搜索“Agent”

s, the RapidsUDF interface with the Java API for cuDF, or custom CUDA kernels via JNI to eliminate fallback and achieve over 20x speedups; The cuDF skills for Apache Spark provide agent-driven workflows that guide testing, conversion, benchmarking, and optimization with verification-driven feedback loops; A major consumer technology company converted more than 30 production UDFs using this workflow, with one rewrite reducing operator time from 274.55 to 12.78 hours across a 600-billion-row workl

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tierCode in June 2026 and updated it to version 1.1 in July. Cognition built the dataset with open source maintainers who spent more than 40 hours per task. Each task requires the agent to make a change to a real open source repository, then checks both unit tests and qualitative tests for what maintainers would require to merge the change. We run FrontierCode 1.1 Main, the primary 100-task set of the benchmark's 150 tasks, using our Stirrup open source reference harness for apples-to-apples com

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We have measured intelligence. Now it's time to see it. We are launching AA-3D, our new benchmark for how well AI models build interactive 3D scenes. Each model works as an agent in an offline sandbox and turns a written brief into a three.js web page. A human panel of judges compares pairs of scenes built from the same brief without knowing which model made which. AA-3D details: ➤ 55 written briefs across seven domains, including landmarks, machines, living bodies, games, and space. Each brief

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gh these optimizations, we convert KVCache capacity expansion into longer effective hit lengths, making cross-session long-prefix reuse possible — particularly beneficial for long agent sessions, multi-user shared system prompts, and repeated tool calls to the same codebase. 2.2 GCache: High-Performance Distributed Cache Infrastructure GCache is a high-performance general-purpose cache system developed by the Xiaomi storage team, forming a critical part of unified training-inference storage arch

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文/VR陀螺 10月13日，阿里巴巴旗下千问正式发布三款智能穿戴新品，包括千问AI眼镜N1、N1 Pro以及千问AI耳夹式耳机。新品重点围绕影像拍摄、眼动交互、健康监测、实时翻译及AI主动服务等功能展开，进一步拓展Personal Agent在日常生活中的应用场景。 目前，三款新品已在淘宝、天猫、京东及抖音等电商平台现货开售。叠加国家补贴及限时优惠后，AI眼镜到手价2218元起，AI耳机到手价1099元起。 AI眼镜方面，N1系列包含N1和N1 Pro两款产品，均支持影像拍摄、翻译及语音AI交互。其中，N1 Pro进一步加入眼动追踪、虹膜识别及体征监测等功能，强化设备对用户状态及现实环境的感知能力。 VR陀螺也对千问N1 Pro进行了深度体验，重点围绕健康监测、眼动交互及AI主动服务等核心能力展开实测。当AI眼镜开始尝试感知用户的身体状态，这些功能究竟能带来怎样的实际体验？更多细节，详见VR陀螺评测视频： 据了解，N1 Pro是行业首款具备实时体征监测功能的AI眼镜，支持心率、血氧、皮肤温度等多项体征数据的实时监测，并通过AI生成健康洞察报告，将相关数据转化为更易理解的信息。例如，在骑

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ect data on your inferences and codebase.) As a test that our plan could work in the first place, we pointed Cursor to OpenRouter and realized we could use its models for the Ask, Agent, and Cmd+K interactions in Cursor. We were also still able to use the normal Cursor Tab completions, which rely on a proprietary model Cursor serves for inline completions (it is very good, so we’re glad to be able to keep it). The solution was to set up a reverse proxy to expose a public endpoint that would forw

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ences. Naturally, we wanted to know which prompts and models were performing better. This post describes our methodology for automatically evaluating the tangible impact of coding agents in our day-to-day software engineering process. Our work does not claim that a particular model is superior to another in a general way. We believe that this is the obvious role for benchmarking. Instead, we focus on local evaluation for individual engineering use cases and workflows. TensorZero was designed fro

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ning (RFT) workflows on three tasks:Data Extraction: RFT improved performance with limited data, though SFT on a larger dataset achieved better results at a still much lower cost; Agentic Coding: RFT improved performance where SFT failed; Customer Service: SFT outperformed RFT. RFT actually degraded performance; RFT costs 100-700x more than SFT on equivalent datasets. SFT on much larger datasets often still costs only a small fraction of a tiny RFT job; We demonstrate how to implement systematic

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price of $16/M. This is 60% lower than 3-ranked GPT-6 Astra at $40/M. - 2-ranked Claude Sonnet 5.5 (xHigh) costs $8/M, 50% less than Opus for a 9-point score gap. It also ranks one place above GPT-6 Astra ( 3) while costing 80% less. Stay tuned to see where Claude Haiku 5.5 lands in the Image-to-WebDev Arena! The Image-to-WebDev Arena ranks AI models on their ability to generate websites from images and screenshots, alongside agentic coding workflows involving multi-step reasoning and tool use.

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ect data on your inferences and codebase.) As a test that our plan could work in the first place, we pointed Cursor to OpenRouter and realized we could use its models for the Ask, Agent, and Cmd+K interactions in Cursor. We were also still able to use the normal Cursor Tab completions, which rely on a proprietary model Cursor serves for inline completions (it is very good, so we’re glad to be able to keep it). The solution was to set up a reverse proxy to expose a public endpoint that would forw

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ning (RFT) workflows on three tasks:Data Extraction: RFT improved performance with limited data, though SFT on a larger dataset achieved better results at a still much lower cost; Agentic Coding: RFT improved performance where SFT failed; Customer Service: SFT outperformed RFT. RFT actually degraded performance; RFT costs 100-700x more than SFT on equivalent datasets. SFT on much larger datasets often still costs only a small fraction of a tiny RFT job; We demonstrate how to implement systematic

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, and how much each change bought. On the training side, Miles, which uses SGLang for rollout and Megatron for training, runs RL end to end on Vera Rubin out of the box, including agentic RL with sandboxes on the Vera CPU. NVIDIA Vera Rubin is the GPU generation after NVIDIA Blackwell Ultra (GB300 NVL72). For kernel work, the most impactful changes are more shared memory per CTA (327 KiB, up from the 227 KiB on NVIDIA Hopper and Blackwell), more SMs (212 on our nodes), and NVIDIA NVLink 6. Some

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AI models perform on enterprise cyber defense tasks. The Index launched with only publicly accessible models, giving users the best indication of publicly available capability on agentic cyber defense. Now, we’re expanding the leaderboard to include models with fewer cyber guardrails, providing a broader view of frontier model cyber capability. The first of these is GPT-6 Sol (Daybreak Blue, max) which is only accessible through @OpenAI's Daybreak program GPT-6 Sol (Daybreak Blue, max) now lead

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rds of 6k per day! This makes these models completely unusable for the average consumer. The goal So the question I asked is how far can these models be pushed? What if you had an agent search the internet for every optimization, every speed up, every cost savings, and definitely every dirty hack that sacrificed quality for speed. And then what if you put it all together into a single custom comfy node that runs on a single consumer GPU? My requirements were the following: Hardware: one RTX 5090

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Introducing the Arena Alignment Index, our new benchmark measuring safety and alignment of AI agents in real-world use. Built from 90K+ real-world agent sessions across 27 models, the index measures three critical signals: - Unauthorized Action (UA): Taking actions beyond the user's instructions or permissions - False Attribution (FA): Attributing statements or actions that are contradicted by user-provided evidence - Deceptive Completion (DC): Claiming a task was completed when it was not. Key

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s. Laya’s calibration module turns labeled examples into records of raw logits and targets, and fits a temperature to them. We build 300 records from the validation split and call agent.fit temperatures, which fits a choice temperature of 1.258 and installs it, then prints the full temperature table next to what shipped. The fit replaced the entire map: every per-option-count entry is gone, because a bucket needs 2,000 records to keep its own temperature, and the score and yes/no temperatures we

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In short : Jeanne DeWitt Grosser, COO of Vercel, walks through the construction of the company's inbound sales agent: one GTM engineer at 20% time, a prompt written by the top SDR, six weeks of human-in-the-loop QA, & the eventual migration from a 1,000-line prompt to 14 deterministic rules with the model reserved for judgment. Jeanne DeWitt Grosser, COO of Vercel, joined me on Office Hours to walk through how she & her team actually built the agent that now runs the top of Vercel’s sales funnel

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s own processes and its own branch. The docs session and the logger session each started the API server to test against it, and neither affected the other. On a single laptop, two agents working in one checkout would edit the same files, and would collide on a port unless each picked its own. Because of that isolation, the logger session had no access to the cache fix the first session was making. Split parallel tasks along file boundaries, merge the branches in a sensible order, and expect a se

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plans, and models will be added as soon as they’re released. The rest of the article will give an overview of everyone’s current limits. All token and dollar amounts below assume agentic usage unless otherwise specified. Tokens are generally priced per MTok (million tokens) across the following types: Input: Fresh tokens added to the LLM’s context window that aren’t cached; Cache write: Input tokens that are cached for multi-turn conversations. Generally slightly more expensive than regular inp

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nvironments. No changes to the harnesses, no changes to the training code. Any open model, any task set, fully open source my friends! Same model, same weights: 62% under Mini-SWE-Agent, 33% under Claude Code. But training inside a real harness normally means reimplementing it as an environment, so most models get trained in a scaffold nobody actually ships. The fix is a proxy, not a rewrite. The harness thinks it's talking to a model API. It's actually talking to a capture proxy that speaks the

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