Liquid AI 放了个端侧友好模型,24B 总参但只激活 2B,M5 Max 上能跑到 184 tokens/s,跑本地智能体可以试试
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物理评测的标准答案自己都错了,GPT-6 Astra 反而是对的,最后拿 98%,这条挺有意思的。
蚂蚁又开源了一个大模型,560B 总参但每个 token 只跑 25B,还能塞 100 万 token 上下文,MIT 协议想怎么商用都行。
中国机器人公司 Nature Will 拿 100 万小时人类视频训练了个 50B 的 MoE 模型,专门用来控制 20 自由度的灵巧手,思路挺有意思。
Google 的 Gemini 4 Argon 今天正式 GA 了,两周前的基准成绩这次被 CursorBench 等第三方独立复现,想用的可以直接上手。
Meta 新模型 Muse Wonder (Max) 的实测排名出来了,智能体任务比上一代强不少,但价格贵了一倍多,文本榜冲到第 2,数据都在这。
The result measures long-tail factual recall plus 3D generation quality in a single offline benchmark, a combination few evals test. Builders comparing frontier models on spatial reasoning and world knowledge can use the same sandboxed prompt setup for their own side-by-side runs. Claude Opus 5.5 (Anthropic) edged out Grok 4.7 (xAI) on geographic accuracy of the name placement.
有人吐槽物理基准 CritPt 被 Astra 刚发布就刷爆,顺便夸了 OpenAI 的 RL 训练管线,聊前沿模型饱和速度的可以看看。
一个才 18M 的 OCR 模型,CPU 就能跑,每秒吞 49 页,做截图取字功能正合适,表格和手写就别指望它了。
The MI355X's 288GB of HBM3E per GPU gives it an edge on very large models like the 1.6T-parameter DeepSeek V4 Pro, but serving-stack maturity on ROCm remains the bottleneck at higher loads. Teams evaluating accelerator purchases for agentic inference can now compare AMD and NVIDIA on realistic coding-agent workloads rather than synthetic throughput numbers. All results run on ROCm nightly builds of vLLM and SGLang, so scores should improve as the software stack matures.
Meta entering the agentic model space puts it in direct competition with OpenAI's operator-style agents and Anthropic's computer-use models, rather than just chat assistants. Developers building personal-assistant or workplace automation products should read the researcher blog to compare benchmark scores against their current agent stack before choosing a base model.
语音模型又卷起来了,这个延迟不到 1 秒,价格还没涨,做语音智能体的可以去看看 benchmark 数据。
OpenRouter 上架了 Jev 1.14,输出免费输入才 $0.042/M,还加了看图能力和多语言改进,性价比党可以试试。
Kombaico 做了个测 AI 设计创造力的榜单,GPT-6 Astra 才拿 65.5 分,人类是 92.3,模型们设计得都挺像,论文和代码都开源了。
Google 的 Embedding Gemma 2 冲上 Hugging Face 热门第一,HF 老大趁机吐槽:别只盯着推理模型,300 多万个公开模型里好东西多着呢
有人拿 Devin 当 Agent,让 Opus 5.5 和 Gemini 4 Argon 各做一段视频,猜猜哪段是谁做的,评论区吵翻了。
昆仑万维这个 Matrix-Game 4.0 用一张 RTX 5090 就能跑 60 帧的视频生成游戏,延迟不到 150 毫秒,还开源了,做游戏原型的可以试试。
StepFun 新模型 Step 5 Preview 上线当天就能在 vLLM 上跑,稀疏注意力加 MoE,Blackwell 和 Hopper 都能部署,附内核实测数据。
Datalab 放出一个开源 OCR 基准,16,288 个可判断对错的测试,覆盖 92 种语言,选 OCR 解析器前可以先跑一遍看看谁过得多。
一家叫 Ricursive Intelligence 的初创公司想让 AI 自己设计芯片,每轮迭代还能自我改进,思路挺新鲜,可以看看他们怎么讲。
A cost gap of roughly 60x per task against Claude Fable 5, while scoring higher on a deliverables benchmark, could push professional-services firms to re-evaluate which workloads run on open models. Builders fine-tuning on top of GLM-5.3 Flash now have a concrete vertical example of post-training for rubric-graded outputs. Its open weights also make on-premise deployment viable for firms with client confidentiality constraints.
Cline 公布了 Gemini 4 Argon 的真实使用数据:首日用户是 Claude 新版的三倍,还省 30% token,编程党可以关注下。
阶跃把 Step 5 Preview 的权重直接放出来了,600B 激活 27B 还能吃 100 万 token 上下文,OpenRouter 调用量已经全球第二,想自建 Agent 的可以上手试试。
Jason Wei 团队拿现成的健康数据,把散落在各领域的专业知识连起来挖出新科学洞见,思路值得琢磨。
一个 5 人团队花 30 万美元训出的开源模型 Ultramarine 1 Flash,主打知识工作,性能对标 Fable 5 还便宜 60 倍,API 已上线。
一篇教游戏智能体超越演示数据的新方法,用16个合成环境做强化学习,MineDojo-300成功率从12.63%拉到38.33%,做agent训练的可以看看。
中国团队用自研大模型 S2 拿下了困扰30年的 Ghys 猜想,北大团队还做了形式化验证,代码都开源了,数学+AI的人值得看看。
有人爆料 Intern-S2 底层其实是 Qwen 3.5-397B-A17B 后训练的,还拿它和 OpenAI 的研究路线做了对比,搞开源模型的朋友可以看看。
有人说 GPT-6 Astra 和 Opus 5.5 在创意内容上的提升,就像当年 Opus 4.5 改变编程那样,做内容的朋友可以留意
一家小团队花 1.33 万美元从零训了个泰米尔语小模型,8 张 H100 六天搞定,还开源了权重和训练细节,低资源语言玩家可以抄作业。
The result suggests the next speed leap may come from changing generation granularity rather than from bigger GPUs: drafting concepts first, then filling in tokens. Researchers and engineers comparing sampling strategies for diffusion and autoregressive models can benchmark NCM against baselines like diffusion language models using far fewer training tokens. As it comes from the Stanford Hazy Research group, expect open follow-ups and code to appear in the open-source model ecosystem.
Progress on a single long-horizon game is a proxy for how quickly agentic planning and decision-making capabilities are moving, and SpireClimb gives teams a repeatable way to measure that gap across model releases. Practitioners benchmarking agents on sequential decision tasks can use the difficulty tiers to compare models beyond static Q&A tests. Tracking which labs' models clear the hardest difficulty will be a useful signal in coming releases.
最难的代码任务可以扔给 Muse Wonder Full,Wang 说它强过 Astra,略输 Claude Code Fable。
语音对话延迟压到 1 秒内,综合分超 GPT-Live-1,Tesla 车机已在用,做语音应用可对比价格。
中国跑分最高的是 Qwen 3.8 Max,追平 Haiku,被 Luna 拉开一截,9 月新版还退步了。
有开发者把 Argon 用在生产环境跑数据分析和 Excel,说质量比 Opus 强、价格还更低。
The notable shift is self-repair: instead of a fixed pipeline, the robot's manipulation code improves itself each time execution fails, so errors become training signal without human relabeling. Practitioners working on robot manipulation or agentic tool-building can study how error messages drive function patching in a physical loop. Compared with SAM2, pairing SAM3 with RGB-D gives this setup grounding in real 3D workspaces.
Gemini 4 Argon 全球上线,Cursor 接入价 $2/$10,Liquid AI 开源了 24B MoE。
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