#推理

我们正在分享一个针对纳维-斯托克斯千禧年大奖难题的解决方案，这是数学前沿最深刻的问题之一。 该证明由一组智能体完成，使用了OpenAI的下一代模型，其能力显著超越GPT-6 Astra。 该问题关乎由纳维-斯托克斯方程建模的光滑三维流体运动描述是否会失效。这一问题已悬而未决约90年。

我们正在分享一个针对纳维-斯托克斯千禧年大奖难题的解决方案，这是数学前沿最深刻的问题之一。 该证明由一组智能体完成，使用了OpenAI的下一代模型，其能力显著超越GPT-6 Astra。 该问题关乎由纳维-斯托克斯方程建模的光滑三维流体运动描述是否会失效。这一问题已悬而未决约90年。

我们正在分享一个针对纳维-斯托克斯千禧年大奖难题的解决方案，这是数学前沿最深刻的问题之一。 该证明由一组智能体完成，使用了OpenAI的下一代模型，其能力显著超越GPT-6 Astra。 该问题关乎由纳维-斯托克斯方程建模的光滑三维流体运动描述是否会失效。这一问题已悬而未决约90年。

This model represents a step-function improvement on many benchmarks, and its training is ongoing. Our internal model group arrived at the Navier–Stokes solution in 88 hours, using around 10,000 coordinating AI agents. Throughout the effort, we maintained the strict safeguards—including monitoring and isolation—that we apply to all our frontier evaluations.

我们正在分享一个针对纳维-斯托克斯千禧年大奖难题的解决方案，这是数学前沿最深刻的问题之一。 该证明由一组智能体完成，使用了OpenAI的下一代模型，其能力显著超越GPT-6 Astra。 该问题关乎由纳维-斯托克斯方程建模的光滑三维流体运动描述是否会失效。这一问题已悬而未决约90年。

🚀 Meet MiniCPM5-2B, a 2B-parameter language model bringing high intelligence density to the edge, now open source! It ranks #1 among open-source models under 4B parameters on the @ArtificialAnlys Intelligence Index, with a score of 23. It also scores 20 on the Agentic Index, bringing an early form of general-purpose agent capability to the edge. Across 34 benchmarks, MiniCPM5-2B achieves an average score of 53.9, covering coding, math, long-context understanding, tool use, and agentic tasks. And this release goes beyond the model itself. We’re opening up the data, training recipes, and RL stack behind MiniCPM5-2B. 🤗 Hugging Face: http://huggingface.co/openbmb/MiniCPM5-2B 💻 GitHub: http://github.com/OpenBMB/MiniCPM Modelscope: http://modelscope.cn/models/OpenBMB/MiniCPM5-2B Web: https://openbmb.cn/

🤝 Day-0 support for MiniCPM5-2B on stable vLLM. ⚡ Dense 2.6B model with 131K native context 🧠 Think / No-Think from the same checkpoint 🔧 Tool Calling support via vLLM’s minicpm5 parser Congrats @OpenBMB on the release, and thanks for keeping it on stock LlamaForCausalLM and opening the training data alongside the weights! 🙌 🔗 http://recipes.vllm.ai/openbmb/MiniCPM5-2B

OpenBMB's MiniCPM5-2B scores 15 on the Artificial Analysis Intelligence Index v4.2, the highest of any open weights model under 4B total parameters OpenBMB (@OpenBMB) is the open-source AI group behind the MiniCPM series of efficient small models. MiniCPM5-2B is a 2.6B parameter dense reasoning model with text input and output, released under Apache 2.0. Scoring 15 on the Intelligence Index, MiniCPM5-2B sits one point behind Ling 3.0 Tiny (16), which has ~3x the total parameters. Among open weights models under 4B total parameters, the next best score is Granite 4.2 3B (11). Key results: ➤ The highest Intelligence Index of any open weights model under 4B total parameters, setting a new Pareto-optimal point on Intelligence vs. Total Parameters: Its score of 15 is 4 points clear of Granite 4.2 3B (11). With 2.6B total parameters, it is 1 point ahead of Qwen3.5 4B (Reasoning, 14, estimated) with 44% fewer parameters, and level with Qwen3.5 9B (Reasoning, 15, estimated) at roughly 4x its size. As a dense model, its size advantage is in memory footprint rather than active-parameter compute. ➤ Strong agentic performance at this size: Its GDPval-AA v2 Elo of 831 leads <4B models, and on τ³-Banking it is joint-first with Ling 3.0 Tiny at 21%, compared to 8% for the next best model, Granite 4.2 8B. On AA-Briefcase, it placed second among the measured models in the comparison set with an Elo of 438, above Granite 4.2 8B (324) and just below Ling 3.0 Tiny (485). ➤ Knowledge, coding and long context are where it gives ground: MiniCPM5-2B places 7th in the set on Humanity's Last Exam (9%, behind Gemma 4 12B (Reasoning) at 16%), 8th on Terminal-Bench v2.1 (9%, behind Qwen3.5 9B (Reasoning) at 29%) and scores 0% on CritPt. On SciCode it is second of the five measured models at 26%, behind Granite 4.2 8B (31%). On AA-LCR v1.1 it scores 59%, 5th in the set, one point behind Ling 3.0 Tiny (60%). On GDP.pdf, our new professional document reasoning evaluation, it passes 1% of tasks outright, behind gpt-oss-20b (high) at 2%. ➤ Its AA-Omniscience score of -12 is earned by abstaining from answering rather than accuracy: MiniCPM5-2B attempts only 29% of AA-Omniscience questions, giving it a Non-Hallucination Rate of 78%. Its accuracy of 8% is a point below Ling 3.0 Tiny (9%) and half that of Qwen3.5 9B (Reasoning, 16%). Peers that attempt far more questions are penalized heavily, with Qwen3.5 9B (Reasoning) at -53 and gpt-oss-20b (high) at -63. ➤ It is token-efficient for a reasoning model: MiniCPM5-2B used 19k output tokens per Intelligence Index task, joint-lowest in the comparison model set with Granite 4.2 3B (19k). Ling 3.0 Tiny spends 56k, roughly 3x as many, for 1 more index point. Additional model details: ➤ Parameters: 2.6B (dense) ➤ Context window: 131k tokens ➤ Input modalities: Text only ➤ License: Apache 2.0

🚀 Hy4 preview is here. 770B, 49B active, 1M context. Built for productivity. Open source frontier. Consistent affordable price. Use it. Tell us what breaks. More on Hy blog：https://hy.tencent.ai/research/hy4-preview HuggingFace：https://huggingface.co/tencent/Hy4-preview Github：https://github.com/Tencent-Hunyuan/Hy4-preview

Excited to see the conversation around OpenAI’s Astra bringing recurrent architectures into the frontier spotlight. At Sapient, recurrence has been central to our vision from day one. We introduced HRM in June 2025 and open-sourced HRM-Text this May. It’s encouraging to see a direction we committed to early gaining wider attention. Want to explore recurrent reasoning yourself? HRM-Text is an open-source ~1B-parameter model trained from scratch on just 40B unique tokens, for about $1,000 in GPU compute-with a full pretraining framework to get you started. Explore the architecture. Train a model. Build on it. There’s still so much to explore in recurrent architectures. We’re continuing that work and building PRAXIST to help more researchers pursue ideas they believe in, just as we have with HRM.