Category
2,290 papers
A Guide to the Logic Series Papers This guide organizes the logic series papers into four parts by time and theme: the discovery process (before 9/1) → after the three dimensions were determined → the theoretical cornerstones → applications. I. Pre-9/1 Versions — The Discovery Process, as Recorded Positioning: These re...
BLOCK VECTOR Research Map The BLOCK VECTOR Research Map is a visual guide to the Stable Authority Boundary (SAB), its formalization, core principles, applications, and related research. The collection examines a common systems problem: a machine, AI agent, autonomous system, or distributed system may remain technically...
Understanding the long-term drivers of settlement location requires disentangling ecological constraints from historically contingent choices. Here I apply a Random Forest machine-learning framework, coupled with explainable AI techniques, to investigate settlement dynamics in Latium vetus (central Italy) from the Earl...
Description The preceding papers in this series argued that explainability cannot ground accountability in probabilistic AI systems, that hallucination is the expected epistemic consequence of forced answer generation, and that accountable AI therefore requires a formal system language of non-decision, decision boundar...
Reach audiences
Advertise in front of researchers, engineers, and readers.
This dataset accompanies a mixed-methods pilot study of an eight-week intervention (22 October – 17 December 2025). In the intervention, 54 university EFL students in China used freely available large language model chatbots for low-stakes speaking practice. The chatbots used were DeepSeek, Doubao, ChatGPT, Kimi Chat a...
The protocol emphasizes several key components, including the preparation of standardized healthcare datasets, the development of stable machine learning models, the evaluation of predictive performance, the generation of clinically relevant explanations, and the statistical assessment of reproducibility across repeate...
Both social design features and chatbot design features shaped adoption-related beliefs, with peer-oriented and family-oriented framing and AI-powered chatbots showing particular promise.
This review synthesizes current evidence across these domains, highlights methodological limitations, and proposes a research agenda through which AI-augmented NHP experimentation can become a bridge from preclinical data complexity to clinical precision in xenotransplantation.
Large language models are increasingly used to generate and evaluate online content, yet it remains unclear whether the qualities they associate with higher engagement match what real users respond to. We study this question using 1.17 million answers to 25,978 questions from Zhihu, Quora, and Reddit, comparing real pl...
Causal Sufficient and Necessary Regional Explanations (SNRE), a framework that learns an input region and output region such that membership in A is both sufficient and necessary for the model output to fall in B, is proposed.
Lighter than ViT, which is effective in capturing long-range dependencies, the proposed CNN-integrated CCT has also proven efficient in breast cancer classification by capturing long-range dependencies among breast tissue regions.
Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work better, and under which conditions. However, such progress is thought to be hindered by difficulties in reproducibility and in defining clinically meaningful evaluation...
GPT-Lab
· gpt-lab.eu
Oct 12, 2026
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
