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t Plus-tier models on hard STEM benchmarks. In instruction following and multilingual tasks , it delivers consistent quality across IFBench, WMT24++, and PolyMATH, with strong coverage across diverse languages. Multimodal Benchmarks GPT-5.4 (xhigh) Opus-4.6 Max Gemini-3.1 Pro Qwen3.6-Plus Qwen3.7-Plus Multimodal Reasoning MMMU-Pro 81.2 73.9 81.8 78.8 79.0 MathVision 91.0 65.5 87.4 88.0 90.3 BabyVision 53.1 12.6 55.9 37.4 70.4 / 64.7 CharXiv(RQ) 84.5 66.0 84.4 81.5 85.9 / 84.4 HiPhO 65.0 40.8 85.

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on problem by adding a pre-filtering stage. Compared with approaches like DSA and MoBA, MSA can partition the KV into blocks more precisely, achieving higher effective context coverage. At the same time, we also optimized directly at the operator level, adopting a "KV outer gather Q" approach that uses KV blocks as the outer loop to aggregate the queries that hit them. Each block is read only once and memory access is contiguous; under M3's head configuration, the arithmetic intensity is signifi

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p training, this new version performs significantly better: Tokenizer expansion. LFM2-8B-A1B was originally trained with a 65K BPE tokenizer optimized for our initial language coverage. To better support non-Latin scripts in LFM2.5, we doubled the vocabulary to 128K by extending the existing tokenizer in place rather than retraining the model from scratch.. We continued BPE merge training from the original merges on a multilingual corpus, which keeps most existing token IDs as identity mappings

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diverse training environments. As shown in the figure below, this environment scaling produces a clear and consistent improvement trajectory, with Qwen3.7-Max achieving a top-3 average ranking that approaches Claude-4.6-Opus-Max. Crucially, all benchmarks in our evaluation feature entirely unseen, out-of-domain environments that were never present in training. We also observe a striking predictability in the scaling behavior: performance gains across any subset of benchmarks are highly consisten

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asoning improved from 46.9% to 52.7%, reflecting broad gains across document understanding tasks. Command A+ significantly expands multilingual capability, broadening language coverage from 23 to 48 languages and recording gains in machine translation and multilingual reasoning. Image 6: comparison of multilingual performance for Command A+ and Command A Reasoning. MT-AIME 2025 is an internal translation of AIME-2025 — an English-language mathematics benchmark — evaluated for Arabic, Japanese, a

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ands visual context to produce more accurate translations. Compared with its predecessor Qwen3-LiveTranslate, Qwen3.5-LiveTranslate-Flash brings major upgrades across language coverage, latency, voice cloning, and terminology handling, making it well-suited for international meetings, livestream localization, online classrooms, and business negotiations. Key Highlights Massively expanded language coverage: understands 18 → 60 languages, speaks 10 → 29 languages. The language support of input aud

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ing frontier performance. Powered by Antigravity, 3.5 Flash executes multi-step workflows to automatically rename and categorize unstructured assets based on dynamic criteria. Leveraging Antigravity, 3.5 Flash uses two agents to synthesize the AlphaZero paper and code a fully playable game in six hours. 3.5 Flash uses the Antigravity harness to transform a messy legacy codebase to Next.js. 3.5 Flash uses subagents to create new city landscapes in Antigravity. 3.5 Flash uses two agents: a builder

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is sampled from the residual distribution of p(x)-q(x). Our mixed-logit sampler first mixes the logits from the diffusion speculator and the autoregressive mode, then uses the averaged distribution for verification. This improves acceptance since the verification logits are closer to the diffusion logits, but has some impact on quality. Even in these early stages, we observe the speedup we attain from diffusion is higher than alternative methods such as multi-token prediction (MTP) and various

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n Agent feel like a true teammate.Sarah Sachs AI Lead In our evals, we saw a double-digit jump in accuracy of tool calls and planning in our core orchestrator agents. As users leverage Hebbia to plan and execute on use cases like retrieval, slide creation, or document generation, Claude Opus 4.7 shows the potential to improve agent decision-making in these workflows.Adithya Ramanathan Head of Applied Research On Rakuten-SWE-Bench, Claude Opus 4.7 resolves 3x more production tasks than Opus 4.6,

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more efficient1. And priced nearly 41% lower — $5 per 1M text input tokens, $19.50 per 1M image output tokens. That’s not just faster than our own flagship. It’s 40% faster on average than other leading text-to-image models2. MAI-Image-2-Efficient leads on render speed Full render time (LTR) measured at P50 median, in seconds, across standardized prompts. Lower is better. Two models, two jobs MAI-Image-2-Efficient is your production workhorse. Use it when you need volume, speed, and tight cost

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ampling: Rather than discarding groups of samples that are all incorrect, a penalty (negative reward) is assigned to the flawed reasoning paths that cause the errors, thereby encouraging the model to avoid logical errors on its own; Group & Global Advantages: Training stability was enhanced by calculating rewards in two stages: evaluating individual response groups and considering the data distribution at the global batch level; Exploratory Token Preservation: By removing the clipping constraint

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ion (measured on RefCOCO-M) and text function calling (measured by BFCLv4). All vision benchmark scores are obtained using VLMEvalKit. Multilingual MMMB scores are based on the average of benchmarks translated by GPT-4.1-mini from English to Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish. Performance: Made for the Edge In real-world deployments, VLMs process live camera and image input, and must reason and act within a tight latency budget. LFM2.5-VL-450M (Q4 0) runs

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ol calls far more than the preview and answers the question faster. One contributing factor to this improvement was the introduction of a length penalty into training, which discourages unnecessarily long trajectories. This directly reduces overthinking and looping, while implicitly encouraging more efficient behaviors like parallel tool use. During training, we observed the model response length growing much more slowly than before while maintaining its intelligence and coding ability. The belo

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t, making MAI-Transcribe-1 not just the most accurate, but also lightning fast. It’s now available in Foundry at the best price-performance of any large cloud provider. Overall Average WER by Model Lower is better. MAI-Voice-1 is our top-tier voice generation model. Built to generate natural, realistic speech, rich with nuance, emotional range and expression that preserves speaker identity even across long-form content. Today we’re adding the ability to safely and securely create your own custom

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Letter Flying Next User /skills brainstorming Find the complete content of Jobs’ ’think different,’ using a vertical paper background, text in typewriter font, arranged in full paragraphs, with the entire text located at the lower third of the page. The typewriter effect appears letter by letter. After the complete content appears, pause for 3 seconds, then the letter ‘o’ in the text moves upward and enlarges to the middle of the page, with the ‘o’ forming a line with its original position, pul

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footwork—rapid stamping, hopping, and spinning—sending puffs of dust into the air. The drum ensemble behind them pounds out layered rhythms while the audience claps and yells encouragement. Around 00:09 a male spectator near the camera calls out, “Why?” in playful disbelief. At roughly 00:12 the masked dancer drops into a low squat, nearly touching the ground, then springs back up, prompting louder applause. 00:15.000 – 00:18.000 The host abruptly pivots the phone back to himself for another clo

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ed languages. Model performance Accuracy Cohere Transcribe is the latest standard for English speech recognition accuracy. It leads the HuggingFace Open ASR Leaderboard with an average word error rate of just 5.42%, outperforming all open- and closed-source dedicated ASR alternatives, including Whisper Large v3, ElevenLabs Scribe v2, and Qwen3-ASR-1.7B. This captures the model’s versatile capability across real-world speech tasks, such as robustness to multiple-speaker environments, boardroom-st

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self-attention layers in the future. $^ $at least for language modeling Hybrid models that combine the general memory-like nature of linear layers with the exact database-like storage of self-attention's KV cache have been shown empirically to outperform pure models while enabling significant memory and compute savings , and we do find here that the combination of linear layers with self-attention enables better retrieval compared to a vanilla Transformer. However, we highlight that the exact w

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nsuring efficient, high-throughput serving across deployment scenarios Figure: Score vs. Output Length across three benchmarks. Top: accuracy scores (higher is better). Bottom: average output length in thousands of characters (shorter is better). Mistral Small 4 with reasoning achieves competitive scores, matching or surpassing GPT-OSS 120B on all three benchmarks, while generating significantly shorter outputs. On AA LCR, Mistral Small 4 scores 0.72 with just 1.6K characters, whereas Qwen model

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t designed for Lean 4. Lean4 is a proof assistant capable of expressing complex mathematical objects such as perfectoid spaces and software specifications like properties of Rust fragments. Unlike existing proving systems that act as wrappers around large generalist models or focus on single math problems, Leanstral is designed to be highly efficient (with 6B active parameters) and trained for operating in realistic formal repositories. Open and accessible: We release Leanstral weights under an

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