搜索“RAG”

60% of GPT-6 Astra (max, $3.26) for a comparable level of intelligence. This cost efficiency is driven by lower token prices, rather than reduced token use, with Gemini 4 Argon averaging 62k output tokens per task, compared with 27k for GPT-6 Astra (max). Google has not yet confirmed the promotion end date, but on standard pricing, Cost per Task will increase to $3.98 ➤ Stronger agentic performance: Historically a weaker area for Gemini models, Gemini 4 Argon shows improvements across agentic ev

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alidate, and patch critical software vulnerabilities. For trusted defenders and our own internal teams at Google, we’ll be releasing Argon without cyber guardrails so they can leverage its full frontier-level cybersecurity defense capabilities. Wiz is already using Argon for cybersecurity defense through its Scan for Good initiative – a program dedicated to protecting critical public infrastructure for free by finding and remediating high-risk exposures. In an early demonstration of its impact,

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asks: Solar Mini 4 generates 208 tokens/s as of launch date, faster than GPT-6 Luna (max) at 152 tokens/s. However, it uses 88k output tokens per Intelligence Index task, so it averages 7.1 minutes per task. ➤ Agentic coding is a relative weakness: It scores 1% on Terminal-Bench 4.0 and 22% on AutomationBench-AA. On agentic knowledge work, it scores 1072 Elo on GDPval-AA and 872 Elo on AA-Briefcase, close to Inkling (xhigh). ➤ Low knowledge accuracy, but also relatively high non-hallucination ra

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re of 7.78%, placing first on the leaderboard. On TALENT, it achieves the top overall ranking across classification accuracy, classification log-loss, and regression RMSE, with average ranks of 6.67, 3.98, and 4.22. On ScoringBench, a benchmark for predictive distributions, Kumo Tabular-Large and Medium rank first and second on average rank. Limitations Kumo Tabular works on numerical and categorical columns only, while text, images, or timestamps can be turned into features via built-in pre-pro

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net 5.5を使った作例として、紅葉のシミュレーターや手描き風のフローチャートツールなどを紹介した。フローチャートツールでは、図形をドラッグして接続したり、図の上に描き込んだりできるという。 A working flowchart tool that looks like doodles on graph paper, by @kevin t ngo. Drag shapes, connect them, or sketch on top. pic.twitter.com/JOWje43O7t— Claude (@claudeai) September 28, 2026 また、AnthropicのEdwin Arbus氏は、Sonnet 5とSonnet 5.5が「はい／いいえ」で答えられる質問を重ね、20問以内で絵文字を当てようとするデモを紹介している。このデモでは、Sonnet 5.5はSonnet 5よりも所要時間が短く、費用と使用トークン数も少なくなっている。 Sonnet 5.5 plays twenty cool calls: It tries to guess the

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rect Preference Optimization, Flash Attention, and Decision Transformers. A dLLM is a drop-in replacement for a typical autoregressive LLM, supporting all its use cases, including RAG, tool use, and agentic workflows. When prompted with a query, instead of producing the answer one token at a time, the answer is generated in a coarse-to-fine way, as illustrated in the above animation. Improvements are suggested by a neural network – in our case a Transformer model – which is trained on large amou

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I, and the company is the first to apply it to language. Based in Palo Alto, CA, Inception is backed by A-list venture capitalists, including Menlo Ventures, Mayfield, M12 (Microsoft’s venture fund), Snowflake Ventures, Databricks Investment, and Innovation Endeavors. Note: Quality is the average of GPQA Diamond, LCB, IFBench, AIME 2025, and SciCode. E2E latency is measured using Artificial Analysis’s methodology, described here. Mercury 2 for Search: Fast enough to run a hundred times per query

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Real-time voice and interaction: Voice interfaces have the tightest latency budget in AI. Mercury 2 makes reasoning-level quality viable within natural speech cadences; Search and RAG pipelines: Multi-hop retrieval, reranking, and summarization latencies stack fast. Mercury 2 lets you add reasoning to the search loop without blowing your latency budget Deploy on Azure AI Foundry Mercury 2 is available in the US and Canada regions through Azure AI Foundry. Deployment is straightforward—provision

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bat this, we gather a high quality human preference dataset by recording the explicit feedback (accepting vs rejecting an edit) that our model receives. Using this dataset, we leverage an unpaired reinforcement learning method called KTO to align our model with human preferences. We find that this step is important for increasing the utility of our model. The results are clear. Our new model’s edits are accepted 48% more often, emphasizing the value of this improvement. It is also 27% more selec

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Artificial Analysis1. Eleven v4 Turbo brings that same technology to low-latency use cases like agents. With a median inference latency of 100ms, it can respond faster than the average pause between two people talking. Both models can generate speech that feels emotive, rather than mechanical. Underlying audio fidelity is also higher across the board, with cleaner, more natural-sounding output. Earlier generations of text-to-speech models can read text aloud, but Eleven v4 provides an emotional

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timodal-native and Multilingual Encoder 120 September 3, 2026 Holo3.1: Fast & Local Computer Use Agents 51 June 2, 2026 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment +54 Models mentioned in this article 3 Hcompany/Holo4-27B Hcompany/Holo4-35B-A3B Hcompany/Holotron4-30B-A3B Datasets mentioned in this article 1 Hcompany/trajectories Collections mentioned in

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fell from 49.3K to 29.9K per task. Reasoning tokens dropped 71.3% and total tokens dropped 39%. The task score was essentially unchanged: 0.753 for Ember-1 versus 0.751 for K3. Average steps fell from 23.8 to 21.4. One customer now runs Ember-1 in production. Ember-1 costs the same per token as Kimi K3 on Fireworks: $3.00 input, $0.30 cached input, and $15.00 output per 1M tokens. The savings come entirely from generating fewer tokens. At that output rate, the A/B figures work out to about $0.74

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subset. That subset likely excludes the hardest programs, such as FFmpeg and the PHP compiler. He also flagged a metric mismatch ( @OfirPress , @OfirPress ): Anthropic reports average test pass rate. ProgramBench reports full task completion. Partial solves often pass 60–70% of tests, which inflates the pass-rate metric. Comparison with Mythos 5.1. Opus 5.5 outscores Mythos 5.1 on Anthropic’s ECI and beats it on every tested cyber eval ( @scaling01 , @scaling01 ). Odd misalignment finding. @teo

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TTS generates a high-energy DJ voice from Melbourne. Hear how Gemini 3.8 Flash TTS generates a super-tinny, monotone robot voice. Hear how Gemini 3.8 Flash TTS brings a Japanese dragon to life. Once you've selected your voices, both TTS models give you precise control over how each line is delivered. Hear how Gemini 3.8 Flash TTS enables natural, highly expressive conversations for interactive voice agents. Watch and hear how Gemini 3.8 Flash TTS uses granular script control to build a deeply e

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nteraction. The model explores possible actions, incorporates new observations, and refines its reasoning as it progresses. Feedback connects decisions to their consequences, encouraging useful reflection throughout the task. We carried these insights into a training collection spanning mathematics, coding, instruction following, conversation, search, tool use, and software engineering, covering both standalone problems and extended interactions to enforce adaptation to observations and outcomes

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ude Opus 5, and new pricing passes those gains straight to customers. Lower per-token prices and much cheaper cache reads stack on top of the efficiency gains. The result is an average lower cost per task than Claude Opus 5, so teams can run more ambitious agentic work at scale. Claude Opus 5.5 is trained to communicate more clearly. As it works, it surfaces what it did, what it found, and what it needs, making long-running tasks easier to follow. Adaptive thinking is always on, and Opus 5.5 dec

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sion/quantization is still moving fast : @ZhihuFrontier summarized Tencent Hunyuan’s engineering behind packing Hy4 Preview (770B) into 214 GiB via mixed-precision quantization averaging 2.38 bits/weight , including custom CUDA kernels in patched llama.cpp. On the edge/local side, @vikhyatk released Parakeet Redux , compressing NVIDIA’s speech model from 1.2GB to 178MB , running at 113x realtime on CPU , while beating the base model on 25-language FLEURS and staying within 0.3 WER on English. Ag

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(2026)5 when using maximum reasoning efforts. But a single reasoning call adds around 35.77 seconds per second of robot motion, and each success costs $13.47 and 16 minutes on average. In contrast, F3A on its own takes 21.08 milliseconds per second of robot motion and each success costs $0.09 and less than two minutes on average when running on a H200 GPU for $3 per hour. For the most part of those two minutes, F3A is sitting idle, waiting while the robot moves. In this setting, F3A could even

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transparency: Natively generates and edits RGBA layers, enabling seamless compositing and text editing within transparent images. - Versatile, high-fidelity editing: Supports up to 10 reference images and precise local control while preserving strict fidelity for portraits and products. - Broad coverage & stunning aesthetics: Excels at panoramas, infographics, and virtual try-ons, delivering realistic textures and elegant typography. Start to create your next masterpiece with Qwen-Image-2.1! 🖼️

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sequent design and composition work. Versatile Editing Qwen-Image-2.1 improves editing in four areas: multiple reference images, local editing, fidelity preservation, and task coverage. Multiple References: Up to 10 Input Images Qwen-Image-2.1 supports up to 10 reference images , combining multiple subjects and assets into a coherent composition. In the group portrait example below, six individual portraits on the left are brought together in one photograph. A group photograph generated from six

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