搜索“RAG”

load, and broader model, hardware, streaming, TTS, and quantization support. [2026/07] We released 0.24.0 - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support. [2026/06] Starting with 0.14.0, vLLM-Omni publishe

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l/sgl-router protoproto pythonpython rustrust scriptsscripts sgl-model-gatewaysgl-model-gateway testtest tools/sglang-simulatortools/sglang-simulator .codespellrc.codespellrc .coveragerc.coveragerc .dockerignore.dockerignore .git-blame-ignore-revs.git-blame-ignore-revs .gitignore.gitignore .isort.cfg.isort.cfg .pre-commit-config.yaml.pre-commit-config.yaml CODE OF CONDUCT.mdCODE OF CONDUCT.md LICENSELICENSE README.mdREADME.md 仓库文件导航SGLang：面向大语言模型和多模态模型的快速推理 文档 | 示例手册 | 网站 | 博客 | Slack SGLang 是一个

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problem is not that credit profiles have changed in the last year, it’s that we’ve got another year and a half of supply coming,” Bloomberg quoted John Yovanovic , co-head of leveraged finance at MetLife Investment Management, as saying. I have seen people suggest that this means the AI bubble is bursting. I am not so sure. A plunging stock price is not necessarily financial doom. Stocks are an expression of confidence in a company’s future, not a reflection of day-to-day business. But CoreWeav

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for the humans who are left. That’s a far cry from the future promised by Google , Anthropic , and OpenAI , in which AI agents will give consumers greater power and bargaining leverage. But that promise is not outlandish. Agents are capable of genuinely helping people by fighting bogus medical bills or researching more affordable car insurance, for example. Whether they can actually be used for these purposes depends on what rules are set for how agents interact with websites and what exactly th

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ree, and actions target element refs from it. Coordinate clicks only come with --caps=vision. About two dozen tools are on by default, with more behind --caps: network mocking, storage, tracing, PDFs, test assertions. By default it starts its own browser with a persistent profile per workspace, so logins stick between sessions. For a clean start every time, add --isolated. To use the Chrome or Edge you already have open, add --extension and install the Playwright Extension. --browser also gets y

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nters. According to the documents reviewed by 404 Media, TCEQ issued air-emission limit certificates to a large data center in Houston; approved the construction of stormwater coverage for data centers in Abilene, Barstow, and Royse City; and fast tracked gas power plants for more than a dozen data center projects. “The TCEQ’s own data indicates that it’s business as usual for data centers and affected communities were promised a freeze. And instead what they’re getting is another reason to dist

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n JevBench, Drex Max reaches 92.2 against 86.7 for Jev 1.14. Agentic benchmarks: Drex Max posts 0.780 on OSWorld-Verified and 98.2 on BFCL. It scores 85.2 on When2Call and 84.8 on RAGTruth. Vision: On the Decision Index Vision public suite, Drex Max scores 74.5. GPT-6 Luna Decisions scores 63.9. Mind2Web reaches 88.6 and InfographicVQA reaches 98.8. Where it trails: Jev 1.14 leads on MMLU-Pro (82.1 vs 72.5), HLE (19.4 vs 19.0) and ARC-Challenge (97.6 vs 97.4). On Hateful Memes, Drex Max trails G

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hether this approach holds up as models grow larger. Does diffusion scale? Here Inception’s answer is yes. Mercury 3 has more than 40x the total parameters of Mercury 1 (mini). Average quality across standard benchmarks rose about 3x between those 2 models. Speed stayed above 1,000 tokens per second. Inception team plots this as a quality vs size curve across 5 Mercury generations. How does Mercury 3 perform on benchmarks? All scores below come from Inception’s table. Every model ran at medium s

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and returned null for the absent discount. Its lowest confidence was 0.776, on the order number. For forecasting, Cloudflare predicted daily TSA checkpoint counts for 21 days. Average error was 3.3%. 17 of 21 actual counts fell inside the 80% interval. 3 of the 4 misses fell on Columbus Day weekend. The main test is a damaged-product claims pipeline. Clairic extracted facts, Clef chose refund or repair, and GLM-5.3 wrote the reply. Cloudflare compared this against GLM-5.3 handling all 3 steps.

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y available: The model selects tokens across all results, not page by page. It gives more tokens to better pages and drops repeated information. Billions of new documents: New coverage includes products, local businesses, flights, events, sports and weather. According to the Exa’s post, Dynamic Highlights uses 95% fewer tokens than full page text while improving answer quality. Highlights carry no extra charge. That matters for agents, where context tokens often cost more than search calls. What

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rs (GenRecs) represent a paradigm shift from scoring every ad to reasoning about ad content and user preferences together in order to predict the best outcome for advertisers. Leveraging the world knowledge built into LLMs makes ad matching more intelligent and precise, compounding gains for advertisers. GenRecs Tokenization is the foundational data representation that makes this possible. Rather than maintaining a dedicated ID for every item, GenRecs Tokenization represents each item with a set

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specialties. A separate panel of physicians independently reviewed each task. The tasks are attempted against synthetically reconstructed patient records in an EHR sandbox. An average multi-step task takes 27 tool calls. Collectively, evaluating the task results in 670 checks in total, covering data retrieval, clinical reasoning, action execution, and documentation. Grading is execution-grounded: Task-specific scripts check whether the right orders exist in the record, with LLM judgments for th

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n is a plugin to vLLM and serves LLMs; vLLM Omni Neuron is a plugin to vLLM Omni and serves diffusion models for image and video generation. Ops and dtypes Broad aten operator coverage for ops seen across text, multimodal and vision language, image and video generation and understanding, state space models, and world models. Models can use BF16, FP32, and FP16, with integer and boolean types, and torch.autocast and mixed precision code operate as written. TorchAO and FP8 are currently supported

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ciation of Attorneys General on behalf of 40 states, those states’ investigations uncovered “serious issues” with Character.AI, including screenshots of Character.AI chatbots encouraging minors to hide their self-harm injuries and kill their parents. A spokesperson for Character.AI says that the filing “is not an accurate portrayal of the safety of our platform.” The filings say that Character.AI was in the process of negotiating with the states over how to address the issues they identified, bu

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med. ( New Scientist $) 10 Amateurs are cracking long-secret codes and ciphers 🕵️ One of the more fun uses for large language models! ( Nature ) Quote of the day —President Trump brags about how much Big Tech bends the knee to him during a rally in Kentucky this weekend, the Verge reports. One More Thing In the last few months alone, Microsoft, Amazon, and OpenAI have all launched medical chatbots. There’s a clear demand for these tools, given how hard it is for many people to access advice thro

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airwaves Every time you make a phone call, stream a video or connect to Wi-Fi, your device is sending and receiving invisible signals through the air. So are your baby monitor, garage door opener, your GPS, the weather-monitoring satellites and the radars keeping planes safely apart. All of these rely on the same invisible resource: the radio spectrum. The demand for wireless data – driven by smartphones, video streaming and billions of connected devices – keeps growing, making available spectr

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a, nearly half of America’s goods deficit of $792 billion), target intellectual property theft and counter Chinese policies that favored domestic companies; and improve the US leverage by reducing its dependency on China. On paper, some of that worked. But look closer, and you see that China didn’t disappear. Supply chains got a remapping instead. Today’s Monday Data looks at what eight years of tariffs have really achieved. Mind the label The immediate impact of tariffs on China was a drop in t

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程师知道哪些模块不能轻易调整，也清楚什么时候需要优先考虑稳定性而非技术上的最优解。 这些经验一部分记录在文档中，更多则分散在长期的代码修改、方案讨论和项目实践里。 类似情况也存在于金融投研、企业运营等领域。通用模型可以分析市场、理解业务流程，但不同团队长期形成的风险偏好、决策习惯和工作方法，往往需要通过持续协作才能逐渐掌握。 目前，AI主要依靠Prompt、RAG、Memory和Skill等机制适应具体场景。它们分别承担指令约束、知识检索、状态保存和能力复用等任务，已经成为企业应用大模型的重要组成部分。不过，这类机制主要在模型外部组织信息，模型自身的参数通常不会因为一次业务交互而发生变化。 当个性化需求从信息获取延伸到长期行为模式，参数化学习提供了另一种技术选择。 通衍灵智采用共享基础模型（Base Model）与个性化参数（Personal Parameters）相结合的架构。 基础模型提供通用知识和推理能力，不同个人、项目和企业则通过轻量化的Personal LoRA进行参数适配，将经过验证的专业偏好和行为特征逐步融入模型。 与传统行业模型主要解决不同行业之间的能力差异相比，这一路

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currently deploys two NVIDIA GPU platforms, with a third (based on NVIDIA’s Vera Rubin NVL72) arriving soon. Each represents a generational leap in performance and efficiency, leveraging our NVL72 rack architecture where 72 GPUs act as a single accelerator within the same network fabric. Catalina: NVIDIA GB200 Our first platform, Catalina, leverages the NVIDIA GB200 NVL72. We deploy the Ariel variant, a two-rack configuration in which 36 GPUs per rack are interconnected via NVLink to form a unif

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员工开发者布道师 Facebook Twitter LinkedIn 邮件 使用 ADK 和 Gemini 3.8 Live 构建一个永不沉默的语音智能体 一份关于 流式工具、非阻塞函数调用和响应调度的指南，适用于 Agent Development Kit（ADK）Gemini Live API 工具包. 在文本聊天中，一次耗时 13 秒的数据库查询或 RAG 流水线会显示一个加载中的旋转图标。而在实时语音对话中，13 秒的阻塞式工具执行会产生 13 秒的死寂，导致用户以为通话已断开而挂断。 你无法总是让后端查询做到亚秒级，因此解决方案必须从架构层面入手：在慢速工具于后台运行的同时，让智能体保持对话。开源 ADK 和 gemini-3.8-live 让你无需自行编写客户端 WebSocket 事件循环来管理异步工具状态，而是可以直接用 Python 原生实现非阻塞语音工具。在我们基于 gemini-3.8-live 的销售报表基准工作流中，从阻塞式工具切换为流式工具后，智能体首次开口的时间从 14.4 秒缩短到了 1.2 秒。 本指南介绍如何使用流式工具、非阻塞的 Function

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