搜索“RAG”

Healthcare is another real-world setting: fragmented symptoms, dense reports, ongoing questions. Feedback from AQ and https://t.co/M7OKhVawk0 (Good Doctor Online) scenarios informed training. Ling-3.1-flash scores 65.35 on HealthBench Professional—an evaluation result, not clinical certification.

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mestamps for the episode at the bottom. Brought to You by • turbopuffer – The turbopuffer engineering team is doing something really cool: they are completely redesigning their storage architecture from first principles to make search faster, cheaper, and more reliable at scale. And they’re documenting all of it! Follow along their rewrite at turbopuffer.com/v3 • Linear – most of us work with agents in a “single-player” setting. Linear’s take is agent work should be teamwork: your teammates can

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Related Resources Developer Tools & Techniques Learn how to use execution traces to understand agent behavior and determine whether harness changes improve task outcomes. AI-Generated Summary NVIDIA NeMo Relay provides a common observability layer that captures ordered lifecycle events and structured trajectories for agent runs.

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n adjuster might need every open auto claim over $10,000 from last month. Both tasks require finding and combining evidence quickly and accurately. Retrieval Augmented Generation (RAG) uses retrieved documents to ground model responses. Amazon Bedrock Knowledge Bases is the fully managed RAG capability for documents. Amazon Bedrock handles parsing, chunking, embeddings, and vector storage, so you can build a conversational interface that returns cited answers from claim files. This technical how

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t the studio’s own back catalog. If the screen objects, the agent calls back to the composition agent for a replacement and re-screens. Delivered as a zip file on Amazon Simple Storage Service (Amazon S3) The workflow: a producer starts a track. The composition agent writes a brief and renders real audio on the instance’s GPU. The delivery agent opens that file, measures it, applies a chain it derived from those measurements, and measures again to prove the result landed on target. The complianc

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ou, of course you can see the files. Exporting virtual machine data doesn’t give people any privileged access to Meta infrastructure or to other people’s data.” Unlike with the average laptop, however, the data potentially reveals some interesting things about how Meta’s new AI platform functions. We seem to be entering into an AI agent renaissance. Meta’s new consumer-facing AI agent, Muse, topped the App Store charts soon after its release and has 600,000 daily active users in the US, by an Ap

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s relaying real-time camera data about stations and their operations, transit vehicle locations, riders, traffic, and road conditions. Unfortunately, the information coming in is fragmented, rather than integrated into a centralized system with overall awareness of the network’s conditions. This system creates an intense work environment for the transit staff making operations and communications decisions that can affect thousands of passengers relying on transit to get them where they need to g

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be surprisingly composed. It doesn’t overcorrect and give away its secrets,” Farina says. The researchers also adapted Ataraxos for other imperfect information games, including Barrage Stratego (a faster-paced variant with fewer pieces), Hanabi (a cooperative card game with many players), and Dou dizhu (a game in which two players cooperate against a third). The system achieved superhuman performance in each instance, demonstrating the generality of this method. In the future, the researchers wa

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currently produces a per-file config for each upstream test file we run, covering thousands of individually named test cases, and a merge run exercises them on real hardware. Coverage grows automatically enabling a new op in the backend to admit every test that was waiting on it, with no manual list maintenance. Auditability matters: Every bucketing choice is written back into the config as a comment explaining why. When someone asks “why is this test skipped?”, the answer is in the file, not i

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Wang, Martin Casado, Marc Andreessen, and Ben Horowitz; Databricks CEO on AI Pacing, Cyber Risk, and the Enterprise Ali Ghodsi, Martin Casado, and Sarah Wang; Investing in Gimlet Raghu Raghuram, Sarah Wang, Shangda Xu, and Stephenie Zhang; You are not a model. Don’t price per token. Tugce Erten and Sarah Wang; Inside Cursor: The Anatomy of a Generational Startup Matt Bornstein, Sarah Wang, and Martin Casado Alex Immerman X; Linkedin is a partner on the Growth team at Andreessen Horowitz, where

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alar RMs. And the gains survive RLHF. When used as the training-time reward, DRM improves downstream policy performance over scalar reward baselines, showing that the benefit is not limited to offline RM benchmarks. The takeaway: Reward modeling may lose something important when it compresses every human judgment into one number. “Everyone thinks this is average” and “people strongly disagree about this” should not look identical to a Reward Model. DRM makes that difference visible — and usable.

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earchers work with organizations to orchestrate and refine our models and agents for their specific conditions, rather than tweaking something indiscriminately designed for the average company By owning the full stack, we are able to quickly raise quality and increase the value delivered to every customer. Model improvements reach our customers immediately, and feedback from real deployments allows us to continually refine both the models and products. “As ElevenLabs has grown its conversational

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nswer directly, but because it was never judged, it is not considered relevant. The resulting nDCG@5 of 0.760 reflects just one of the five results. The trade-off is a growing coverage gap: a newer model may retrieve a genuinely relevant document that was never included in the original judgment pool, yet conventional nDCG will not recognize it. As retrieval systems become more capable and explore parts of a corpus that earlier systems did not, benchmarks can increasingly reflect just what previo

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ng system, and credit growth became increasingly disconnected from the real economy. A lot of it went into the property sector and local government infrastructure. After the deleveraging campaign started in 2016 and intensified by 2018, you had a sharp decline in credit growth. This coincides with Xi Jinping’s rise to power and how that changes financial risk, which we should discuss separately. You now have credit that cannot expand at the same rates because the financial system is already over

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Key takeaways State-of-the-art enterprise retrieval: Embed 5 Pro achieves the highest average score of any model we tested, particularly across financial datasets, parsed PDFs, and visually rich documents; A new Fast tier: Embed 5 Fast brings strong retrieval quality to latency, and cost-sensitive workloads, at $0.08 per million tokens; One index, two models: Pro and Fast share an embedding space, so teams can index with Pro and query with either model without re-indexing; Built for complex ente

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models. Always-on agents are proving to be a winning consumer form factor for AI, and plugging them directly into the top models is a differentiator only OAI and Anthropic can leverage. The Rundown: Blotato offers unified social media APIs for your AI agents. Easily schedule posts, create Manychat-style DM automations, and automate your social media marketing with Claude, ChatGPT, Grok Bot, Muse, or any AI agent. With Blotato’s unified social API, your AI agents can: Create content with the late

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the historian, which surprised no one who has met a historian; Make the one-off durable. "Create a dashboard showing verbosity of the agents over time." It built one measuring average visible output tokens per agent turn, and added panel descriptions on request; Add the alert. Rather than specifying it, Marcelo asked for a suggestion: "I've had a lot of 500s lately, can you suggest an alert for it?" The agent proposed a query filtered by service and route on 5xx status with a count threshold ab

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ts physical content, not visual appearance. The final training set holds 183K videos: 71K filtered from WISA-80K plus 112K retrieved clips. Retrieval alone added 3.01 points on average across 3 benchmarks. On VideoPhy-2, chemical and thermal processes each gained 8.00 points. Fine-tuning uses LoRA on attention projections, with no architecture or objective change. Physis-Lang on Cosmos3-Nano versus Google's Veo 3.1: Gains hold across backbones: +7.05 on Wan2.1-14B, +3.24 on Cosmos3-Edge-4B, +6.2

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natural-language descriptions first seems to act as a form of structured reasoning, distilling the essential information before it reaches the decision-making model. Equally encouraging, the improvements were consistent across several underlying vision-language models, including GPT-4o, GPT-4.1, GPT-4o-mini, and GPT-5. This suggests that the benefits stem from the communication strategy itself rather than from any particular model. UNCAP is an early step toward autonomous vehicles that cooperat

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astructure and Systems Research DeepSeek DSec : DeepSeek published its sandbox infra for agent RL , which has handled all sandbox workloads from V3.2 through V4.1. Backends and storage : four backends (FnCall, Container, MicroVM, Full VM) with composable EROFS/OverlayFS layers. Image loading : on-demand loading from 3FS matters because only 4–13% of image data is ever read; it gave a 1.71x speedup on 8,192-container creation. Density : overcommit exceeds 50x. Scale : each shard serves 3M sandbox

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