Machine Learning Research
Custom Prompts for Safer Code: A Stanford team built a pipeline to improve system prompts to build more secure code
Large language models (LLMs) can write useful code, but they often introduce security vulnerabilities.
Machine Learning Research
Large language models (LLMs) can write useful code, but they often introduce security vulnerabilities.
Machine Learning Research
The biggest open dataset of source code went years without an update.
Machine Learning Research
The U.S. National Institute of Standards and Technology (NIST) has been testing quantum-proof replacements for today’s encryption algorithms.
Machine Learning Research
DeepSeek’s updated small model overtook the company’s own flagship.
Machine Learning Research
Assessments of the environmental impact of large language models typically focus on their final training runs, but there’s a lot more to building AI systems.
Machine Learning Research
To measure how good its models were at hacking, OpenAI reduced guardrails and ran them against a benchmark’s problem set.
Machine Learning Research
After launching Claude Fable 5, the future of Anthropic’s once-flagship Opus line was uncertain, except as a fallback for the company’s premium models.
Machine Learning Research
Large language models often are called upon to gather news. In this task, researchers found, their ability to find relevant reports is the weakest link.
Business
With Llama, Meta marked itself as an open alternative to OpenAI. With its new closed models, Meta now positions itself as a low-cost, high-value competitor.
Machine Learning Research
Moonshot’s latest model leapfrogged the month-old GLM-5.2 and a host of proprietary competitors to finish just behind GPT-5.6 Sol and Claude Fable 5 on many benchmarks.
Machine Learning Research
Providers of large language models stand to benefit by building models that spur user engagement, but users may bear a cost in undue influence on their world views.
Machine Learning Research
An AI agent proposed new medical uses for established drugs nearly autonomously — uses that were supported by experiments on isolated human cells — with human input only to name diseases to be treated and run the AI-proposed lab experiments.