
OpenAI shares new results on long-standing open problems in mathematics and theoretical computer science, including advances in geometry, cryptography, and complexity.
An AI model has solved ten long-standing mathematical problems that had seen no progress for at least a decade, spanning fields including high-dimensional geometry, coding theory, group theory, and quantum complexity. The solutions were discovered using an internal version of a next major model, with the computational cost equivalent to roughly two thousand dollars, and human researchers then prepared the mathematical arguments into manuscripts and formalized them in formal proofs. OpenAI is sharing these results while acknowledging that AI's role in mathematics raises important questions that require engagement with the broader mathematical community, and the organization has stated that attribution should honestly reflect how results were produced rather than claiming human authorship for entirely AI-generated work. The company emphasizes responsibility by providing free access to its best models for scientists and mathematicians through an academic researchers initiative.

Microsoft has open sourced code-testing-generator, a polyglot agent that writes unit tests and then proves they work. It ships in the dotnet-test plugin inside the MIT-licensed dotnet/skills repository. The agent targets a gap that coding assistants usually leave open. A prompt like ‘generate unit tests’ does not say which framework, file location or assertions to use. code-testing-generator settles those decisions by reading the repository before it writes anything. It then pla

A new SaferAI report finds Z.ai's open-weight GLM-5.2 approaches frontier AI capabilities while lacking key safety mitigations, renewing concerns that powerful open models could outpace governance and safeguards.

In this tutorial, we design an end-to-end evaluation workflow for PerceptionBench. This multimodal benchmark measures fine-grained visual perception capabilities across tasks such as OCR, counting, localization, contextual reasoning, comparison, depth understanding, and hallucination detection. We begin by configuring a Colab-compatible environment, installing the required libraries, and loading a balanced subset of the dataset through a robust multi-stage streaming and download strategy. We th
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