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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.
When you’re training a robot via reinforcement learning, a handcrafted reward function is labor-intensive to build but often dispenses rewards more effectively than a general-purpose reward model based on a vision-language model. Researchers built reward models that narrowed the gap.
SWE-bench, a family of benchmarks that focuses on an LLM’s ability to fix software bugs, is giving way to new tests that evaluate agent software-engineering performance in more challenging ways.
Before Anthropic pulled its latest Claude models from circulation, even professional testers couldn’t readily tell whether they were getting a Mythos-class model or a lesser version under the same name.
AI agents seem to be increasingly capable of performing economically valuable tasks, but current benchmarks measure this capability only narrowly.
The latest update of OpenAI’s flagship model sets new states of the art in important benchmarks but has difficulty distinguishing between what it does and doesn't know.
The Qwen3.5 family of open-weights vision-language models includes impressive larger models as well as a smaller one that outperforms an OpenAI open-weights model 10 times its size.
Artificial Analysis, which tests AI systems, updated the component evaluations in its Intelligence Index to better reflect large language models’ performance in real-world use cases.
OpenAI launched GPT-5.2 only weeks after its CEO Sam Altman reportedly issued a “code red” alarm in response to Google's Gemini 3.
Large language models often fail at puzzles like Sudoku, for which a solution includes multiple elements and a single mistake invalidates all of them. Researchers showed that a tiny network, by repeatedly refining its solution, can solve this sort of puzzle well.
Claude Opus 4.5, the latest version of Anthropic’s flagship model, extends the earlier version’s strengths in coding, computer use, and agentic workflows while generating fewer tokens.
Google introduced Gemini 3 Pro and Nano Banana Pro, its flagship vision-language and image-generation models, and deployed them to billions of users worldwide.
Transformers are well suited to predicting future values of time series like energy prices, wages, or weather, but often — as in those examples — multiple time series often influence one another. Researchers built a model that can forecast multiple time series simultaneously.
A new type of transformer can check its work. Instead of guessing the next output token in one shot like a typical transformer, it starts with a rough version of the token and improves it step by step.
A bottleneck in fine-tuning large language models for software engineering is building a dataset that can show them how to edit code, search for subroutines, write test scripts, control a terminal, manage a file system, and so on. Researchers built a pipeline that produces such data automatically.
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