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10月22日周四
  2. IT之家·人工智能66

    讯飞星火 X2.6 端云一体大模型发布，首次基于全国产算力实现代码和智能体能力突破

    科大讯飞在全球 1024 开发者节发布讯飞星火 X2.6 端云一体大模型，宣称首次基于全国产算力实现代码和智能体能力突破。模型采用 293B-A30B 参数的 MoE 架构，上下文长度拓展至 1M，输入限时 0.9 元/百万 tokens，输出限时 3.6 元/百万 tokens；同时开源星火 X2.6-4B 端侧模型，新增三档推理模式，推理定价限时免费。

  10. Rohan Paul64

    Recursive 发布首个模型 Flint，一个 40B MoE 本地编码模型，由其自动化研究系统 Eureka Machine 构建，从零开始用时不到 4 个月，权重以 Apache 2.0 开放。

    引用Richard Socher@RichardSocher

    Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

10月21日周三
  9. Rohan Paul54

    Coco Labs 的 AI 设计模型 Vyb 按图层逐个元素构建设计，文字保持可编辑文本、图形保持矢量、照片保持像素，并从网站自动提取 logo、字体和配色。在 145 款 Google 字体上按像素重叠 IoU 打分，Vyb 达到 90.3%，对比中 最强图像模型为 76.0%，该测试的测量上限为 94.4%。由于每层保留原生格式，导出 Figma 时结构完整保留。

    引用Anusha Ramesh@anurahk

    I launched the first image and video generation models at Google, and kept asking why all AI designs look the same. AI has brought us the WORST era of design. It made content creation effortless, but it also made 90% of it look painfully generic. My co-founder and I spent 2 years building in stealth to change that. Meet Vyb, the first AI design model that understands your taste like a designer would. RT + comment your website URL and we’ll make your first design.

  11. Arena.ai60

    Arena 宣布 Meta 的 Muse Wonder Full (Max) 在 Agent Arena 排名第 7，净提升 +11.3%，基于超 6.5K 真实智能体会话，将 Meta 推至实验室第 4 位。

    引用Arena.ai@arena

    Exciting news: Muse Wonder Full (Max) by @AIatMeta has just debuted in the Code Arena: WebDev at #6 overall with 1726 pts, putting Meta in the top 3 labs! This is +75 pts and 11 places above Muse Wonder (Max) at #17 (1651 pts), and +67 pts and 8 places above Muse Spark 1.3 (Max) at #14 (1659 pts). Muse Wonder Full (#6) also landed +16 pts above Claude Sonnet 5.5 (#17), and +48 pts above Gemini 4 Argon (#10). With this score, Muse took the #3 lab spot (previously held by Google). Across categories, Muse Wonder Full (Max) landed: - #4 in Data & Analytics and in Consumer Product - #5 in Brand & Marketing - #6 in Content Creation Tools - #7 in Gaming and in Simulations - #8 in Reference-Based Design Congrats to the @AIatMeta team and @alexandr_wang on this release!

  12. Rohan Paul54

    SoMark 团队发布开源文档解析模型 OXR（1.3B 参数），将 PDF 和文档图像转为含布局坐标的 Markdown 或 JSON 的 Agent-Friendly 数据。

    引用SoMark@somarkai

    Beyond OCR. Meet OXR. Only 1.3B. Open source. #1 on olmOCR-Bench, OmniDocBench & DocParsingBench🏆 #1 in parsing speed among open-source models⚡ RSI-powered. A first in document parsing. Turn documents into Agent-Friendly data. If OXR helps, give us a star on GitHub! 🌟

  15. Arena.ai57

    Arena 更新 Agent Arena Pareto 前沿，OpenAI 的 GPT-6 Luna 和 GPT-6.1 Sol 在不同推理档位下占据 11 个前沿位置中的 6 个，加上 GPT-6 Astra (Max) 共 7 席。

    引用Arena.ai@arena

    Update to the Agent Arena Pareto frontier: GPT-6 Luna (Max) has landed on the Agent Arena Pareto frontier with +1.59% net improvement at a median cost of just $0.05 per task! Luna costs 29% less than DeepSeek-V4.1-Flash and 76% less than Hy4 preview, while trailing their net-improvement scores by just 2.27 and 2.51 percentage points respectively. Compared to other GPT models on the Pareto frontier, GPT-6 Luna (Max) costs 94% less than GPT-6 Sol (Max), and 98% less than GPT-6 Astra (Max). Congrats to the @OpenAI team on this cost-efficient release!

  17. fal68

    fal 发布与 Black Forest Labs 合作后训练的视频模型 FLUX 3 Max，在第三方独立人类偏好评测中获 1139 Elo，与 Seedance 2.5 并列图生视频质量第一梯队。该模型约 12 秒生成一段 10 秒 720/768p 视频，未来三周降价 75%，后续还将继续后训练改进。

    引用fal@fal

    隆重推出 FLUX 3 Max，这是 fal Research 与 @bfl_ai 合作推出的最新后训练视频模型。 在独立第三方基准测试中，FLUX 3 Max 在图生视频质量上并列第一，可在约 12 秒内生成一段 10 秒、720/768p 的视频。 在接下来的三周内，FLUX 3 Max 享受 75% 折扣，使其成为同等质量水平下最实惠的前沿视频模型。

    推荐理由：原文给出第三方 Elo 分数和生成速度数据，读者可据此判断该模型在图生视频梯队中的位置。