Zero-Training Temporal Drift Detection for Transformer Sentiment Models: A Comprehensive Analysis on Authentic Social Media Streams
arXiv:2512.20631v1 Announce Type: new
Abstract: We present a comprehensive zero-training temporal drift analysis of transformer-based sentiment models validated on authentic social media data from major real-world events. Through systematic evaluation across three transformer architectures and rigo...
Real Time Detection and Quantitative Analysis of Spurious Forgetting in Continual Learning
arXiv:2512.20634v1 Announce Type: new
Abstract: Catastrophic forgetting remains a fundamental challenge in continual learning for large language models. Recent work revealed that performance degradation may stem from spurious forgetting caused by task alignment disruption rather than true knowledge...
BitRL-Light: 1-bit LLM Agents with Deep Reinforcement Learning for Energy-Efficient Smart Home Lighting Optimization
arXiv:2512.20623v1 Announce Type: new
Abstract: Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We present BitRL-Light, a novel framework combining 1-bit quantized Large Language Model...
Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deployment
arXiv:2512.20624v1 Announce Type: new
Abstract: This study introduces a quantum inspired framework for optimizing the exploration exploitation tradeoff in multiagent reinforcement learning, applied to UAVassisted 6G network deployment. We consider a cooperative scenario where ten intelligent UAVs a...
arXiv:2512.20626v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to dynamically access external information, which is powerful for answering questions over previously unseen documents. Nonetheless, they struggle with high-level conceptual und...
MicroProbe: Efficient Reliability Assessment for Foundation Models with Minimal Data
arXiv:2512.20630v1 Announce Type: new
Abstract: Foundation model reliability assessment typically requires thousands of evaluation examples, making it computationally expensive and time-consuming for real-world deployment. We introduce microprobe, a novel approach that achieves comprehensive reliab...
The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel
An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings into contextual representations, illustrated with simple examples and an Excel-friendly walkthrough.
The post The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel appeared f...
Training a Model with Limited Memory using Mixed Precision and Gradient Checkpointing
This article is divided into three parts; they are: • Floating-point Numbers • Automatic Mixed Precision Training • Gradient Checkpointing Let's get started! The default data type in PyTorch is the IEEE 754 32-bit floating-point format, also known as single precision.
Waymo is testing Gemini as an in-car AI assistant in its robotaxis
Waymo is testing a Gemini-powered in-car AI assistant, per findings from a 1,200-line system prompt. The assistant can answer general knowledge questions, control certain in-cabin features, and more.
Step into a fictional cocktail party where today’s most popular AI models—ChatGPT, Copilot, Claude, Gemini, MidJourney, Stable Diffusion, and Bard—banter, argue, and collaborate. This playful analogy highlights their unique personalities, training philosophies, and biases, while revealing how they c...
Scientists reverse Alzheimer’s in mice and restore memory
Alzheimer’s has long been considered irreversible, but new research challenges that assumption. Scientists discovered that severe drops in the brain’s energy supply help drive the disease—and restoring that balance can reverse damage, even in advanced cases. In mouse models, treatment repaired brain...
Is Your Model Time-Blind? The Case for Cyclical Feature Encoding
How cyclical encoding improves machine learning prediction
The post Is Your Model Time-Blind? The Case for Cyclical Feature Encoding appeared first on Towards Data Science.
What we put on our plates may matter more for the climate than we realize. Researchers found that most people, especially in wealthy countries, are exceeding a “food emissions budget” needed to keep global warming below 2°C. Beef alone accounts for nearly half of food-related emissions in Canada. Sm...
Italy tells Meta to suspend its policy that bans rival AI chatbots from WhatsApp
Italy has ordered Meta to suspend its policy that bans companies from using WhatsApp's business tools to offer their own AI chatbots on the popular chat app.
AI supercharges scientific output while quality slips
AI writing tools are supercharging scientific productivity, with researchers posting up to 50% more papers after adopting them. The biggest beneficiaries are scientists who don’t speak English as a first language, potentially shifting global centers of research power. But there’s a downside: many AI...
Best OCR and vision language models you can run locally that transform documents, tables, and diagrams into flawless markdown copies with benchmark-crushing accuracy.
Large Language Models for EDA Cloud Job Resource and Lifetime Prediction
arXiv:2512.19701v1 Announce Type: new
Abstract: The rapid growth of cloud computing in the Electronic Design Automation (EDA) industry has created a critical need for resource and job lifetime prediction to achieve optimal scheduling. Traditional machine learning methods often struggle with the com...
Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches
arXiv:2512.19713v1 Announce Type: new
Abstract: Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they...
Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data
arXiv:2512.19716v1 Announce Type: new
Abstract: Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict in-hospital mor...
Thermodynamic Focusing for Inference-Time Search: Practical Methods for Target-Conditioned Sampling and Prompted Inference
arXiv:2512.19717v1 Announce Type: new
Abstract: Finding rare but useful solutions in very large candidate spaces is a recurring practical challenge across language generation, planning, and reinforcement learning. We present a practical framework, \emph{Inverted Causality Focusing Algorithm} (ICFA)...
Synthetic Data Blueprint (SDB): A modular framework for the statistical, structural, and graph-based evaluation of synthetic tabular data
arXiv:2512.19718v1 Announce Type: new
Abstract: In the rapidly evolving era of Artificial Intelligence (AI), synthetic data are widely used to accelerate innovation while preserving privacy and enabling broader data accessibility. However, the evaluation of synthetic data remains fragmented across ...
PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research
arXiv:2512.19799v1 Announce Type: new
Abstract: Advances in LLMs have produced agents with knowledge and operational capabilities comparable to human scientists, suggesting potential to assist, accelerate, and automate research. However, existing studies mainly evaluate such systems on well-defined...