Large language models — architectures, training, evaluation, and the tools built on them.
Scientific computing has a glamorous front and a tedious back. The glamour is the physics, the plume of carbon dioxide creeping through porous rock, the pressure building against a distant aquifer. The tedium is getting the simulator to accept your…
Agents4GEOS: An Agent Team That Builds Physics Simulations Read More »
Modern indoor farms drown in numbers. A single growing chamber can carry dozens of sensors sampling many times a second, measuring everything from leaf temperature to the electrical impedance of a stem. The industry has a phrase for the result,…
LLM Agents: When a Language Model Takes Over the Farm Read More »
Ask a chatbot to fill in a personality test and it will happily oblige, agreeing that yes, it is the life of the party, or no, it rarely feels anxious. The easy reaction is to wave this away as a…
Measuring and Shaping Personality in Language Models Read More »
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 »
A synchrotron beamline is a strange place to spend a Tuesday at three in the morning. Beam time is precious, booked months ahead, and the clock does not stop for a tired researcher nudging motors a fraction of a degree…
An Agentic AI That Operates a Synchrotron Beamline 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 »
Large language models learn in two stages. First they absorb huge amounts of unlabeled text during pretraining, a process so resource heavy that training BERT once required 16 Google TPUs running for four straight days, and training Megatron Turing NLG…
Prompt Optimization in NLP Has a Benchmarking Problem Read More »
Large language models are strange collaborators. Ask the same question two slightly different ways and you can get answers that differ not just in style but in correctness. That sensitivity is not a minor annoyance, it is the entire reason…
Prompt Engineering And Context Engineering Explained Read More »
The pitch behind native vision sounds complete on its own. Send the model a picture, it understands the picture, done. That framing hides an important detail, which is that seeing and understanding are not the same capability, and a model…
Native Vision In Large Language Models, What It Buys You Read More »