Ai2 has published OlmoEarth v1.3, an embedding model that condenses a full year of radar and multispectral satellite data into a single compact vector for every 10-meter patch of the planet. Alongside it comes a free browser tool, the OlmoEarth Embeddings Explorer, that lets anyone compare landscapes across years or train tiny classifiers without touching a GPU. Benchmarked against AlphaEarth Foundations and Tessera v2 across 27 tests, the new model comes out on top overall while costing far less to train than its predecessors.
IBM rolled out three updates to its Granite Speech family: a brand-new 2B speech-language model (granite-speech-5.0-2b), a revised 470M Turbo CTC model built for speed, and an improved 4.1-2B-Plus model tuned for multi-speaker recordings. Together they target high-accuracy transcription/translation, fast on-device recognition, and accurate speaker/timestamp labeling. All three remain under Apache 2.0.
IBM Research released vLLM-Hook MIA, a plugin for the open-source vLLM inference engine that lets developers observe and intervene in a model's internal states at runtime — for example to decline malicious prompts, flag hallucinated facts, or adjust tone — without disabling vLLM's fast graph-replay execution mode. According to IBM's technical report, MIA is up to 20x faster than the earlier vLLM-Hook and up to 14x faster than methods that insert code directly into the model. In tests, steering an 8B Llama or 1.5B Qwen model added only about 2% latency, and a 70B Llama model added about 6%.
Google announced new vLLM and SGLang inference engines built directly on PyTorch for TPU, developed with Inferact and RadixArk respectively. The engines run on a new TorchTPU layer that makes TPUs a native PyTorch device, replacing the previous JAX-based backend approach. Google reports a public benchmark result of 2.01 cents per million tokens in high-concurrency configurations via the SemiAnalysis InferenceX project, plus support for agentic workloads, long context, disaggregated serving and multimodal models.
Google Research announced the final milestone of its Coral NPU open hardware standard — a 32-bit RISC-V architecture with a new Matrix Execution Unit for up to 1 TOPS at roughly 10mW — plus a v1.0 open-source IREE/MLIR compiler that takes JAX, PyTorch and TensorFlow models directly to the hardware. Synaptics is launching the first commercial silicon built on this standard, including the Astra SR200 microcontroller series (400 GOPS) and new Coralboard SR and SL developer kits, with the SL now generally available via DigiKey. Google is also partnering with VeriSilicon and GlobalFoundries to bring the open RTL into production chips.
Red Hat, IBM, and Google announced llm-d 1.0, a Kubernetes-native distributed inference serving system that moves beyond proof-of-concept performance tricks into full production readiness. The release adds multi-tenant quality of service, zero-downtime operations, and agent-aware serving so the same endpoint can handle both human users and autonomous agents efficiently. In one test, llm-d's prefix-aware router captured 97% of available cache reuse on an agentic coding workload, and IBM/Red Hat ran 3,000 concurrent software-engineering agents for 5 to 10 times less cost than using the same model as a hosted service.
Reflection, a new US AI lab, has launched Beam, its first model, as an open-weight mixture-of-experts system with 501B total parameters (23B active per token) and a 1M-token context window, released under Apache 2.0. The model is built for coding and agentic use cases, ships with a custom Rust-based inference harness, and has day-0 support in transformers, vLLM and SGLang. Reflection reports strong results on coding, reasoning and tool-calling benchmarks across BF16, FP8 and NVFP4 checkpoints.
IBM has released Granite Switch 4.1 in 8B and 30B sizes, a modular system that runs chat, decision-making and speech transcription from one hosted endpoint instead of separate models. The update adds a general-purpose Decision adapter, a specialized Guardian adapter for safety-related classifications, and an embedded ASR model, Granite-Speech-5.0-Turbo-CTC, which tops the OpenASR Leaderboard. Despite bundling three capabilities, the combined model uses roughly the same VRAM as the base model alone.
Mistral AI has launched Timestral, its first family of time-series foundation models, available in four sizes from 5M to 209M parameters. The largest model ranks first on all three major public forecasting benchmarks (GIFT-Eval, TIME, fev-bench), beating previous state-of-the-art models that have more parameters. The models produce probabilistic, multivariate forecasts, support past and known-future covariates, and run efficiently on CPUs without needing a GPU.
AWS detailed three kernel-level optimizations for Mixture-of-Experts (MoE) model serving on Hopper GPUs, implemented in collaboration with the vLLM community and shipped in Amazon SageMaker AI's LMI container. The changes target the inefficiencies of fused MoE computation during low-batch decode, which previously left Tensor Cores underutilized. Combined, the optimizations deliver up to 10% higher throughput and up to 9% faster time-to-first-token (TTFT).
NVIDIA has released the full Run:ai platform as open source, adding it as a foundation of the NVIDIA DSX AI Factory Platform for AI workload and GPU orchestration on Kubernetes. The release includes four modular components — KAI Scheduler, GPU Fractioning Operator, KAI Resource Management, and Workload Map — that can be adopted together or independently. Eight ecosystem partners, including Crusoe, Mirantis, Nebius, OpenNebula Systems, Rafay, Saturn Cloud, Spectro Cloud, and vCluster, are integrating the platform into their own offerings.
IBM Research used a coordinated multi-agent 'agent factory' to explore hardware design optimizations for a quantum error-correction decoder, comparing proprietary and open frontier models under the same 100-hypothesis search budget. The open-model configuration, run on-premises via IBM's llm-d distributed inference framework, achieved a 52x speedup over baseline for about $26 in inference cost, versus $299 for a proprietary-model configuration that reached a higher 204x speedup. The study highlights that cache-aware routing and efficient serving (98% prefix-cache hit rate, ~$0.11 per million tokens) made the open-model approach dramatically cheaper while still finding valid, compact hardware designs.
Amazon Web Services researchers published a method called 'priming' that converts pretrained Transformer models into hybrid architectures mixing attention and state space model (SSM) layers, reusing existing weights instead of retraining from scratch. The approach cuts pretraining costs by over 99% and lets a 32-billion-parameter hybrid model match its source Transformer's quality while decoding up to twice as fast. Amazon tested three SSM types and released an open-source model zoo, training code, and serving tools under Apache 2.0.
At the OCP Global Summit, NVIDIA contributed its Vera Rubin rack and compute tray designs to the open MGX architecture and announced a series of partnerships covering AI factory power, cooling and networking standards. Partners include hyperscalers such as Meta, Google, Microsoft and AMD, plus Cisco and Visionbay. The announcements cover rack design specs (MGX 1.2), new open-source software for managing AI data centers (DSX OS components), grid resilience standards, an 800VDC power architecture, new Ethernet switching (Spectrum-6) and a new optical transceiver standard (USFP) with Cisco.
Mistral AI has released Voxtral Mini 4B Realtime Arabic, a streaming speech-to-text model fine-tuned for Arabic dialects, Modern Standard Arabic, and code-switching between languages. The 4.4-billion-parameter model processes 16 kHz audio in real time with a configurable transcription delay, achieving an 8.82% average Character Error Rate across seven Arabic benchmarks at a 480ms delay — close to the offline Voxtral Transcribe Arabic model's 7.91% CER. It was co-developed with Morocco's digital transition ministry as part of a sovereign AI partnership and is released under Apache 2.0.
Anthropic has announced the Cyber Mission, a long-term initiative to help defenders secure operational technology and open-source code using Claude models. It includes the Critical Infrastructure Defense Program, built with partners like Accenture, CrowdStrike, Deloitte and Rockwell Automation, and the free OSS Scanner tool that scans open-source projects for vulnerabilities. The effort builds on lessons from the earlier Project Glasswing, now merged into an expanded Cyber Verification Program.
Google released ML Drift, an open-source, cross-platform GPU compute engine for on-device ML inference, replacing the legacy TFLite GPU delegate. It unifies shader handling across OpenGL ES, OpenCL, Metal and WebGPU, adds 5D tensor support, and introduces stage-aware optimizations for LLM prefill/decode. It is already running in production in Chrome, YouTube Shorts, Google Photos, Meet and partner apps from Adobe and Snap.
The Technology Innovation Institute (TII) has released Falcon-ASR, a 1.6 billion parameter automatic speech recognition model built primarily for Arabic, with particular focus on the Emirati dialect. It also transcribes English, French, Spanish and Portuguese using the same model weights. TII reports it beats the best published leaderboard result on a standard Arabic benchmark and leads on an internal Emirati evaluation, while also including word-level timestamps.
Anthropic has merged its Project Glasswing and Cyber Verification Program into a single, expanded CVP with three access tiers: Defense Access, Red Team Access, and Specialized Access. Each tier unlocks progressively fewer safety blocks on cyber-related tasks in Claude Opus 5.5, Claude Sonnet 5.5, and Claude Mythos 5.1 for vetted security organizations. Anthropic also published benchmark results and vulnerability-discovery numbers from the earlier Glasswing program to justify the expansion.
GitHub Security Lab built an open-source 'Taskflow Agent' that uses custom AI prompt workflows to audit code for vulnerabilities, and used it to find and report over 24 vulnerabilities in Android applications. Two detailed examples show the agent finding a location-tracking flaw in the OsmAnd navigation app and an account-takeover chain in the Wikipedia Android app via a deeplink parsing bug. The team notes LLMs are strong at finding complex logic vulnerabilities and understand API behavior well, but struggle to correctly judge severity and often produce false positives, so findings still require human review. The taskflows are open source and can be run on any GitHub repository via a Copilot-powered codespace script, though running them consumes premium model requests and can take hours.
Allen Institute for AI (Ai2) has open-sourced AstaBrief 8B, a model built on Qwen3-8B that turns research questions and retrieved literature excerpts into cited scientific reports. It's now live in Ai2's Asta platform as a 'Fast mode' alongside a Claude-powered 'Thinking mode', generating reports in about 51.1 seconds on average compared to 178.5 seconds for the Claude pipeline — roughly 3.5x faster. Ai2 trained the model using supervised fine-tuning on 47K examples (from 90K filtered real research queries) followed by DPO on about 6K preference pairs, and found that filtering training data for citation density was the single most effective lever for improving grounding quality.
Google DeepMind unveiled SynthID Bio, a watermarking technology that embeds an imperceptible, verifiable signature into AI-designed proteins and 3D structures without compromising their biological function. The system was tested on protein binders (VEGF-A, SARS-CoV-2 spike RBD, PD-L1) using AlphaProteo and a modified ProteinMPNN, and also fine-tuned into AlphaFold 3's diffusion network for watermarking predicted 3D structures. DeepMind is also collaborating with Stanford's Hie lab and Arc Institute to apply the approach to Evo 2, a genomic model, to watermark designed bacteriophage genomes. The goal is to strengthen biosecurity by helping DNA synthesis screening providers and public databases verify the provenance of AI-generated biological designs.