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Tuesday

  1. Zyphra Research

    Zyphra Inference Wave 2: Accelerating MoE and Long-Context Inference Serving on MI355X

    Zyphra has released ZOE, its second-generation inference optimization, which on AMD Instinct MI355X reduces GLM-5.3's time to first token (TTFT) at 512K context from 178.8 seconds to 64.6 seconds (2.8x), and from 42.3 seconds to 11.8 seconds (3.6x) at 90% prefix cache hit rate. Its CSA top-K selection HIP kernel raises DeepSeek-V4-Pro's single-user decode throughput at 1M context from 43.5 to 61.1 tokens/s (1.40x). AMD MXFP4 quantization of GLM-5.3 shrinks the model from 756 GB to 410 GB, with benchmark scores such as AIME25 within 3 points of FP8.

    Zyphra has released ZOE, its second-generation inference optimization, which on AMD Instinct MI355X reduces GLM-5.3's time to first token (TTFT) at 512K context from 178.8 seconds to 64.6 seconds (2.8x), and from 42.3 seconds to 11.8 seconds (3.6x) at 90% prefix cache hit rate. Its CSA top-K selection HIP kernel raises DeepSeek-V4-Pro's single-user decode throughput at 1M context from 43.5 to 61.1 tokens/s (1.40x). AMD MXFP4 quantization of GLM-5.3 shrinks the model from 756 GB to 410 GB, with b

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    #lab#Product updates#Reasoning#English text is being prepared.
  4. METR BlogSelected

    METR reviews the misalignment risk chapter of Anthropic's August 2026 risk report

    METR conducted an external review of the "Misalignment in high-risk scenarios" chapter in the August 2026 edition of Anthropic's risk report. It agreed that the catastrophic misalignment risk of the covered models (including Mythos 5 and the internal model Model 2) is low, but held that this is mainly due to limited model capabilities rather than safeguards and alignment training. METR pointed out that the covert capability benchmark in the report cannot represent a model's ability to evade Anthropic's internal monitoring systems, and stated that recent events weaken the report's argument that unknown severe situational misalignment is "unlikely." METR also recommended disclosing four redacted pieces of information and conducting end-to-end red-team testing of the monitoring system.

    METR conducted an external review of the "Misalignment in high-risk scenarios" chapter in the August 2026 edition of Anthropic's risk report. It agreed that the catastrophic misalignment risk of the covered models (including Mythos 5 and the internal model Model 2) is low, but held that this is mainly due to limited model capabilities rather than safeguards and alignment training. METR pointed out that the covert capability benchmark in the report cannot represent a model's ability to evade Anth

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    #safety#Safety / alignment#Evaluations / benchmarks#Anthropic
  5. Google Developers Blog

    Google integrates TorchTPU into TorchTitan to optimize model training for TPUs

    Google integrates TorchTPU into Meta's TorchTitan; Cloud TPU can now train PyTorch models directly through the native backend torch.device("tpu"), without rewriting them into JAX. The TorchTitan TPU branch provides pretraining recipes for Llama 3 70B/405B, Qwen 3 8B/32B/235B MoE, and FLUX.1 12B, and replaces standard implementations with Pallas kernels such as Splash Attention. On FLUX, SimpleFSDP brings TorchTitan to 99% performance parity with JAX MaxDiffusion.

    Torch Titan Header Image

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    #engineering#Product updates#Open-source ecosystem#English text is being prepared.
  13. Ant inclusionAI: New GitHub Repository

    Ant inclusionAI Open-Sources Ming-3.0-asr-tiny: Supports Chinese-English Transcription, Dialect Recognition, and Context ASR

    Ant inclusionAI has open-sourced Ming-3.0-asr-tiny on GitHub, combining the Ling-tiny-v3 language model with an audio encoder to support Chinese-English transcription, dialect recognition, and Context ASR. Dialect evaluation covers 15 test sets including Hunan, Minnan, Cantonese, and Sichuan-Chongqing; Context ASR can recognize proper nouns via hotwords in prompts or preceding dialogue. The inference stack uses Transformers 5.12.1, fla-core 0.5.2, and PyTorch 2.11.0, and provides an SGLang accelerated deployment example.

    Ming-3.0-asr-tiny model architecture

    Ant inclusionAI has open-sourced Ming-3.0-asr-tiny on GitHub, combining the Ling-tiny-v3 language model with an audio encoder to support Chinese-English transcription, dialect recognition, and Context ASR. Dialect evaluation covers 15 test sets including Hunan, Minnan, Cantonese, and Sichuan-Chongqing; Context ASR can recognize proper nouns via hotwords in prompts or preceding dialogue. The inference stack uses Transformers 5.12.1, fla-core 0.5.2, and PyTorch 2.11.0, and provides an SGLang accel

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    #engineering#English text is being prepared.#Speech#Open-source ecosystem
  15. Ant Group's inclusionAI: New GitHub repository

    Ant Group's inclusionAI open-sources the Ming-3.0-audio-flash native audio model

    Ant Group's inclusionAI has open-sourced the Ming-3.0-audio-flash native audio model on GitHub. It uses a 126B-parameter MoE architecture with 6.5B activated parameters, and there is also a Ming-3.0-audio-tiny version with 9.3B parameters and 2.7B activated parameters. The models cover Chinese and English recognition across multiple dialects and accents, long audio understanding, audio reasoning, and end-to-end speech dialogue. They support three inference methods: vLLM-Omni, SGLang, and Transformers, and are released under the MIT license.

    License: MIT

    Ant Group's inclusionAI has open-sourced the Ming-3.0-audio-flash native audio model on GitHub. It uses a 126B-parameter MoE architecture with 6.5B activated parameters, and there is also a Ming-3.0-audio-tiny version with 9.3B parameters and 2.7B activated parameters. The models cover Chinese and English recognition across multiple dialects and accents, long audio understanding, audio reasoning, and end-to-end speech dialogue. They support three inference methods: vLLM-Omni, SGLang, and Transfo

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    #engineering#Model releases#Open-source ecosystem#Speech

Wednesday

Monday

  2. TechCrunch AI

    Laplace Intelligence conducts pre-deployment red team testing for robots and autonomous vehicles

    Laplace Intelligence, incubated at the University of Michigan, is building automated red team testing for robots and physical AI. It uses high-fidelity simulation environments post-trained on Nvidia Cosmos plus AI agents to automatically design corner cases, finding and fixing problems before deployment. The company has already generated revenue and is slightly profitable; its customers include autonomous trucking company Bot Auto and a major Japanese automaker, and it is running a free smart city pilot with Nvidia and T-Mobile in San Jose, California.

    Laplace Intelligence, incubated at the University of Michigan, is building automated red team testing for robots and physical AI. It uses high-fidelity simulation environments post-trained on Nvidia Cosmos plus AI agents to automatically design corner cases, finding and fixing problems before deployment. The company has already generated revenue and is slightly profitable; its customers include autonomous trucking company Bot Auto and a major Japanese automaker, and it is running a free smart ci

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    #tech-media#Industry news#Safety / alignment#Embodied AI