←Search results

English text is being prepared.

By relevance · Total 13 results Clear search

Thursday

  1. Rohan Paul

    Google Releases EmbeddingGemma 2 Paper: A 744M-Parameter On-Device Multimodal Embedding Model

    Google has released the EmbeddingGemma 2 paper, an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. The model is built on Gemma 4 and tuned through contrastive training by learning from the larger Gemini Embedding 2; with 744M parameters, it surpasses models 2 to 12 times larger on multiple tasks. On the Pixel 11 Pro, the quantized text weights occupy about 191MB of active memory, while the full multimodal model takes about 567MB; the context window is expanded 4 times to 8K tokens, accommodating about 5.5 minutes of audio, 29 images, or 58 video frames; directly embedding speech scores 69.54 on the MSEB spoken query retrieval benchmark, higher than the 57.59 of transcribing first and then retrieving, and code retrieval on MTEB Code improves from 68.76 in EmbeddingGemma 1 to 78.68.

    Google has released the EmbeddingGemma 2 paper, an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. The model is built on Gemma 4 and tuned through contrastive training by learning from the larger Gemini Embedding 2; with 744M parameters, it surpasses models 2 to 12 times larger on multiple tasks. On the Pixel 11 Pro, the quantized text weights occupy about 191MB of active memory, while the full multimodal model t

    Matched fieldsTitle、Summary、Body

    #ai-news#Papers/Research#Multimodal#English text is being prepared.

Wednesday

  1. Jiwei.com

    A team at Tsinghua University has developed Airscope, a 1-gram wireless mesoscopic microscope, with the results published in Nature

    The team led by Dai Qionghai of Tsinghua University and the team led by Dai Pu of Chinese PLA General Hospital have developed Airscope, a wireless mesoscopic microscope weighing only 1 gram. It can record neural activity in the mouse cerebral cortex within a 6-millimeter field of view at 4 to 6 micrometers single-cell resolution, and a single mouse can have more than 8,500 neurons recorded simultaneously, 25 times that of existing devices of the same type. The device uses only 4 aspheric lenses and achieves on-device compression through a 2-megapixel image sensor, enabling wireless transmission of megapixel-level images and freeing it from cable constraints. The results were published online in Nature on the 19th, and the team has used it to record cortical activity in multiple mice in a 5-square-meter natural habitat and a large maze with 32 endpoints.

    The team led by Dai Qionghai of Tsinghua University and the team led by Dai Pu of Chinese PLA General Hospital have developed Airscope, a wireless mesoscopic microscope weighing only 1 gram. It can record neural activity in the mouse cerebral cortex within a 6-millimeter field of view at 4 to 6 micrometers single-cell resolution, and a single mouse can have more than 8,500 neurons recorded simultaneously, 25 times that of existing devices of the same type. The device uses only 4 aspheric lenses

    Matched fieldsSummary、Body

    #hot-signal#Papers/Research#English text is being prepared.

Saturday

Monday

Sunday

  1. Story1 reports · 1 facts

    Meta distills a 30B on-device agent model, Muse Glimmer

    Meta distilled the dense 30B model Muse Glimmer from the larger Muse Spark, training it on synthetic agent trajectories in simulated applications. Its 4-bit weights are under 20 GB, allowing it to run on a single RTX 5090. According to a report dated October 22, 2026, on the multi-tool benchmark MCPAtlas, Muse Glimmer scored 75.5, higher than Gemma4-31B's 54.2 and Qwen3.6-27B's 62.5; however, on OSWorld-Verified it lagged behind Qwen3.6-27B's 75.6 with a score of 65.9. There are currently no subsequent developments.

    Meta distilled the dense 30B model Muse Glimmer from the larger Muse Spark, training it on synthetic agent trajectories in simulated applications. Its 4-bit weights are under 20 GB, allowing it to run on a single RTX 5090. According to a report dated October 22, 2026, on the multi-tool benchmark MCPAtlas, Muse Glimmer scored 75.5, higher than Gemma4-31B's 54.2 and Qwen3.6-27B's 62.5; however, on OSWorld-Verified it lagged behind Qwen3.6-27B's 75.6 with a score of 65.9. There are currently no sub

    Matched fieldsTitle

  2. Story1 reports · 1 facts

    Meta compresses its Agent model Muse Glimmer 30B to run on-device

    On October 20, 2026, Meta released the technical report for Muse Glimmer, a 30B open-weight model aimed at agent tasks on consumer-grade hardware, running at 4-bit precision on a single consumer GPU. The reporter adds that the model leads open-source models of the same scale in tool calling and multi-step planning, surpassing Qwen3.6-27B and Gemma4-31B on some metrics; after 4-bit quantization it is about 17GB, targeting devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding. There are currently no subsequent developments.

    On October 20, 2026, Meta released the technical report for Muse Glimmer, a 30B open-weight model aimed at agent tasks on consumer-grade hardware, running at 4-bit precision on a single consumer GPU. The reporter adds that the model leads open-source models of the same scale in tool calling and multi-step planning, surpassing Qwen3.6-27B and Gemma4-31B on some metrics; after 4-bit quantization it is about 17GB, targeting devices with 24GB of VRAM, and uses DFlash speculative decoding to reach ab

    Matched fieldsTitle

  3. Story1 reports · 1 facts

    Dai Qionghai's team and Dai Pu's team develop the ultra-light wireless mesoscopic microscope Airscope

    The team led by Dai Qionghai at Tsinghua University and the team led by Dai Pu at Chinese PLA General Hospital have developed an ultra-light wireless mesoscopic microscope, Airscope. Weighing only 1 gram, the device can record neural activity in the mouse cerebral cortex within a 6-millimeter field of view at a single-cell resolution of 4 to 6 micrometers, capturing more than 8,500 neurons simultaneously in a single mouse—25 times that of existing devices of the same type. The device uses only 4 aspheric lenses and achieves on-device compression through a 2-megapixel image sensor, enabling wireless transmission of megapixel-level images and freeing it from cable constraints. The results were published online in Nature on the 19th. The team has used it to record cortical activity in multiple mice across a 5-square-meter natural habitat and a large maze with 32 endpoints.

    The team led by Dai Qionghai at Tsinghua University and the team led by Dai Pu at Chinese PLA General Hospital have developed an ultra-light wireless mesoscopic microscope, Airscope. Weighing only 1 gram, the device can record neural activity in the mouse cerebral cortex within a 6-millimeter field of view at a single-cell resolution of 4 to 6 micrometers, capturing more than 8,500 neurons simultaneously in a single mouse—25 times that of existing devices of the same type. The device uses only 4

    Matched fieldsSummary

Thursday

  1. FactRelated story

    Meta distills a 30B on-device agent model, Muse Glimmer

    Meta distilled the dense 30B model Muse Glimmer from the larger Muse Spark, training it on synthetic agent trajectories in simulated applications. Its 4-bit weights are under 20 GB, allowing it to run on a single RTX 5090. According to a report dated October 22, 2026, on the multi-tool benchmark MCPAtlas, Muse Glimmer scored 75.5, higher than Gemma4-31B's 54.2 and Qwen3.6-27B's 62.5; however, on OSWorld-Verified it lagged behind Qwen3.6-27B's 75.6 with a score of 65.9. There are currently no subsequent developments.

    Matched fieldsTitle

    View story:Meta distills a 30B on-device agent model, Muse Glimmer

Tuesday

  1. FactRelated story

    Meta compresses its agent model Muse Glimmer 30B to run on-device

    On October 20, 2026, Meta released the technical report for Muse Glimmer, a 30B open-weight model aimed at agent tasks on consumer-grade hardware, running at 4-bit precision on a single consumer GPU. The reporter adds that the model leads open-source models of the same scale in tool calling and multi-step planning, surpassing Qwen3.6-27B and Gemma4-31B on some metrics; after 4-bit quantization it is about 17GB, targeting devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding. There are currently no subsequent developments.

    Matched fieldsTitle

    View story:Meta compresses its Agent model Muse Glimmer 30B to run on-device

Friday

  1. Story1 reports · 1 facts

    Google Releases EmbeddingGemma 2 Paper

    Google has released the EmbeddingGemma 2 paper, introducing an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. Built on Gemma 4, the model learns from the larger Gemini Embedding 2 and is tuned with contrastive training; with 744M parameters, it outperforms models 2 to 12 times larger on multiple tasks. On the Pixel 11 Pro, the quantized text weights occupy about 191MB of active memory, while the full multimodal model takes about 567MB; the context window is expanded 4 times to 8K tokens, accommodating about 5.5 minutes of audio, 29 images, or 58 video frames. Embedding speech directly scores 69.54 on the MSEB spoken query retrieval benchmark, higher than the 57.59 achieved by transcribing first and then retrieving, and code retrieval on MTEB Code improves from 68.76 in EmbeddingGemma 1 to 78.68. There are currently no further developments.

    Google has released the EmbeddingGemma 2 paper, introducing an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. Built on Gemma 4, the model learns from the larger Gemini Embedding 2 and is tuned with contrastive training; with 744M parameters, it outperforms models 2 to 12 times larger on multiple tasks. On the Pixel 11 Pro, the quantized text weights occupy about 191MB of active memory, while the full multimodal

    Matched fieldsSummary

Friday

  1. Story1 reports · 1 facts

    Nvidia releases Nemotron 3.5 Super VL and Bolt models

    On October 15, 2026, Nvidia announced the launch of Nemotron 3.5 Super VL and the edge-oriented 3.5 Bolt models. According to Nvidia's official introduction, Super VL is a 120B-parameter open model aimed at always-on agent tasks, and the company says it has the highest throughput among comparable open models, 5 times faster than the next-best model; it also launched two edge models, Bolt 74B-A9B 24GB and Bolt E4B 4GB, with the former able to run agent tasks locally on RTX/DGX Spark and the latter aimed at voice and conversational agents on GeForce RTX and Jetson. Super VL and Bolt 74B-A9B are already available on Hugging Face, and Bolt E4B is scheduled for release on October 23. On the same day, SGLang announced Day-0 support for Super VL, reaching 243.9 tok/s on a single RTX 6000 PRO with the help of MTP; Prime Intellect and Nvidia used GRPO to post-train it for about 11 hours on 128 GB200 GPUs, raising its score on the public AutomationBench-AA set from 34.0% to 57.7%. That evening OpenRouter listed the model, and the next day Perplexity announced that Pro and Max subscribers and the Agent API can use it.

    On October 15, 2026, Nvidia announced the launch of Nemotron 3.5 Super VL and the edge-oriented 3.5 Bolt models. According to Nvidia's official introduction, Super VL is a 120B-parameter open model aimed at always-on agent tasks, and the company says it has the highest throughput among comparable open models, 5 times faster than the next-best model; it also launched two edge models, Bolt 74B-A9B 24GB and Bolt E4B 4GB, with the former able to run agent tasks locally on RTX/DGX Spark and the latte

    Matched fieldsSummary

Sunday

Tuesday