MiniMax M3.1 Flash weights are now public. 428B parameters total, 23B activated per token. Agentic performance: A generational leap in agentic tasks, coding, and cybersecurity — 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym. Efficient by design: MiniMax Sparse Attention and DSpark speculative decoding improve inference efficiency. Hugging Face: huggingface.co/MiniMaxAI/Min… ModelScope: modelscope.cn/models/MiniMax… API: platform.minimax.io M Plan: minimax.io/m-planMiniMax MCode for desktop: agent.minimax.io/download

Next step: Longevity and Rejuvenation. We are almost there. Researchers in Bonn turned blood cells from an 80-year-old into neural stem cells with a molecular age below 20. More than 60 years younger by epigenetic clocks, which estimate age from chemical marks on DNA. The team directly reprogrammed blood precursor cells into neural stem cells, skipping the usual pluripotent stem cell stage. The rejuvenation unfolded gradually and could be tracked for over 100 days.

Introducing Ling-3.1-flash-1019 🚀 It scores 44 on the Artificial Analysis Intelligence Index, up from 41 for Ling-3.1-flash. Gains in agentic work: AA-Briefcase ~1400→1524 Elo and AutomationBench-AA 62%→68%. API access and open weights coming soon. Stay tuned.

We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.

I see where we are headed though now. We had the LLM then from LLM came the HARNESS that took us to AGENTS. Now we are into the AGENTIC world the next thing is the PHYSICAL which is ROBOTS. LLMs for the BRAIN and LLMs that will be for the Physical world. Robotic Agents !

When @jensenhuang asks for a somersault, you deliver. 🦆 See how AI agents on NVIDIA RTX Spark help bring @huggingface's Microduck from simulation to the real world.

🚨重磅爆料！GLM-5.4/5.5专家纪要流出：智谱下一代基座直接干到2.5-3.5T！还要Day1大规模供得上！有望比肩Claude Fable 5.1！ 一份在投资圈流传的专家访谈纪要显示，智谱下一代基座模型，市场传闻的GLM-5.4/5.5 细节比此前预期更激进： 🔹参数规模大幅超预期：并非此前传闻的1.6万亿，实际在2.5万亿至3.5万亿区间。相比今年8月发布的GLM-5.3（约7440亿参数），规模扩大至约3.4-4.7倍。 🔹架构更激进：既要训得快，也要推得快，同时把推理速度和成本做成硬约束。目标是发布首日就能大规模提供服务，已有多家云厂商就分成模式洽谈，智谱也补强了infra团队。 🔹训练曾遇阻但已解决：预训练阶段一度出现Loss不降（持续投入算力指标却无法继续改善），激进架构风险较高。但受访者认为问题最近已经解决，预计很快完成训练。 🔹性能预期旗舰级：从目前情况判断，下一代表现有望达到Claude Fable级别的旗舰水平。 🔹核心目标：“发得出、供得上”——从架构设计阶段就解决大模型发布后供应不足的问题。 目前市场仍在牌桌上奔着3T规模去，智谱这次在规模+推理成本+大规模供给上同时发力，但因训练波折时间有所推迟。 坐等正式测评验证能否真达到Fable旗舰水平。 #智谱 #GLM #大模型 #AI #Claude #人工智能 #GLM54 #GLM55 #智谱AI #唐杰 #Zcode #Zai

Running GLM-5.3-Flash on 2× DGX Spark with NVIDIA Flare ✨ - 1M context window, 2.32M-token shared KV pool - Prose decode, single stream: 117.2 tok/s - Prose decode, 2 concurrent: 167.4 tok/s aggregate - Prose decode, 4 concurrent: 215.8 tok/s aggregate - 32K prefill: 2,327.9 tok/s - Available memory after loading: ~13 GiB on head /worker each Checkpoint: GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD github.com/NVIDIA/flare-loca…

⚡️ Along with this version, we've also released our DFlash 2 drafter for Qwen3.8-Flash-Next! huggingface.co/incoai/Qwen3.…

Sage-A1.5 is here, bringing 256K context, parallel research, and evidence checks to long-horizon scientific workflows. 🤖 modelscope.ai/collections/In… 🏆 SOTA across scientific benchmarks: Scores 90.0 on PinchBench, 81.0 on FrontierScience-Olympiad, and 48.3 on FrontierScience-Research, ahead of Kimi-K3, GLM-5.3, and GPT-5.6-sol in the reported comparison. 🧩 Agentic Orchestration: When enabled, delegates independent investigations to parallel subagents with isolated contexts, then integrates their evidence and findings to guide subsequent work. ✅ Verification: Optional Verification Mode reviews progress, checks evidence and assumptions, and redirects stalled searches within the model's own reasoning, without a separate critic model at inference. 🔬 Open Scientific Discovery: Supports exploratory research through tool-assisted analysis and produces inspectable research artifacts, extending its use beyond predefined question answering. 🛠️ Native tool use for research: Supports APIs, code interpreters, search engines, and external tools for tool-augmented scientific reasoning and professional workflows. ⚡ Available in BF16, FP8, and GGUF F16/Q8/Q4 formats, with Transformers, vLLM, and SGLang support. 📜 Apache 2.0.

EAGLE-4 is here. From EAGLE-1 to EAGLE-3, we’ve kept making inference faster. EAGLE-4 is our fastest yet. 961.6 tokens/s on @Alibaba_Qwen’s Qwen3.8-27B with TokenSpeed (BF16, 4× @NVIDIA B300, TP4, one request, T=0). Github: github.com/eagle-hq/EAGLE-4