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ads. During teacher specialization, LFM2.5-24B-A2B exhibited well-known reward-hacking behaviors: searching for the final answer in a repository's git history or on platforms like GitHub, PyPI, and Hugging Face. More surprisingly, it even figured out it was in a sandbox, as shown in the examples below. This behavior looks emergent but might be explained by training data containing similar patterns. We tightened our sandboxes in response, and all reported results below come from sealed evaluation

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A3B scores 90.4 on OCRBench, 5.2 points ahead of Qwen3.6 35B-A3B. Easier customization: We introduce Self-Replay Fine-Tuning, a simple way to customize new target capabilities at low cost while maintaining general performance. Broad hardware support: Day-0 adaptations for Apple, Intel, AMD, Arm and Rockchip, with many more platforms supported through FlagOS! Download, deploy, and build something we haven't imagined yet. 👀👇 🔗 GitHub: https://t.co/msjm2QlrvX 🤗 Hugging Face: https://t.co/W95QKGhymw

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首个支持端侧无损流畅运行的开源 MoE 模型，在 iPhone 17 Pro Max 上以仅 3GB 内存达到 13+ token/s 的解码速度。 IT之家了解到，MiniCPM-V 4.7 同时支持 iOS、安卓和鸿蒙，并适配 Intel、AMD、Arm、瑞芯微、后摩等芯片平台，覆盖更多移动设备与本地计算场景。同步开源 Self-Replay Fine-Tuning (SRFT) 微调方案，帮助开发者低成本扩展模型能力和减少遗忘，方便二次开发。 此外，该模型 35B-A3B 版本在 Instruct 模式下，以更短的推理时间取得比 Qwen3.6 35B-A3B 更高的综合得分；1B 版本相比 MiniCPM-V 4.6 持续提升基础能力，并在综合得分和推理效率上均显著领先 Qwen3.5 0.8B。 参考： MiniCPM-V/README zh.md at main · OpenBMB/MiniCPM-V · GitHub 广告声明：文内含有的对外跳转链接（包括不限于超链接、二维码、口令等形式），用于传递更多信息，节省甄选时间，结果仅供参考，IT之家包含外链的文章均包含本声明。

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mes as fast as GPT-6 Sol at a fraction of the cost for Xbox, and 46 times more consistent in Discovery. But the model may have a bigger job in mind. On Wednesday, the company said GitHub Copilot will soon decide when to run a task on-device and when to send it to cloud-scale models , though it hasn’t disclosed what Copilot sends to the cloud. Model routing is among the use cases Microsoft lists for Decision-1, but the company hasn’t said whether the model will make those calls for Copilot. With

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rs. The Q8 0 GGUF also matched on an Apple M5 Pro on both Metal and CPU. A Drex agent skill plugs the model into Claude Code, Codex, Cursor, OpenCode, Hermes Agent, Gemini CLI and GitHub Copilot. Nace’s launch post also offers a $25 sign-up bonus for cloud users. How does Drex 1.5 compare with other decision models? Decision Index 0.3.1 rows are from the Drex 1.5 model card, which cites the public leaderboard for Jev and Nimble. Nimble’s 0.2.1 score is from its own model card. What are the limit

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he 500-task public subset with a 100-turn limit, using Gemini 3.1 Pro as the judge; AutomationBench. All models are evaluated on the 600-task public subset, following the official GitHub setup in all other respects; BrowseComp. We adopt the context-compaction strategy used in the Claude model cards, triggered at 300K tokens. When evaluated with a 1M-token context window and no context management, Kimi K3 achieves a score of 90.4. The results of Claude Fable 5, Claude Opus 4.8, GPT 5.6 Sol, and G

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d as incorrect; reported scores likely underestimate true performance; WorldVQA, a benchmark designed to evaluate atomic vision-centric world knowledge. Access WorldVQA at https://github.com/MoonshotAI/WorldVQA; OmniDocBench Score is computed as (1 − normalized Levenshtein distance) × 100, where a higher score denotes superior accuracy 5. Coding Tasks Terminal-Bench 2.0 scores were obtained with the default agent framework (Terminus-2) and the provided JSON parser. In our implementation, we eval

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your own We recommend running Kimi K2 on one of the following inference engines: vLLM, SGLang, KTransformers, or TensorRT-LLM. For detailed deployment instructions, please see our GitHub repository. What's next While Kimi K2 serves as a strong foundation for open agentic intelligence, a general agent uses more advanced capabilities such as thinking and visual understanding. We plan to add these to Kimi K2 in the future. Limitations In our internal tests, we've identified some limitations in curr

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hts; 📈 Large-scale RL in post-training; 🏆 Significant performance boost with minimal labeled data; 🔢 Math, code, and reasoning tasks on par with OpenAI-o1; 📄 More details: https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek R1.pdf 🌐 API Access & Pricing; ⚙️ Use DeepSeek-R1 by setting model=deepseek-reasoner; 💰 $0.14 / million input tokens (cache hit); 💰 $0.55 / million input tokens (cache miss); 💰 $2.19 / million output tokens; 📖 API guide: https://api-docs.deepseek.com/guides/thinking

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e through Claude Platform on AWS in North America Give Claude Haiku 5.5 a try on the Amazon Bedrock console, in Claude Platform on AWS, or explore the Getting Started notebooks on GitHub. About the authors Aamna Najmi Aamna is a Senior Specialist Solutions Architect for Generative AI focusing on Anthropic models and operationalizing and governing generative AI systems at scale on Amazon Bedrock. She helps ISVs solve their challenges, embrace innovation, and create new business opportunities with

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t timestamps where matching moments occur Download the latest version of Google AI Edge Gallery app from Google Play Store or Apple App Store to try these new demos. Check-out the Github repo to learn how it’s implemented and start building your own experiences using EmbeddingGemma 2. Productivity boost and context retrieval in Google AI Edge Foresight on Mac We are excited to announce the launch of Google AI Edge Foresight for Mac, an experimental app that brings a context-aware meeting compani

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17x faster than LimiX-2 on a single RTX 6000 Pro. All 3 sizes sit on the accuracy and inference-time Pareto front. Sources: NVIDIA blog , SDM model docs , Prior Labs docs , LimiX GitHub , TabICLv2 paper . Checked September 30, 2026. The license row is the real differentiator. TabPFN-3, LimiX-2, and TabFM weights carry non-commercial terms. Kumo Tabular and TabICLv2 are the permissive options, and Kumo Tabular leads the benchmarks NVIDIA reports. Install the library and pass a DataFrame through

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res therefore reflect reranking quality rather than first-stage retrieval performance, which we thoroughly evaluate elsewhere against nDCG and Recall. 2 The annotations and code needed to evaluate ViDoRe V3 with RCP-nDCG are available on GitHub. 3 Both sides must use the same output dimension. Compatibility also holds with Matryoshka truncation and int8 quantization, so the same pattern works with compressed indexes. Product Launch; Company News AI isn’t a shortcut. It’s how business gets ahead.

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o text: a model pretrained on millions of tables can read a labeled table as its context and predict the labels of new rows directly. Today, we are releasing NVIDIA Kumo Tabular ( GitHub , HuggingFace ), an open foundation model for tabular classification and regression. Given a table with labeled rows and the rows you want predictions for, Kumo Tabular returns class probabilities or numeric predictions in a single forward pass. How Kumo Tabular Works Kumo Tabular is a Transformer built around t

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through Claude Platform on AWS in North America. Give Claude Sonnet 5.5 a try in the Amazon Bedrock console, in Claude Platform on AWS, or explore the Getting Started notebooks on GitHub. About the authors Dani Mitchell Dani is a Senior Specialist Solutions Architect for Generative AI at AWS, working on go-to-market for Anthropic on Amazon Bedrock. He helps enterprises across the world design and deploy generative AI solutions using Anthropic’s models and capabilities on Amazon Bedrock to build

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y gap. Example NV-Reason-CT run and output The following example loads NV-Reason-CT and runs a reasoning pass over a 3D CT volume. Checkpoints are available from Hugging Face. The GitHub repository includes inference scripts, training configurations, and post-training recipes. Output excerpt How NV-Reason-CT fits within the NVIDIA Medical AI ecosystem NV-Reason-CT is part of the NVIDIA Medical AI family of open medical AI models, designed to work together across the radiology pipeline: NV-Genera

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e through Claude Platform on AWS in North America. Give Claude Opus 5.5 a try in the Amazon Bedrock console, in Claude Platform on AWS, or explore the Getting Started notebooks on GitHub. About the authors Aamna Najmi Aamna is a Senior Specialist Solutions Architect for Generative AI focusing on Anthropic models and operationalizing and governing generative AI systems at scale on Amazon Bedrock. She helps ISVs solve their challenges, embrace innovation, and create new business opportunities with

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ws : Cognition introduced Devin Cloud in Terminal and devin ssh , making the model’s VM directly accessible from the CLI and allowing handoff between Devin and the user’s machine. GitHub Copilot teased editable diffs in the desktop app, while @pierceboggan showed a Sentry-integrated canvas for moving from crash report to fix. A recurring systems point: inference and agent infra are shifting toward test-time compute : @sarahookr predicts compute moving from pretraining—where marginal FLOPs yield

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atrick Esser, Dominik Lorenz, et al. 2026. Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis. arxiv.org/abs/2603.06507; Chen, Xiao. 2026. Awesome Astra Embodied AI. GitHub repository. github.com/zjwzcx/Awesome-Astra-Embodied-AI; Du, Yilun, Mengjiao Yang, Bo Dai, et al. 2023. Learning Universal Policies via Text-Guided Video Generation. arxiv.org/abs/2302.00111; Ebert, Frederik, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine. 2018. Visual Foresight: Model-Based

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tributions from community developers. You can enter the following request directly in your usual office agent: Help me install the core, api, and omni-related plugins from https://github.com/QwenLM/Qwen-MM-Plugins. Alternatively, install from the command line: Run the official guided installer: curl -fsSL https://raw.githubusercontent.com/QwenLM/Qwen-MM-Plugins/main/install.sh | bash In the menu, select: Install The agent harness you use The Omni plugins you need Plugin Capability core Read imag

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