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Phi-4-reasoning-vision-15B

Phi-4-reasoning-vision-15B is a 15B-parameter open-weight vision-language reasoning model from Microsoft, released on March 4, 2026 under the MIT licence. It takes text and image input, is trained mainly on English, and has a 16,384-token context length.

Released

March 4, 2026

Type

multimodal

License

open-weight

MIT

Context

16,384 tokens

Capabilities

textimagesreasoning

Architecture

Parameters: 15B

Sources

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Links

In the news: Phi-4-reasoning-vision-15B and Microsoft Research

OpenAI

On the Navier–Stokes Millennium Prize Problem

An artificial intelligence system has generated a solution to the Navier-Stokes Millennium Prize Problem, a long-standing challenge in mathematics and physics. The AI's output includes a detailed writeup of the solution and a formal proof formalized in the Lean theorem prover. This development marks a significant moment in the application of AI to complex scientific and mathematical problems, potentially accelerating research and discovery in fields reliant on fluid dynamics. The Navier-Stokes equations describe the motion of viscous fluid substances, such as air, water, and blood. They are fundamental to understanding a vast array of phenomena, from weather patterns and ocean currents to the aerodynamics of aircraft and the flow of blood in arteries. Despite their importance, proving the existence and smoothness of solutions to these equations in three dimensions has remained an unsolved problem for over a century. The Clay Mathematics Institute designated it as one of seven Millennium Prize Problems in 2000, offering a $1 million prize for a correct solution. The AI's contribution, as detailed in its accompanying writeup, aims to address the existence and smoothness of solutions. Formalizing the proof in Lean is a critical step, as it allows for rigorous verification by human mathematicians. Lean is an interactive theorem prover and programming language developed by Leonardo de Moura at Microsoft Research, designed to assist in the formal verification of mathematical proofs and software. Its use signifies a commitment to mathematical rigor and allows the broader scientific community to scrutinize the AI's findings. While the AI has produced the solution and proof, its validity is now subject to review by experts in the field. The process of peer review for such a complex mathematical problem, especially one generated by an AI, is expected to be thorough and potentially lengthy. The implications of a confirmed AI-generated solution could be profound, not only for fluid dynamics but also for the broader landscape of AI-assisted scientific research. It could pave the way for AI to tackle other grand challenges in mathematics and physics, potentially leading to breakthroughs that have eluded human researchers for decades. The Clay Mathematics Institute will ultimately determine if the provided solution meets the criteria for the Millennium Prize.

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