Google Developers BlogGoogle AI Edge Gallery has launched on-device automatic speech recognition (ASR) and text-to-speech (TTS) capabilities, enabling fully offline operation of multilingual ASR and natural TTS models on phones and wearable devices.
PyTorch BlogExecuTorch WebGPU backend allows PyTorch models to run in WebGPU browsers without converting framework formats, supporting multiple models such as Llama 3.2, Qwen3, and Gemma 3.
Berkeley AI Research2222BAIR team presented multiple research achievements at the Robot Learning Conference (CoRL) held from November 9 to 12, 2026, in Austin, Texas, including 33 main conference papers, 4 breakthrough posters, 10 oral presentations, and 7 thematic speeches, among which Professor Negar Mehr will deliver an early career keynote speech. The conference covers topics such as robotic vision-language-action models, embodied agents, fine-grained manipulation alignment, and hindsight reward editing.
Google ResearchFeatured7272Google Research published a multimodal AI system AI-PPG-ACC-HTN in Nature, which passively identifies hypertension status without wristband calibration, using only PPG and accelerometer data from consumer-grade smartwatches.
Why it matters: The study passively identifies hypertension using PPG and accelerometer data from smartwatches, providing quantitative results for screening and confirmation in two stages, with 24-hour dynamic blood pressure monitoring as the gold standard.
IBM Research Blog3838HRL Laboratory develops an inertial measurement unit (IMU) using advanced algorithms, providing an uninterrupted GPS alternative. Its MEMS inertial sensor is the size of a can of soup, with accuracy close to GPS, and solves the long-standing problem of bias drift. Through the AutoCal automatic calibration system, it continuously corrects errors, achieving unlimited navigation support. The IMU uses ASIC and advanced packaging technology to optimize cost and size, making it suitable for light vehicles such as drones.
Hugging Face BlogFeatured7272Phonon releases a small TTS model that can run on local devices, without a server and network, offering two versions of about 65 MB and 75 MB, covering English, French, German, Spanish, and Portuguese.
Why it matters: The original text outlines the specifications, open-source license, and leaderboard performance of on-device TTS models, allowing judgment on the usability boundary of small models in local voice scenarios.
AWS Machine Learning Blog2828Amazon Bedrock now supports inference for Kimi K3 within India, meeting strict data localization requirements. Customer Ultrahuman enables their AI health assistant to handle over 14,000 daily conversations through API calls, while keeping health data within AWS, reducing costs by 80% and improving multi-turn conversation speed by 85%.
Hugging Face BlogFeatured7676Liquid AI releases the new generation Liquid Foundation Models, LFM3, deriving six 2B-level models from the shared backbone LFM3-1.7B-Base, covering text, vision, audio, multimodal, and decision, all open weights on Hugging Face.
Why it matters: Liquid AI derives six modality models from the same 2B-level backbone and provides single-card fine-tuning and edge deployment paths, which can be used to judge the specialization route for small models.
LangChain Deep Agents releasesThe deepagents-code==0.2.2 version of LangChain Deep Agents has been released, including security and performance improvements. New features include GPT-6.1 Terra support, Chinese text input to chat, plugin update detection, etc.; bug fixes include project .env file trust value settings, terminal UI link limits, Git option limits, etc.; performance has been improved by reusing parent process context, optimizing server startup processes, and streamlining transcription rendering, etc. It is recommended to upgrade as soon as possible.
AWS Machine Learning Blog5252AWS introduces three methods to train Qwen3 on AWS Trainium with torchtitan, including using Transformers v5, torchtitan native configuration, and Neuron Kernel Interface (NKI) mixture-of-experts (MoE) kernels.
Hugging Face BlogHugging Face introduced how to use TorchNeuron for system-level inference performance analysis on AWS Trainium, capturing clean traces with torch.profiler configured for the Neuron backend, and using key_averages() to identify time-consuming operations.
Google Developers BlogGoogle has released the native PyTorch backend for vLLM and SGLang inference engines, supporting TPU and optimizing production-level services; vLLM-TorchTPU achieved a cost of 2.01 cents per million tokens for 175B parameter models in the SemiAnalysis InferenceX benchmark, leading the industry.
Google Developers BlogCoral NPU has released a complete 32-bit architecture, including new matrix execution units, supporting up to 1 TOPS performance and 10mW average power consumption; launched the Coral Compiler v1.0 based on IREE/MLIR.
Google Developers BlogLiteRT Tensor API is released, supporting developers to write and optimize models natively in C++ on edge devices, enabling cross-platform deployment (CPU/GPU/NPU) without script conversion. Developers can fully control the computational graph structure and directly load model weights, achieving zero-copy data transfer through shared accelerator buffers, while providing building blocks for AI coding agents.
Google DeepMindGemini 4 Argon has achieved major breakthroughs in physical understanding, code generation, and visual semantic reasoning, achieving a pure reasoning score of 87% on the Embodied Reasoning (ERQA) benchmark test, surpassing all leading models. Argon can be applied to all stages of the robot's lifecycle, including mobile manipulation practice using MuJoCo, zero-shot robot control, and closed-loop steering of video world models.
Amazon ScienceAmazon Science proposes Priming technology, which reduces the pre-training cost of hybrid state space models by over 99% by leveraging pre-trained Transformer weights, enabling developers to explore problems that were previously too costly to investigate.
Google Developers BlogGoogle's AI Edge team has open-sourced ML Drift, a high-performance, cross-platform edge device GPU computing engine for on-device AI/ML inference. By abstracting the complexity of hardware low-level APIs such as OpenGL ES, OpenCL, Metal, and WebGPU, ML Drift enables developers to build real-time interactive AI experiences.
Amazon ScienceAmazon Science has released Graph Center Agent technology, which combines a continuous synchronized graph (digital twin) representing the network with device attributes and connections, with cascade graph algorithms (decomposition, aggregation, tracing) and agent orchestration to achieve minute-level root cause analysis of complex network failures, achieving 95% accuracy in a demonstration at NTT DOCOMO's MWC.
Google Developers BlogSparse VideoGen (SVG) dynamically routes attention heads to spatial or temporal masks by identifying the spatiotemporal structures of different attention heads in video diffusion models, converting theoretical sparsity into actual end-to-end speedup. On TPU v6e, SVG retains about 38.87% of query-key pairs, achieving 3.5x inference acceleration while maintaining output quality at a given sparsity level. The scheme employs custom JAX and Pallas Splash attention kernels to convert theoretical sparsity into physical sparsity on hardware.
Meta AI BlogMeta AI's DINOv3 and SAM models achieve real-time perception in edge computing, supporting the University of Pittsburgh's RAMMP project development of assistive robots, interacting through natural language and image data to reduce user cognitive load, already integrated in prototypes with automatic door button detection, cup recognition, and navigation assistance features, and plans to add voice and touch input support.
Berkeley AI ResearchAdapting to parallel reasoning allows models to decide on their own when to decompose and parallelize independent subtasks, how many threads to start, and how to coordinate. Existing methods such as fixed parallelism, heuristic structured search, and Monte Carlo tree search determine the parallel structure externally. Recent advancements like ParaThinker and GroupThink attempt to let models adaptively select parallel strategies, but decisions are still constrained externally.
