搜索“RAG”

te, Qwen's next-generation real-time simultaneous interpretation model! 📢 Built on an Interleave architecture, it improves faithfulness, fluency, and conciseness while reducing average lagging (LAAL) from 2.8s to 2.3s across 60 languages. New capabilities: 🙌 - Real-time speaker diarization — distinguishes speakers in multi-party speech and preserves each speaker's voice through more stable voice cloning. - Synchronized bilingual display — source and translation on screen together. - Long-context

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. Qwen3.8-LiveTranslate rebuilds real-time simultaneous interpretation with an Interleave architecture, improving faithfulness, fluency, and conciseness across the board, while average lagging (LAAL) drops from 2.8 seconds to 2.3 seconds. We want simultaneous interpretation to convey not only language but also the people and context behind a conversation. Building on support for 60 languages, Qwen3.8-LiveTranslate adds three capabilities that bring simultaneous interpretation to a wider range of

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ong workflows to auto-edit vlogs, translate short videos, and turn movies into recaps. - A major leap: approaching Gemini 3.8 Flash in audio-video capabilities; +19.5 points on average in agent performance across WildClawBench-MM & UniClawBench. - 1M-token context with agentic perception: actively explore long videos and locate key moments with higher accuracy, using 51.8% fewer tokens than static understanding on OmniVideoBench. Video input costs are reduced by about 89% compared with Qwen3.5-O

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he KV cache optimizations DeepSeek improved the KV Cache utilization on DeepSeek-V4.1-Flash compared to previous generation DeepSeek V4, only consuming ¼ the HBM, and ⅛ the SSD Storage. Figure 2: KV Cache on DeepSeek-V4.1-Flash This new KV Cache optimization unlocks better economics for your coding agents. From the DeepSWE run, we had the following economics: Cost ComponentTokens/TaskRateCost/taskShareUncached input148,741$0.22/M$0.03277.6%Cached input36,744,586$0.007/M$0.257259.9%Output211,513$

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system North Small Translate outperforms similarly sized open-weight models under 1T parameters and API-based translation models in various dimensions of machine translation on average. In WMT model evaluations, North Small Translate leads with an WMT26 All Languages benchmark score of 83.60, compared with 81.56 for Qwen 3.5 397B A17B, 76.50 for GLM 5.2 FP8, 81.37 for DeepL NextGen, 79.46 for Gemma 4 31B (on), and 68.20 for Google Translate. North Small Translate (Agentic) — which can find error

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alongside fewer detours and redundant reads. On FrontierCode 1.1 Main, we see that SWE-2 medium scores higher than SWE-1.7 while taking 58% fewer turns and costing 81% less on average. SWE-1.7 vs. SWE-2 on FrontierCode 1.1 Main: MeanSteps per run Explore (read / grep / ls); Plan / todo; Write / edit code; Build (make / lint); Run tests; git add / commit; Final message In our previous post2, we observed SWE-1.7 as being exceedingly careful through its thorough exploration of the codebase before

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ade production. In this intensive series, you will: Code agent state transitions in Python with Google's ADK, with zero vendor lock-in Build long-term agent memory with multimodal RAG and session persistence Deploy secure, rate-limit-free agents at enterprise scale on Cloud Run Register for the technical series . Image source: The Rundown’s YouTube interview with Sundar Rowan: I recently had the opportunity to sit down with Google CEO Sundar Pichai for an exclusive interview , and one line has s

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enges a few years ago have become tools people can actually use. One example is Computer Use - if you’ve tried this before and felt like it was too slow or not good enough, I encourage you to give it another shot. We’ve come a long way since Operator, and it “just works” now. We’re also asking these systems to act on your behalf for more consequential work. Agents needs to stay aligned with your goals and values, think transparently, and respond to oversight even when tasks become difficult. We’

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T-Transcribe, Whisper V3-Large, and ScribeV2, while also handling a broader range of real‑world audio. Our model ranks first on the FLEURS benchmark across 60 languages with an average Word-Error-Rate of 5.2%, defines the Pareto Frontier for accuracy and latency on Artificial Analysis, and ranks second on the Artificial Analysis Word-Error-Rate leaderboard, continuing the hill-climbing from previous versions. Solve more challenges with a single model From clinical note-taking to legal documentat

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ove over or focus a timeline bar to see its full prompt. The timestamped event stream. The clip is the model's output for this prompt — play it and the timeline follows. Click or drag on the timeline to seek. Actions overlap freely, and speech is an action like any other, carrying the line to be said. Real-Time Generation The finetuned model is still too slow, and cannot yet generate autoregressively because it is still bidirectional. Therefore, we post-train the bidirectional model into a real-

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ng of that reasoning at launch. Chief Scientist Jakub Pachocki posted that he wants to "prevent a race into unmonitorability" from "confused reporting," while calling monitoring "fragile." Why it matters: The alarm bells are less about Astra, which OAI says still shows its work, and more about a performance race that tempts the industry to loop harder and monitor less. Pachocki himself admits current monitoring is already fraying for reasons unrelated to Astra, so the main window into what model

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AI's Astra isn't far behind — meaning the summer of caution is about to get its first stress test. Reminder: Our next live workshop is today at 1 PM EST. Join and learn how to leverage AI as a manager to get more value out of your 1:1 meetings with employees and improve your team’s performance. RSVP here . In today’s AI rundown: Fable 5.1 ends the frontier's quiet period Bernie Sanders escalates AI pause conversation How to pick a dedicated AI device Apple, OpenAI trade blame in legal battle LAT

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sees. Imagine browsing a virtual clothing store where the showroom itself is the interface. Using a single image of yourself as a reference, you can pick up a shirt from a rack, drag it onto yourself to try it on or rearrange the display as naturally as you would in a physical store. Second, it is alive. Because the application is continuously rendered, it is always evolving rather than waiting for the next user action. Reflections shift with the lighting, and objects respond naturally as they'

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pire 业务重分类与盈利重构 🎬 视频演示（见公众号原文 ） 演示说明：本 Demo 仅可视化展示当前示例任务的处理轨迹，不代表模型的全量能力、所有任务流程或最终产品界面；演示内容仅供参考，不构成任何投资建议或决策依据 在 Spire 案例中，Ling-3.0-flash-Fin 通过 55 次工具调用，逐项核对 10-K、业绩公告和 8-K，识别 Storage 转为终止经营后的口径变化，重新拆分原 Midstream 盈利，并沿 Adjusted、Other 和 GAAP 三条路径交叉勾稽。模型最终确认持续经营业务的历史盈利为 1,780 万美元，并明确该数据不能直接作为后续预测基线。 04 估值建模：理解财务模型的复杂勾稽，实现模型自动更新交付 财报季期间，分析师往往需要在公司披露业绩后，第一时间更新数十家公司的财务模型，整理分析图表并准备研究报告。跨文档取数、实际值覆盖、历史期与预测期公式切换、财务勾稽、异常排查和可视化制作环环相扣，不仅工作量大，也容易因口径或公式问题产生错误。 SpreadsheetBench V1 主要考察真实工作簿中的单元格、公式和格式操作；Sprea

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nd your canvas (outpainting); Using personalization, moodboards and srefs (moodboards/srefs may work best with some extra prompt direction in this version) Ways to use the model: Drag images into the prompt bar under "attach to prompt”; Click ‘edit’ in the lower right in the lightbox; Click the ‘edit’ tab on the left and upload an image; Type --edit url... on Discord We’ve updated UIs on both the main midjourney.com website and alpha.midjourney.com, alpha will be changing rapidly with lots of ex

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es of enterprise documents. It converts complex, multimodal files into structured, machine-readable data that can power enterprise knowledge use cases–including document indexing, RAG, and agentic retrieval. More than text recognition, Parse detects and understands key visual elements - such as tables and embedded images - and returns clean Markdown files for downstream processing and application. Use Parse to process your documents and images across nine major world languages. Parse is designed

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dor spend report out of QuickBooks and picked out the real vendors versus the noise. Computer built me a script that cleaned up messy vendor names. 133 vendors, 1,463 subfolders, dragged to OneDrive, and the count matched exactly.” See Brian’s full workflow Visit The Rundown University . How do you use AI? Tell us for a chance to be featured. 🚀 GLM-3.5-Flash - Z AI’s new model revealed as the mystery Ox Alpha 🎥 H3 Max - Fal's top-ranked post-trained video model tuned for speed and quality 🗣️ Gem

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re not applicable. Vision Language Qwen3.8-Flash-Next Qwen3.8-27B Qwen3.7-Plus Claude-Opus-4.6 (Max) Agentic Multimodal Intelligence Multimodal tool use ClawEval-MM Pass@3 64.4 Average 60.4 Pass@3 57.4 Average 56.9 Pass@3 57.4 Average 60.1 Pass@3 52.5 Average 54.7 Application recreation RecreationBench 49.9 47.1 30.2 -- Mobile use AndroidWorld 84.5 81.9 81.0 62.0 Computer use OSWorld 2.0 Binary 19.4 Partial 52.3 Binary 19.4 Partial 48.0 Binary 2.8 Partial 21.5 -- Visual web development Vision2We

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emini app on macOS using just your voice. See how Gemini 3.5 Transcribe uses Rambler on Android to automatically remove filler words and clean up speech. Gemini 3.5 Transcribe leverages screen context on Google Antigravity to ensure accurate transcription accuracy. By leveraging the Gemini Live API, developer platforms such as Agora , Fishjam , LangChain , LiveKit , Pipecat , Vercel , and Vision Agents enable developers to build and deploy high-performance voice-driven interfaces with ease. Thes

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A reads the selected entries from the original attention K/V cache. The indexer projects four 128-dimensional query heads and one shared key head. Every four raw index keys are averaged in FP32, normalized, and rotated with the first token's MRoPE position to form one compressed key. A query scores the visible compressed blocks with QSA keeps the best 512 blocks, expands them back to 2048 logical token positions, and appends the zero-to-three tokens in the current incomplete block. The final spa

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