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Hao AI Lab integrates MiniMax H3 Flex into FastVideo
On November 4, 2026, Hao AI Lab announced that it has integrated Pika Labs' open-source MiniMax H3 Flex into FastVideo, claiming inference speeds up to 7 times faster than the official open-source inference code. On a single GB200, generating a 5-second 480p video with audio using the same prompt and random seed took 59.7 seconds with the official code, 26.6 seconds with FastVideo BF16, 16.8 seconds with NVFP4 QAD, and 15.2 seconds with FP4 attention. The official code supports only a single GPU; after FastVideo added multi-GPU support, generating a 15-second video on 4 GPUs was 7 times faster; generating a 5-second 480p first-and-last-frame video on a single DGX Spark took 59.5 seconds. The project also releases the weights, inference code, and complete training recipe, and supports the 2K model of Open-H3-Regenerate-2K.
On November 4, 2026, Hao AI Lab announced that it has integrated Pika Labs' open-source MiniMax H3 Flex into FastVideo, claiming inference speeds up to 7 times faster than the official open-source inference code. On a single GB200, generating a 5-second 480p video with audio using the same prompt and random seed took 59.7 seconds with the official code, 26.6 seconds with FastVideo BF16, 16.8 seconds with NVFP4 QAD, and 15.2 seconds with FP4 attention. The official code supports only a single GPU
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Hao AI Lab supports MiniMax H3 Flex in FastVideo
On November 4, 2026, UCSD's Hao AI Lab announced that it would integrate Pika Labs' open-source MiniMax H3 Flex into FastVideo. According to its published information, the official open-source inference code takes 59.7 seconds to generate a 5-second 480p video with audio on a single GB200, while FastVideo BF16 takes 26.6 seconds, NVFP4 QAD takes 16.8 seconds, and FP4 attention takes 15.2 seconds. Hao AI Lab claims it is up to 7 times faster than the official code, and added multi-GPU support, with 4 GPUs generating a 15-second clip 7 times faster. NVFP4 QAD distills BF16 Flex, coming 14-17% closer to BF16 than ordinary quantization; FP4 attention reduces a 768p 15-second clip from 101.8 seconds to 85.3 seconds. That clip takes 59.5 seconds on a single DGX Spark. FastVideo also supports the Open-H3-Regenerate-2K 2K model, and the weights, inference code, and complete training recipe have all been released.
On November 4, 2026, UCSD's Hao AI Lab announced that it would integrate Pika Labs' open-source MiniMax H3 Flex into FastVideo. According to its published information, the official open-source inference code takes 59.7 seconds to generate a 5-second 480p video with audio on a single GB200, while FastVideo BF16 takes 26.6 seconds, NVFP4 QAD takes 16.8 seconds, and FP4 attention takes 15.2 seconds. Hao AI Lab claims it is up to 7 times faster than the official code, and added multi-GPU support, wi
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Hao AI Lab announces that FastVideo supports Open-H3-Regenerate-2K
On November 4, 2026, UCSD Hao AI Lab announced that FastVideo now supports the Open-H3-Regenerate-2K model from HaoZhe65347 and NVIDIA AI, and provided a link to the 2K model. The same source also reviewed the series' earlier work: FastVideo optimized inference for Pika Labs' open-source MiniMax H3 Flex, generating a 5-second 480p video with audio on a single GB200 in 5.8 seconds, up to 7 times faster than the official open-source inference code, and added multi-GPU support. There are currently no subsequent developments.
On November 4, 2026, UCSD Hao AI Lab announced that FastVideo now supports the Open-H3-Regenerate-2K model from HaoZhe65347 and NVIDIA AI, and provided a link to the 2K model. The same source also reviewed the series' earlier work: FastVideo optimized inference for Pika Labs' open-source MiniMax H3 Flex, generating a 5-second 480p video with audio on a single GB200 in 5.8 seconds, up to 7 times faster than the official open-source inference code, and added multi-GPU support. There are currently
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Cohere launches third-party open-source models on Model Vault
On November 3, 2026, Cohere launched third-party open-source models in Model Vault and Model Vault Encrypted, allowing enterprises to run both Cohere's own models and open-source models such as DeepSeek V4.1 Flash, GLM 5.3 Flash, Gemma 4, GPT-OSS, and Phi on a fully managed encrypted inference platform. According to Cohere's product and research blog, Model Vault applies zero data retention to prompts and outputs by default, and the Encrypted version uses confidential computing so that neither Cohere nor cloud providers can see the data. Model Vault is already available in the United States, the European Union, and Canada; the Encrypted version is currently limited to the United States, with support for the European Union and Canada coming soon.
On November 3, 2026, Cohere launched third-party open-source models in Model Vault and Model Vault Encrypted, allowing enterprises to run both Cohere's own models and open-source models such as DeepSeek V4.1 Flash, GLM 5.3 Flash, Gemma 4, GPT-OSS, and Phi on a fully managed encrypted inference platform. According to Cohere's product and research blog, Model Vault applies zero data retention to prompts and outputs by default, and the Encrypted version uses confidential computing so that neither C
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Google DeepMind launches cheminformatics skill for Antigravity
On November 3, 2026, Google DeepMind released a cheminformatics skills set (Cheminformatics Skills) for Google Antigravity. The skill is based on RDKit, connects to ChEMBL and PubChem, and has a language model responsible for task orchestration and scientific reasoning, while deterministic chemistry tools perform molecular computations. Taking a lung cancer drug as an example, the agent retrieved thousands of compounds within about one hour, completed matched molecular pair analysis and trained a potency prediction model, and also proposed novel rigidified macrocyclic molecule designs with synthetic routes. Google DeepMind also noted that sparse data, inter-laboratory assay noise and narrow dynamic range can limit prediction accuracy, and that computational design is only a verifiable hypothesis that still requires experimental synthesis for validation. The skill is now live on Antigravity and can be used by installing the Science plugin, and free access to the highest usage tier for six months is being offered to 10,000 scientists.
On November 3, 2026, Google DeepMind released a cheminformatics skills set (Cheminformatics Skills) for Google Antigravity. The skill is based on RDKit, connects to ChEMBL and PubChem, and has a language model responsible for task orchestration and scientific reasoning, while deterministic chemistry tools perform molecular computations. Taking a lung cancer drug as an example, the agent retrieved thousands of compounds within about one hour, completed matched molecular pair analysis and trained
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OpenClaw launches Particle-1
On November 3, 2026, OpenClaw launched Particle-1, and the model is now available in OpenClaw. According to @particle_api, compared with DeepSeek V4.1 Flash, Particle-1 reduces output tokens by about 45%, achieves comparable benchmark accuracy, and retains no data during standard inference. Users can try it by upgrading to OpenClaw v2026.10.10. There are currently no further developments.
On November 3, 2026, OpenClaw launched Particle-1, and the model is now available in OpenClaw. According to @particle api, compared with DeepSeek V4.1 Flash, Particle-1 reduces output tokens by about 45%, achieves comparable benchmark accuracy, and retains no data during standard inference. Users can try it by upgrading to OpenClaw v2026.10.10. There are currently no further developments.
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LG AI Research proposes DistPFN to mitigate label shift in tabular models
In an ICML 2026 blog post, LG AI Research pointed out that tabular foundation models such as TabPFN suffer from label shift: among 253 OpenML datasets, about 85% (214) are class-imbalanced, and in extreme cases 98.3% of test samples were predicted as the majority class, with minority-class recall nearly collapsing. To address this, LG AI Research proposed DistPFN, which at inference time multiplies predicted probabilities by a correction factor based on relative training class proportions, requiring no architecture changes, retraining, or estimation of the test label distribution. The work is currently published as an official blog post, with no further progress disclosed.
In an ICML 2026 blog post, LG AI Research pointed out that tabular foundation models such as TabPFN suffer from label shift: among 253 OpenML datasets, about 85% (214) are class-imbalanced, and in extreme cases 98.3% of test samples were predicted as the majority class, with minority-class recall nearly collapsing. To address this, LG AI Research proposed DistPFN, which at inference time multiplies predicted probabilities by a correction factor based on relative training class proportions, requi
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Report: DeepSeek V5 plans pretraining on Ascend chips
On November 3, 2026, according to a report by Star Market Daily, multiple industry chain sources said that DeepSeek's next-generation large model, DeepSeek V5, plans to conduct pretraining based on Huawei Ascend chips. A same-day report by East Money mentioned that previously Ascend chips were mainly used for inference tasks of DeepSeek models; after the release of DeepSeek-V4 in April 2026, Huawei said that the full series of Ascend supernode products could fully support the V4 series models; on September 30, DeepSeek open-sourced infrastructure components for the Ascend computing platform, including the TileLang compilation tool, DeepGEMM, FlashMLA, and DeepEP. The report also said that at present, the implementation of domestic computing power is still concentrated on the inference side, and the full-parameter post-training of DeepSeek-V4-Pro previously completed by the Ascend 910C cluster did not involve basic pretraining.
On November 3, 2026, according to a report by Star Market Daily, multiple industry chain sources said that DeepSeek's next-generation large model, DeepSeek V5, plans to conduct pretraining based on Huawei Ascend chips. A same-day report by East Money mentioned that previously Ascend chips were mainly used for inference tasks of DeepSeek models; after the release of DeepSeek-V4 in April 2026, Huawei said that the full series of Ascend supernode products could fully support the V4 series models; o
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ARC Prize publishes evaluation results for DeepSeek V4.1 Flash 1017
On November 3, 2026, ARC Prize published the verified ARC-AGI results for DeepSeek V4.1 Flash 1017: 78.1% on ARC-AGI-2 at $0.16 per task, and 94.5% on ARC-AGI-1 at $0.06 per task; on ARC-AGI-3 it scored 5.8% at a cost of $1.2K under the provider adapter harness, and 0.2% at $1.0K under the standard harness. ARC Prize stated that the model leads open-weight models on ARC-AGI-3 and ARC-AGI-2, and matches the best open-weight model results on ARC-AGI-1. Compared with the original V4.1 Flash of September 10, 2026, 1017's ARC-AGI-3 score rose from 2.68% to 5.81% on the semi-private set while falling from 11.05% to 8.97% on the public set; at maximum reasoning effort, the reasoning tokens consumed by its first action decreased by an average of 36% across 25 public games and by 48.6% across 55 semi-private games.
On November 3, 2026, ARC Prize published the verified ARC-AGI results for DeepSeek V4.1 Flash 1017: 78.1% on ARC-AGI-2 at $0.16 per task, and 94.5% on ARC-AGI-1 at $0.06 per task; on ARC-AGI-3 it scored 5.8% at a cost of $1.2K under the provider adapter harness, and 0.2% at $1.0K under the standard harness. ARC Prize stated that the model leads open-weight models on ARC-AGI-3 and ARC-AGI-2, and matches the best open-weight model results on ARC-AGI-1. Compared with the original V4.1 Flash of Sept
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AWS releases a tutorial on distilling a shopping agent with SeqKD on SageMaker AI
On November 3, 2026, the official AWS Machine Learning blog published a tutorial on how to build an efficient shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD). The tutorial uses GLM-5.3 on Amazon Bedrock as the teacher model to generate trajectories, uses the ShoppingBench scorer to retain only trajectories that solve the task, and then performs serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the average absolute success rate of the distilled 4B student rose from 0.297 to 0.541, essentially on par with the teacher model and mid-sized frontier models; when the teacher's reasoning process was removed and only tool calls were learned, the average success rate dropped to 0.415. The tutorial is now publicly available alongside the official blog post.
On November 3, 2026, the official AWS Machine Learning blog published a tutorial on how to build an efficient shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD). The tutorial uses GLM-5.3 on Amazon Bedrock as the teacher model to generate trajectories, uses the ShoppingBench scorer to retain only trajectories that solve the task, and then performs serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the average absolute success ra
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NVIDIA Expands Cosmos Platform Training Tools and Data Blueprints
On October 22, 2026, NVIDIA announced the expansion of the Cosmos platform, adding training tools, simulation checkpoints, and Cosmos Data Factory blueprints to help developers build world models. According to information released by the NVIDIA Newsroom, Cosmos Data Factory provides five cloud-native blueprints, now available through GitHub, Microsoft Azure, Nebius, and CoreWeave; Porsche is using it to build a shared autonomous driving data flywheel, which is expected to reduce repetitive real-vehicle testing by up to 90% and shorten development cycles by up to 85%. Cosmos Framework's training, optimization, and deployment tools are being extended in the form of agent skills to open-source models such as GR00T, Alpamayo, QWEN, and Pi0.5, and the Cosmos 3 NIM microservice delivers up to 2x faster inference under validated configurations. There are currently no further developments.
On October 22, 2026, NVIDIA announced the expansion of the Cosmos platform, adding training tools, simulation checkpoints, and Cosmos Data Factory blueprints to help developers build world models. According to information released by the NVIDIA Newsroom, Cosmos Data Factory provides five cloud-native blueprints, now available through GitHub, Microsoft Azure, Nebius, and CoreWeave; Porsche is using it to build a shared autonomous driving data flywheel, which is expected to reduce repetitive real-
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Karpathy builds HackerTube with Claude Opus 5.5 in two hours
Over the weekend, Andrej Karpathy used Claude Code together with Opus 5.5 to build the video site HackerTube in about two hours. According to a report by A Yi AI Notes, he filtered roughly 43 million Hacker News comments from the past twenty years down to 6,454 frequently recommended long videos; the site requires no login, uses a middle frame of each video as its thumbnail, and stores browsing state in the local browser session. He himself only manually applied for a Google Cloud YouTube API key, rented a VPS and bought a domain, then handed the IP over to Claude, which completed the deployment, security hardening and code optimization. A follow-up report by 36Kr · AI added that he used the desktop version of Claude Code with Opus 5.5 at Medium reasoning effort, that the page layout replicates YouTube, and that the underlying data can be exported and handed to one's own AI agent to rebuild a version. There is currently no further public progress on this matter.
Over the weekend, Andrej Karpathy used Claude Code together with Opus 5.5 to build the video site HackerTube in about two hours. According to a report by A Yi AI Notes, he filtered roughly 43 million Hacker News comments from the past twenty years down to 6,454 frequently recommended long videos; the site requires no login, uses a middle frame of each video as its thumbnail, and stores browsing state in the local browser session. He himself only manually applied for a Google Cloud YouTube API ke
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Browser Use opens BU Ultrafast to all US cloud plans
On October 31, 2026, Browser Use opened its fastest browser agent, BU Ultrafast, from the waitlist to all US Cloud plans, including the free tier. Official data shows that BU Ultrafast has a median task time of 22 seconds and a cost of $0.0064 per run; by comparison, the default agent on GPT-6 Luna takes 79 seconds and costs $0.0098. To use it, simply set model to bu-ultrafast; for more step-by-step reasoning, you can switch to bu-fast from the same agent, which is billed at standard rates. There are currently no further developments.
On October 31, 2026, Browser Use opened its fastest browser agent, BU Ultrafast, from the waitlist to all US Cloud plans, including the free tier. Official data shows that BU Ultrafast has a median task time of 22 seconds and a cost of $0.0064 per run; by comparison, the default agent on GPT-6 Luna takes 79 seconds and costs $0.0098. To use it, simply set model to bu-ultrafast; for more step-by-step reasoning, you can switch to bu-fast from the same agent, which is billed at standard rates. Ther
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