Natural language processing covers how machines represent, understand, and generate human language. Articles here move from foundational ideas like embeddings and attention to current research on language models, retrieval, and evaluation, written to connect the theory with the systems people use today.
More and more people are bringing their troubles to a chatbot instead of a friend. Many come away feeling genuinely heard. Not everyone does, and the stakes of getting it wrong in an emotional conversation run high, from unhelpful advice…
Can LLMs Reliably Judge Empathic Communication Read More »
A materials scientist sits down with a fresh stack of papers and already knows the stack is a rounding error. The field now publishes more abstracts in a year than any one person could read in a decade of weekends.…
How LLMs and Concept Graphs Predict New Materials Research Read More »
The properties of a material, whether it conducts electricity, whether it is magnetic, how much energy it takes to form, all trace back to how its atoms are arranged in space. Two materials with the same chemical formula can behave…
MatterChat Brings Multimodal LLMs to Materials Science Read More »
A standard knowledge graph contains two qualitatively different types of edges. The first type connects entities to other entities through named relations, forming the relational backbone that most retrieval methods are built to traverse. The second type connects entities to…
Human motion simulation supports a genuinely practical set of jobs, ergonomic risk assessment, safety training, and digital human modeling for manufacturing lines where getting a worker’s posture wrong on paper is a lot cheaper than getting it wrong on the…
Why GPT-4 Rewritten Prompts Only Sometimes Improve HUMAN Motion Simulation Read More »
Dr. Fatima Nkosi is managing 19 patients on a Saturday night hospitalist shift. At 2am, she opens the clinical AI assistant embedded in her hospital’s Epic instance and types: “64-year-old male, day 2 post-op colectomy, HR 118, temp 38.9°C, WBC…
Clinical LLMs 2026: Med-Gemini, Med-PaLM 2, and GPT-5 in Medicine Read More »
Ask a developer who has been using AI coding tools since 2022 how their workflow has changed, and you will get one of two answers. The first: “I paste code into ChatGPT and it writes the next function for me.”…
The New LLM Coding Workflow for 2026: How Developers Actually Use AI Read More »
Researchers at Technische Universität Berlin and Qingdao University of Technology achieved 73.68% construction activity recognition accuracy using pure prompting — no labeled training data, no fine-tuning, just a domain ontology, structured visual extraction, and carefully engineered in-context examples.
Researchers at Nanjing Normal University placed a trainable 10-million-parameter intermediary between a frozen 1.2-billion-parameter vision-language model and a tiny student classifier — solving the architectural mismatch, noise contamination, and unstable optimization that make direct VLM-to-lightweight distillation fail, and gaining +12.63%…
DAIT: Distilling CLIP into Tiny Classifiers with an Adaptive Intermediate Teacher Read More »
Researchers from UNC-Chapel Hill, Carnegie Mellon, UC Santa Cruz, and UC Berkeley built a continual meta-learning framework that gives deployed language model agents two complementary superpowers: instant skill injection from failures, and opportunistic weight-level fine-tuning while you sleep — pushing…
MetaClaw: The LLM Agent That Meta-Learns and Evolves in the Wild Read More »