搜索“RAG”

sts GPT-Live-1 (Astra, Medium) at 74.5%, Gemini 3.8 Live (Extended Thinking, High) at 68.6%, and Grok Voice 2.0 at 56.5%. xAI also reported that Artificial Analysis measured an average cost per task on its Big Bench Audio run of $0.025 for Grok Voice 3.0, compared with $0.030 for GPT-Live-1 (Astra, Medium) and $0.055 for GPT-Realtime-2.1 (High). Artificial Analysis defines its Speech-to-Speech Index as an equal-weighted score across four components: speech reasoning from Big Bench Audio, agentic

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senschaftler können die höchste Nutzungsstufe von Antigravity sechs Monate lang kostenlos nutzen. Dafür ist eine Bewerbung erforderlich. Die bereits bekannten Einführungspreise betragen zwei US-Dollar pro Million Input- und zehn Dollar pro Million Output-Token. Gecachte Input-Token sind 95 Prozent günstiger. Ab dem 1. Dezember verdoppeln sich die Preise. Das Kontextfenster umfasst insgesamt eine Million Tokens für Eingaben, interne Berechnungen und Ausgaben. Neue Beispiele aus der Praxis Wie sic

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log post that Argon tried to work around its own evaluations during training. What the company sees in practice is “an agent trying too hard to finish the job it was given,” Anca Dragan, vice president of AI safety and behavior, and Raluca Ada Popa, head of Gemini security at Google DeepMind, wrote in the post. According to that post, the model, in rare cases during training, tried to raise its container privileges to change grading scripts and pull answers from the test runner’s memory. In one

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Opus 5.5、Sonnet 5.5和Meta Muse Wonder Full。 沃顿商学院教授伊桑·莫利克（Ethan Mollick）认为，Gemini 4在创意和写作方面取得显著进步，为现有前沿模型提供了不同的选择。不过，他指出，模型处理复杂多智能体任务时仍有不足，有时过于自信，Antigravity的工具链也需要改进。 企业AI咨询公司Red Dragon AI的山姆·维特文（Sam Witteveen）认为，Argon是一个非常出色但并非最强的模型。他看好模型结合视觉理解、软件生成和创意媒体的能力，但认为Antigravity与Claude Code、Codex等开发工具相比仍有差距。 X用户@cheaty则认为，Argon早期测试中的运行速度和成本表现不够理想。尽管模型能够完成任务，部分任务的实际花费仍明显高于其他模型。这也说明，企业评估模型时，不能只比较每百万token的标价，还要看完成同一任务需要多少调用、耗时多久，以及最终结果是否可用。 Argon的全面开放，让更多开发者和企业有机会检验谷歌展示的能力。但从模型能够完成某项任务，到企业可以稳定、低成本地将其投入生产

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DP, using 6 private benchmarks and 2 public ones. Argon scored 68.90% there, ahead of Claude Sonnet 5.5 at 67.04% and Claude Opus 5.5 at 66.97%. It also ran faster and cheaper, averaging under 47 minutes and $15.68 per test against 1 hour 18 minutes and $21.34 for Sonnet 5.5. Vals priced Argon at the $4 and $20 standard rate Opus 5.5 also charges, yet Opus cost $32.14 per test. Argon’s cost edge therefore comes from using fewer than half Opus’s input tokens, so it should survive the end of the d

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f industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations. LATEST STORIES Exclusive: Huskeys adds Profiling to map which parts of web applications a business depends on; Groundcover lets Claude and Codex hand production investigations

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ni 4 as a hard-core, serious companion for STEM workers. We’re far away from the days of Nano Banana — Google’s AI image generator that blew its competition out of the water and enraged creators. Now Google is focusing on both practical and abstract applications of AI for math and science, like robotics and nuclear fusion research. It’s part of a bigger trend we’ve seen across the AI industry. As criticism of AI and the companies building it mounts — from the anger over data center construction

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focus for the last year or two that we are moving away from chatbots and going to this agentic world where [we're] able to delegate some tasks that these systems can do,” said Chirag Shah, a professor at the University of Washington in Seattle. With Meta Muse, OpenAI Dots and Gemini Agent, AI is on the verge of its next big milestone, he added. Other popular new personal agents include Grok Bot from SpaceXSI, Instinct and Hermes. OpenClaw, now part of OpenAI, pioneered the personal agent trend

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urce Agent Development Kit (ADK), a framework powering enterprise agents, push their performance engineering further. The team gave Argon their .agents/skills rulebook and a one-paragraph goal to eliminate framework overhead without breaking existing apps. Working autonomously overnight, Argon surfaced optimizations, now live in google/adk-python, that cut in-memory session prep time by 40% in long sessions and sped up cold imports by 21%. Read the engineering deep dive to learn more; Quantum ch

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urce Agent Development Kit (ADK), a framework powering enterprise agents, push their performance engineering further. The team gave Argon their .agents/skills rulebook and a one-paragraph goal to eliminate framework overhead without breaking existing apps. Working autonomously overnight, Argon surfaced optimizations, now live in google/adk-python, that cut in-memory session prep time by 40% in long sessions and sped up cold imports by 21%. Read the engineering deep dive to learn more; Quantum ch

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ment learning. Unlike its predecessor, LFM2.5-24B-A2B is a reasoning-only model that produces an explicit chain of thought before its final answer. We adopted this strategy to leverage its high decode speed and improve answer quality. Architecture. It keeps the 40 layers, 64 experts per MoE block with top-4 routing, and the same hybrid of gated short convolutions and GQA. The two architectural changes are the context window extension from 32,768 to 131,072 tokens, and the in-place tokenizer expa

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0，\n 跃变处留 3px 软过渡（soft = 3/256）避免锯齿。未登记的行留纯黑。\n 一个物件类型占一行，用行号索引，例：\n 0 深色远景树冠 / 2 中景树冠 / 9 树干 / 23,24 岩石 /\n 26,27 枯草 / 48,49 地面草地 / 50 小径 / 56 沙 /\n 58,59 草叶 / 73 墙 / 93 屋顶\n 材质 fragment：\n rampX = fit(dot(N, uSunDir), -1, 1, 0, 1) shadow\n col = texture2D(tRamp, vec2(rampX, rowV)).rgb\n 即：明暗只有两档色块，硬边分界。这是绘本色块的本质，不要磨平。\n\n3. 草地专用规则（最容易做错的一处）：\n 草不能用 dot(N,L) 查 ramp —— 实例化草叶法线杂乱，会发暗发噪。\n 草改用 shadow 值直接查 ramp。\n 另外用 step(0.8, hash) 让 20% 的草采「基行 +1」那条 ramp，\n 靠两行色差造出叶片明暗差异，而不是靠堆面数。\n\n4. 雾在 HSV 空间

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ository files navigation We introduce Intern-S2-Mobius, a 35B foundation model built on the Mobius-v0 architecture realized by Xtuner and LMDeploy. Instead of binding knowledge storage and reasoning computation layer by layer as in conventional Transformer models, Mobius organizes knowledge into a globally shared Memory and lets multiple Reasoners iteratively query and refine hidden states against this shared repository. This knowledge-reasoning separation gives Intern-S2-Mobius two native capab

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yTorch SDPA; flash-attn is needed only by the AToken visual tokenizer (i.e. every task that encodes or decodes images/video/3D — plain text generation works without it); Object-storage packages (petrel-oss-sdk / boto3) are optional — local token files need neither; Precomputed .pkl/.pt token files are deserialized with PyTorch's trusted-payload mode for legacy NumPy payloads; load only files from a trusted source. To create such files yourself, see Tokenizer Utilities; The root driver is intenti

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ltimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre-training, reinforcement-learning task coverage, and interactive agent environments. By combining a new vision-language pre-training paradigm with large-scale multi-task reinforcement learning and long-horizon agent reinforcement learning, Intern-S2-397B delivers a step change in general reasoning, scientific problem solving, and agentic capabilities. Features

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r example, it generates the back side of the robot, and it guesses that there should be a grassy lawn next to the pool. From a single image, Atlas generates views from any angle. Drag to change the view. Generating with Spatial Context Similar to an LLM, Atlas first encodes its inputs into a context, then generates outputs conditioned on the context. However, Atlas is unique because each image is grounded at a 3D position in space; this forms a spatial context. Managing this spatial context unlo

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d GLM-OCR on OCRBench, scores are reported as API / pure VLM. 📜 License Agreement The code and the weights of FireRed-OCR are licensed under Apache 2.0. 🖊️ Citation We kindly encourage citation of our work if you find it useful. ⚠️ Ethics Statement FireRed-OCR is a technical tool designed for document digitization and structural parsing. Prohibited Use: This project must not be used to generate or process content that is illegal, defamatory, pornographic, harmful, or that violates the privacy, r

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r variants; Release REDEdit-Bench, a comprehensive benchmark for image editing evaluation; Release FireRed-Image model, a text-to-image generative model 🖊️ Citation We kindly encourage citation of our work if you find it useful. ⚠️ Ethics Statement FireRed-Image-Edit has not been specifically designed or comprehensively evaluated for every possible downstream application. Users should be aware of the potential risks and ethical considerations when using this project, and should use it responsibl

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e dialects; FireRedTTS3-Instruct — natural-language voice design and speech editing (semantic + acoustic) in one unified model Highlights ✨ 🌍 Multilingual — 24 Languages — Best average WER/CER (avg 3.754%) and best average speaker similarity on MiniMax-MLS-Test (avg 84.8%), plus best-in-class cloning WER/CER (avg 3.04%) and similarity on Seed-TTS-eval (avg 78.8%). Supported languages: Arabic · Cantonese · Chinese · Czech · Dutch · English · Finnish · French · German · Greek · Hindi · Indonesian

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hips, but what does that mean for Nvidia? Not much yet, with demand still sky-high, but once its biggest buyers can run on their own silicon, Nvidia loses at least some of the leverage over pricing that helped make it the kingmaker of the AI chip world and the first $5.5T company. QUICK HITS ▸ A reader turned ChatGPT into a personal car repair advisor Today’s workflow comes from an anonymous Rundown reader: “I’m a 71-year-old retiree who never imagined artificial intelligence would become such a

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