#开源/仓库

Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. https://nvda.ws/4hcoq7m

🚢 Marin 535B-A23B started training this week! As usual, the whole process is open. Voyage plan: pretraining (80%) + midtraining (20%) on 18.75T tokens on 11 x GB200 NVL72 for ~3 months (2.7e24 FLOPs). Post-training will follow. Before kicking off the run, we trained a 4-rung scaling ladder from 1.6B-A61M (48B tokens) to 27.7B-A1.2B (926B tokens) to debug issues, and to make a forecast of our hero run. This is by far our biggest run, so definitely expecting the unexpected.

We’re excited to bring @MiniMax_AI to Raven 🐦‍⬛ as a first-class model option. MiniMax powers the model. Raven makes that intelligence compound across runs. Built on EverOS, Raven is a memory-first, self-improving agent harness. It gives models durable user and agent memory, actively manages context during long-running work, follows up proactively through Sentinel, and turns successful workflows into reusable skills that improve with feedback. Paired with MiniMax’s long-context capabilities, agentic reasoning, coding performance, and tool use, Raven can take on longer, more complex workflow, from deep research and coding to automation and persistent digital workers. Try Raven × MiniMax and let us know what you build. https://github.com/EverMind-AI/Raven Get 12% off MiniMax API plans through our dedicated links:

Made some improvements on the decode path for MiniMax M3 by @MiniMax_AI on MLX-VLM Faster decode, slightly lighter footprint. Thanks to @ivanfioravanti for the PR 🚀 PR: https://github.com/Blaizzy/mlx-vlm/pull/1374

Recently, we took time to consolidate all of the work behind M2 and published it here: our M2 paper on arXiv It’s been just over six months since we first open-sourced M2 on December 23 last year. During that time, a number of our ideas and systems have been broadly adopted by the open-source community — including CISPO, Forge RL System, Self-Evolution. Over the past six months, we’ve felt incredible enthusiasm from the open-source community. Nearly every model release reached the #1 spot on the Hugging Face leaderboard. Now it’s time for a new chapter. We’re getting ready for M3. MSA paper is on the road. https://arxiv.org/abs/2605.26494