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2,807 papers
HACoD is proposed, a Human–Agent Emotional Co-Driving Framework that links driver emotional needs, human–agent collaboration patterns, and affective agent design elements and highlights the need for transparent data boundaries and real-world validation.
The rhizospheric microbiome is one of the important factors determining plant health, productivity, and secondary metabolite biosynthesis and differs profoundly between medicinal and nonmedicinal plants. Medicinal plants harbor more specialized and metabolically diverse microbial communities, trained by specialized roo...
This publicly available, multi-clinic dental imaging dataset advances research in artificial intelligence (AI), computer vision, and computer-aided diagnosis (CAD) using panoramic dental radiographs (orthopantomogram, OPG). It contains 2,095 fully anonymized panoramic radiographs retrospectively collected from four den...
📌 EXECUTIVE & METHODOLOGICAL FRAMEWORK: How to Read This Document This white paper goes beyond a standard advocacy text; it serves as a foundational methodological pillar within a longitudinal participatory observation corpus. Readers, institutional reviewers, and policymakers should evaluate this document through the...
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This paper presents UMOSU (Unified Model of Social Universe), an ontological model designed to classify phenomena according to the components that are necessary for their manifestation, and UMOKWAI (Unified Model of Knowledge With AI), an epistemic-operational model designed to classify bodies of knowledge according to...
This conceptual research note proposes an invariant-first, falsification-first research program for the P versus NP problem. It does not claim a proof of P=NP or P≠NP. Instead, it asks whether progress can be organized more effectively by shifting attention away from individual candidate algorithms and toward structura...
This conceptual research note proposes an invariant-first, falsification-first research program for the P versus NP problem. It does not claim a proof of P=NP or P≠NP. Instead, it asks whether progress can be organized more effectively by shifting attention away from individual candidate algorithms and toward structura...
An argument that understanding is not a matter of holding the right information, but of building structure that costs something to be wrong about.A student can recite a definition perfectly, reproduce its derivations, and answer routine questions about it — and still be caught flat by a question that requires seeing wh...
Large Language Models have become powerful tools for programming. However, they often overlook essential security practices, producing code with vulnerabilities. Despite this risk, existing work offers limited guidance on steering LLMs toward secure code generation and lacks systematic analysis of how effectively LLMs...
Text-based person retrieval under a sim-to-real gap (synthetic training data, a real-image gallery) is usually tackled with costly fine-tuned cross-encoders. We ask whether a frozen-encoder system can compete. We present SCOUT, which casts cross-modal retrieval as prediction in embedding space. A trainable predictor ma...
Technology ecosystems often reward whichever firm first controls a tightly held physical or infrastructural asset -the "anchor" -because that control reduces uncertainty, sets de facto standards, and buys market access for a higher-margin software, service, or workflow layer -the "expansion."Existing strategy research...
Reproducibility artifacts for the meta-research study "Available but not runnable out of the box: an audit of execution prerequisites and label reporting in shared code for dysphagia and swallowing artificial intelligence." The protocol keeps the working title it was written under. We audited whether published swallowi...
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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