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Wednesday

  1. FactRelated story

    Flatkey gateway supports calling multimodal models in Codex and Claude Code via CLI

    As a unified API gateway, Flatkey supports calling multimodal models in Codex and Claude Code via CLI. According to a report dated October 21, 2026, the gateway can call text models such as GPT, Claude, DeepSeek, GLM, and Kimi, as well as image and video models such as GPT Image and Seedance; users can access 100+ official models and 1,000+ tool APIs with a single Key, with costs consolidated into one bill. The report also states that its everyday prices are at least 20% lower than official pricing; during the October 19–23 promotion, GPT, GLM 5.2, Kimi K3, and DeepSeek are 40% off, Claude is 20% off, and Seedance 2.0 is 15% off. There are currently no subsequent developments.

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    View story:The Flatkey gateway supports calling multimodal models in Codex and Claude Code via CLI

Tuesday

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  3. FactRelated story

    Central China Normal University and others propose multimodal medical image fusion method CoReFuse-Med

    Central China Normal University, Huazhong University of Science and Technology, Guangzhou University of Chinese Medicine and its First Affiliated Hospital, and the University of North Carolina at Chapel Hill propose a multimodal MRI segmentation network, CoReFuse-Med. The method uses LACE+IMSH to remove resampling artifacts at shallow layers and MBCF to calibrate modality contributions at deep layers, addressing the problem of low-quality modalities contaminating the fusion. On the EPVS dataset, it achieves a DSC of 0.7326 and an HD95 of 7.35, outperforming MedNeXt's 0.7085 and 8.17; on BraTS, its DSC is 0.8528 with only 2.665M parameters. The method targets the resolution degradation after spatial alignment and does not cover scenarios with missing modalities or strong noise.

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    View story:Central China Normal University and others propose multimodal medical image fusion method CoReFuse-Med

Sunday

  1. Story1 reports · 1 facts

    Luma proposes an exact sequence parallelism method for packed multimodal masking

    Luma proposes an exact sequence parallelism method for packed multimodal masking. At each layer, the method chooses among ring attention, sparse all-to-all, or Ulysses, and its numerical results are consistent with those on a single GPU. On up to 128 H100 GPUs, the method outperforms the FlashMask + Ulysses hybrid scheme in 47 of 48 cases. There are currently no follow-up developments.

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  2. FactRelated story

    Luma proposes an exact sequence parallelism method for packed multimodal masks

    Luma proposes an exact sequence parallelism method for packed multimodal masking. At each layer, the method chooses among ring attention, sparse all-to-all, or Ulysses, and its numerical results are consistent with those on a single GPU. On up to 128 H100 GPUs, the method outperforms the FlashMask + Ulysses hybrid scheme in 47 of 48 cases. There are currently no follow-up developments.

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    View story:Luma proposes an exact sequence parallelism method for packed multimodal masking

  3. Story1 reports · 1 facts

    Central China Normal University and others propose multimodal medical image fusion method CoReFuse-Med

    Central China Normal University, Huazhong University of Science and Technology, Guangzhou University of Chinese Medicine and its First Affiliated Hospital, and the University of North Carolina at Chapel Hill propose a multimodal MRI segmentation network, CoReFuse-Med. The method uses LACE+IMSH to remove resampling artifacts at shallow layers and MBCF to calibrate modality contributions at deep layers, addressing the problem of low-quality modalities contaminating the fusion. On the EPVS dataset, it achieves a DSC of 0.7326 and an HD95 of 7.35, outperforming MedNeXt's 0.7085 and 8.17; on BraTS, its DSC is 0.8528 with only 2.665M parameters. The method targets the resolution degradation after spatial alignment and does not cover scenarios with missing modalities or strong noise.

    Central China Normal University, Huazhong University of Science and Technology, Guangzhou University of Chinese Medicine and its First Affiliated Hospital, and the University of North Carolina at Chapel Hill propose a multimodal MRI segmentation network, CoReFuse-Med. The method uses LACE+IMSH to remove resampling artifacts at shallow layers and MBCF to calibrate modality contributions at deep layers, addressing the problem of low-quality modalities contaminating the fusion. On the EPVS dataset,

    Matched fieldsTitle

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