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2,807 papers
This work proposes ScopeMamba-YOLO, built around an off-path, zero-gated selective-scanning principle that decouples contextual modeling from the convolutional stream, and shows consistent improvements on AI-TOD, especially for very-tiny and tiny objects.
This work introduces AutoScreen, an AI co-scientist system supporting target discovery through both Pre-screen Design, which constructs perturbation libraries de novo from free-text research descriptions, and Post-screen Analysis, which re-ranks experimental screen hits by integrating statistical scores with biological...
🚀 Release v1.0.0 — Official Research Implementation This is the initial official release providing the complete, reproducible source code and experimental pipeline for the research paper: "A Hybrid CNN-LSTM Framework for Global Renewable Energy Forecasting: Integrating Socio-Economic Drivers, Explainability, and a Cas...
This paper develops a theoretical model of how responsibility structures may come under pressure for reconfiguration as non-human reactive entities—including generative AI, autonomous control systems, recommendation algorithms, and other computational systems—participate continuously in social operations. Rather than a...
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Unit IV – AI in Research, Generative AI and Prompt Engineering is an Open Educational Resource (OER) presentation developed for the undergraduate AI Fundamentals course. This presentation introduces the use of Artificial Intelligence in experimentation and multidisciplinary research, followed by the fundamentals of Gen...
AI assistants increasingly refer to information they ostensibly retain from earlier sessions. A remembered preference can signal attentiveness, whereas a reference to stress or late-night behavior can feel intrusive. Existing accounts of personalization and privacy do not fully explain why these cues elicit opposing re...
Polycystic Ovarian Disease (PCOD) has remained one of the most common endocrine diseases in women of childbearing age in the world. Although it is common, the heterogeneous symptoms, subjective clinical criteria, and sociocultural barriers to care impede the early diagnosis of the disorder. The convergence of AI techno...
Artificial Intelligence is transforming Anti-Money Laundering enforcement by enabling proactive detection, real-time surveillance, and data-driven decision-making across financial & criminal justice institutions.The present study examines how AI capabilities in anomaly detection, risk scoring, network analysis, and aut...
Unit I – AI and Its Subfields is an Open Educational Resource (OER) presentation developed for the AI Fundamentals course for undergraduate students. This presentation provides a structured introduction to Artificial Intelligence and its major concepts. It covers the definition, objectives, characteristics, history and...
Digital transformation and artificial intelligence are reshaping how organizations develop capabilities, make decisions, innovate, and pursue sustainable performance. However, existing research largely examines digital transformation, Dynamic Capabilities, AI capability, organizational intelligence, and sustainability...
This study introduces a modular, AI-powered disease diagnosis system that combines deep learning, neuro-symbolic reasoning, and structured automation to support early, scalable, and explainable clinical decision-making. Built upon the large-scale DDXPlus dataset, the system processes over one million patient records in...
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
GPT-Lab
· gpt-lab.eu
Oct 30, 2026
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
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