content. This work studies popularity bias through the lens of user-recommender alignment. To this end, we introduce Popularity Quantile Calibration, a measurement framework that quantifies misalignment between a user's historical popularity preference and the popularity of their recommendations. Building on this notion of popularity alignment, we propose SPREE, an inference-time mitigation method for sequential recommend
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South Korea's Lee Says He Seeks to Work Together With France on AI, Nuclear Energy - U.S. News & World Report
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South Korea's Lee says he seeks to work together with France on AI, nuclear energy - Reuters
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Army tests autonomous strike drone featuring AI-enabled targeting capabilities - DefenseScoop
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