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Announcing Magistral, our first reasoning model designed to excel in domain-specific, transparent, and multilingual reasoning.

🚨 RAG is a popular approach but what happens when the retrieved sources provide conflicting information?🤔 We're excited to introduce our paper: “DRAGged into CONFLICTS: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs”🚀 A thread 🧵👇

We’re excited to introduce Text-to-LoRA: a Hypernetwork that generates task-specific LLM adapters (LoRAs) based on a text description of the task. Catch our presentation at #ICML2025! Paper: arxiv.org/abs/2506.06105 Code: github.com/SakanaAI/Text-… Biological systems are capable of

Thought real machine unlearning was impossible? We show that distilling a conventionally “unlearned” model creates a model resistant to relearning attacks. 𝐃𝐢𝐬𝐭𝐢𝐥𝐥𝐚𝐭𝐢𝐨𝐧 𝐦𝐚𝐤𝐞𝐬 𝐮𝐧𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐫𝐞𝐚𝐥.

Day 1/5 of #MiniMaxWeek: We’re open-sourcing MiniMax-M1, our latest LLM — setting new standards in long-context reasoning. - World’s longest context window: 1M-token input, 80k-token output - State-of-the-art agentic use among open-source models - RL at unmatched efficiency:

Gemini 2.5 Pro + 2.5 Flash are now stable and generally available. Plus, get a preview of Gemini 2.5 Flash-Lite, our fastest + most cost-efficient 2.5 model yet. 🔦 Exciting steps as we expand our 2.5 series of hybrid reasoning models that deliver amazing performance at the

Hello Gemini 2.5 Flash-Lite! So fast, it codes *each screen* on the fly (Neural OS concept 👇). The frontier isn't always about large models and beating benchmarks. In this case, a super fast & good model can unlock drastic use cases. Read more: blog.google/products/gemin…

Part 2 of this mystery. Spotted on reddit. In my test not 100% reproducible but still quite reproducible. 🤔

NEW RESEARCH: Approximating Language Model Training Data from Weights ever wonder how much information is available in an open-weights model? DeepSeek R1 weights are 1.2 TB... what can we learn from all those bits? our method reverses LLM finetuning to recover data: 🧵

Meet Kimi-Researcher - an autonomous agent that excels at multi-turn search and reasoning. Powered by k 1.5 and trained with end-to-end agentic RL. Achieved 26.9% pass@1 on Humanity's Last Exam, 69% pass@1 on xbench. 🔗 Tech blog：moonshotai.github.io/Kimi-Researche…

Excited to announce 🎵Magenta RealTime, the first open weights music generation model capable of real-time audio generation with real-time control. 👋 **Try Magenta RT on Colab TPUs**: colab.research.google.com/github/magenta… 👀 Blog post: g.co/magenta/rt 🧵 below

If you studied algorithms, I'm sure you've heard of Dijkstra’s algorithm to find the shortest paths between nodes in a weighted graph. Super useful in scenarios such as road networks, where it can determine the shortest route from a starting point to various destinations. It's

One appealing property of Seal-0 is that you need to search to achieve better performance, but the more you search, the more conflicting evidence you encounter. This poses a major challenge for deep research agents / frontier LLMs. Congrats Kimi.ai on the strong results!

We’re bringing powerful AI directly onto robots with Gemini Robotics On-Device. 🤖 It’s our first vision-language-action model to help make robots faster, highly efficient, and adaptable to new tasks and environments - without needing a constant internet connection. 🧵

+1 for "context engineering" over "prompt engineering". People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window

Thrilled to introduce AlphaGenome, our new DNA sequence model now available via our AlphaGenome API. Really excited to see how the scientific community uses AlphaGenome’s predictions to understand genome function, drive biological discoveries, develop new treatments, and more...

Excited to share that our paper on model merging at scale has been accepted to Transactions on Machine Learning Research (TMLR). Huge congrats to my intern Prateek Yadav and our awesome co-authors Jonathan Lai, Alexandra Chronopoulou, Manaal Faruqui, Mohit Bansal, and Tsendsuren 🎉!!