AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,378 papers
Reinforcement learning (RL) is concerned with how intelligence agents take actions in a given environment to maximize the cumulative reward they receive. In healthcare, applying RL algorithms could assist patients in improving their health status. In ride-sharing platforms, applying RL algorithms could increase drivers...
Principles of Good Scientific Practice in Geoinformatics: From Data Management to Reproducible Code Trial lecture (Probevorlesung, § 13 HabilO) for the Venia Legendi in Geovisual Analytics at the Faculty of Environmental Sciences, Technische Universität Dresden (October 15, 2026, Dresden, Germany). Reflecting the core...
Smaller language models often exhibit high confidence in erroneous predictions, undermining traditional ensemble methods. In heterogeneous ensembles, simple averaging can grant disproportionate influence to overconfident but incorrect outputs. This paper introduces a reliability-weighted extension of the Proportional C...
Pediatric speech and language assessment supports the early identification of communication difficulties and access to timely intervention. Large language models (LLMs) and related generative artificial intelligence technologies are increasingly being incorporated into systems that analyze children’s speech and languag...
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Purpose Large Language Models (LLMs) can be helpful for literature search and summarization but retracted articles can confuse them. It is therefore important to know how aware LLMs are of retractions. Design/methodology/approach Three open-weight (offline) LLMs were asked whether 161 high-profile retracted articles ha...
Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slow experimental iteration. Unlike catalysis, where surface reactivity dominates, SEs require simultaneous optimization of bulk ion transport,...
A position paper arguing that closed source has failed as a protection, for science first. Large language models now decompile binaries into re-executable source; black-box queries extract parts of production models; distillation copies capabilities at industrial scale; and the largest 2026 trade-secret dispute in arti...
Large language models can generate programs that compile and pass functional tests, but these outcomes are insufficient for evaluating code in secure multi-party computation (MPC). MPC programs must also follow the target interface's programming constraints, implement the requested algorithm, and satisfy the stated sec...
Fine-tuning large language models (LLMs) enables task specialization but introduces privacy risks through training data memorization. Practitioners deploying fine-tuned LLMs in sensitive domains such as medicine, law, and finance must choose among fine-tuning methods without clear guidance on the privacy implications o...
Argument structures represent how individual arguments interact within a discourse. However, leveraging these structures to infer meaningful knowledge remains challenging, as it requires a formal representation of how argumentative influence propagates across the structure. We propose Neuro-symbolic Argumentation Reaso...
Large language models (LLMs) are increasingly being integrated into creative and decision-making processes, raising critical questions about their role in shaping knowledge and understanding. This dissertation examines the emerging phenomenon of LLM-generated synthetic personas (SP) and its implications for design proc...
BACKGROUND Large language models (LLMs) are increasingly used to provide patient-facing health information, yet evidence regarding their performance in elective aesthetic and appearance-related plastic surgery consultation remains heterogeneous. This review evaluated the scope, quality, usability, safety, and technical...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.
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