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Nov 3Tue
  1. AWS Machine Learning Blog34

    Prepare web content for Amazon Bedrock Knowledge Bases with Crawl4AI

    This post explains when to use the Amazon Bedrock Web Crawler connector versus a custom Crawl4AI-based preparation step that writes Markdown and metadata sidecars to Amazon S3 for ingestion into Amazon Bedrock Knowledge Bases. It walks through the sample AWS CDK workflow—scheduled Fargate crawling, S3 upload, and ingestion job—and covers chunking strategies, metadata filtering for retrieval validation, and responsible AI and security considerations.

Oct 30Fri
  1. Baseten Blog58

    Decisions for a penny: introducing joey-1, our fastest decision model

    Base Labs introduces joey-1, a decision model with 3B or fewer parameters that returns a label and calibrated probability score in a single forward pass, serving an average 2,000-token request at a P50 end-to-end latency of 20 ms (about 2.5x faster than elephant-1) at $0.01 per million tokens. Post-trained from Qwen3.5-2B using the same data and recipe as elephant-1 and mammoth-1, it is available on Baseten Model APIs with open weights on Hugging Face, and supports a 16k context window on Model APIs versus the full 262k on dedicated deployments.

Oct 29Thu
  1. vLLM Blog62

    Day 0 support for Mistral Large 4 on vLLM

    vLLM announces day-0 support for Mistral Large 4, a 1T-parameter MoE multimodal model with 49B active parameters per token, running on the V2 model runner with breakable CUDA graphs and a new vllm/models/mistral/ package. The work adds cross-layer KV sharing, DCP/PCP context parallelism, DSpark speculative decoding, NIXL-based disaggregated serving and HiSparse KV offload, plus day-0 ROCm support on AMD Instinct MI350 Series GPUs with MXFP4 experts. Support ships in the vllm/vllm-openai:mistral-large-4 Docker image, with FP8 and NVFP4 checkpoints and serving commands documented.

    Why it matters: A detailed engineering account of what it takes to land a frontier model on an inference engine, useful for teams planning long-context or disaggregated deployments.

  2. Mistral AI82

    Le Chonk: Built for Coders, Trusted by Defenders.

    Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model trained from scratch in Europe, reporting leading open-weight results on coding benchmarks such as FrontierCode 2.0 (19.7%) and Terminal-Bench 4.0 (41%), plus 51% on the Artificial Analysis Cyber Index. It offers three reasoning modes (Low, Medium, High) and is priced at $0.70 input / $0.07 cached input / $2.10 output per million tokens under a modified MIT license that requires a separate commercial license for Model-as-a-Service offerings.

    Why it matters: The post details benchmark scores, pricing, licensing terms and deployment options for a flagship open-weight model, useful for developers weighing coding performance against cost and control.

  3. Hugging Face Blog58

    Introducing AnySpeechRecognizer and AnyDecisionModel: Open Models for Speech and Decisions on Apple Platforms

    The author announces two Swift packages: AnySpeechRecognizer, which mirrors Apple's SpeechAnalyzer API while allowing alternative engines such as NVIDIA's Parakeet and Alibaba's Qwen3-ASR running locally on Core ML, plus speaker diarization via NVIDIA's Streaming Sortformer; and AnyDecisionModel, which answers yes-or-no, multiple-choice, and rating-scale questions with typed results and probabilities from backends including MLX, Core AI, llama.cpp, Jev, and Ollama. Both can download open models from the Hugging Face Hub and run them on device, require Swift 6.2 and macOS 26 or iOS 26 or later, and are published on GitHub.

  4. Databricks Blog36

    Voice Agents are here. Context turns them into real customer value.

    Databricks argues that voice agents are now practical because synthetic voices sound natural and can respond within roughly 200 milliseconds, but that most enterprise AI investment has focused inward on employee assistants rather than customer conversations. It describes a prototype in which LiveKit carries calls, Deepgram handles speech-to-text, ElevenLabs adds voices, and Databricks supplies the "brain" that looks up caller context, applies business rules, and records each decision, built on context, ontology, and governance. The post outlines three pillars—grounding answers in business data, fast and slow AI decision-making including the ai_decide capability, and a learning loop from recorded calls—and points to the open-source DIVA blueprint on GitHub and a suggested 90-day pilot.

  5. Allen Institute for AI Blog38

    How researchers are adapting olmOCR 2 for historical and archaeological archives

    Ai2 describes how researchers adapted its open olmOCR 2 model: historian Maurice Casey built ArtificeOCR to run olmOCR 2 locally and make his Tropy photo library of archival sources searchable, while TU Wien and Austrian Academy of Sciences researchers fine-tuned olmOCR 2 with LoRA on 57 annotated Carnuntum pottery records, lowering transcription error on 450 test records from 15.58% to 1.11% through their CENTURIA benchmark.

Oct 28Wed
  1. Hugging Face Blog78

    NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt

    NVIDIA released Nemotron 3.5 Transcribe, an open-weight, promptable model that handles transcription in 40 languages, translation of 31 to or from English, speaker attribution in 13 languages, and audio question answering in one checkpoint, running on a single NVIDIA GPU. It ranks first among open-weight models on the Hugging Face Open ASR Leaderboard for English at 5.33% average WER and posts the lowest diarization error rate on David AI's 21-language leaderboard. The release also includes Granary v2, an open data curation pipeline covering 463,062 hours across 48 languages, and NeMo Speech fine-tuning recipes that cut held-out WER by 45.8% relative to the best prompt in a medical case study.

    Why it matters: It documents concrete benchmark standings, an open data pipeline, and a reproducible fine-tuning recipe for teams evaluating or adapting open speech models.

  2. Browser Use Blog58

    Odysseys2: a long-horizon web agent benchmark

    Carnegie Mellon University and Browser Use released Odysseys2, a benchmark of 100 long-horizon tasks on the live web, each graded on 8 to 12 pass/fail checks. The best agent, GPT-6 Astra, fully completes 47 of 100 tasks with a mean rubric score of 84.6, while no other agent reaches 20 and five complete none. The authors note the LLM judge has not been validated against a human reference, reward-hacking flags are model judgments, and the live web changes.

  6. Hugging Face Blog78

    Build specialized multimodal models with LFM3

    Liquid AI introduces LFM3, a generation of Liquid Foundation Models in the 2B parameter range that places text, vision, audio, omni, and decision models on one shared backbone built from LFM3-1.7B-Base, which was pre-trained on 22T tokens and uses a tri-hybrid layer mix with a 256K context window. The release includes six 2B-class models, new in-house vision and audio encoders, and LiquidCodec, with every model fine-tunable on a single GPU and the text model fitting in about 1 GB at 4-bit precision. All LFM3 models are open-weight and available on Hugging Face, with fine-tuning notebooks and agent harness guides provided.

    Why it matters: The post is a detailed technical release covering architecture changes, benchmark comparisons, and runnable code for six models, useful for developers evaluating small multimodal models for on-device deployment.

  7. Hugging Face Blog58

    Can we trust LLMs to judge whether a research idea is novel?

    Ai2 released "Old Ideas, Novel Problems," a study with a benchmark, data, code, and an automated pipeline for testing LLM novelty judges, built from ICLR 2026 peer reviews to form Human-Only and Human+Generated evaluation sets. Across six judges (GPT-5.1, GPT-5.2, GPT-5.4, Claude Sonnet 4.5, Claude Opus 4.5, Claude Opus 4.6), small prompt changes shifted accuracy by more than 50 percentage points on AI-generated ideas, retrieval and extra reasoning did not consistently help, and two purpose-built novelty systems scored below a simple prompted Claude Opus 4.6 baseline.

Oct 27Tue
  1. Baseten Blog55

    BDN Sealed Volumes: publish once, mount anywhere

    Baseten released BDN Sealed Volumes, which let you publish read-only data such as model weights, LoRA adapters, indexes, and compiled engines once as an immutable, digest-addressed version and mount it in any deployment via config.yaml. Volumes are pushed or synced from Hugging Face, S3, GCS, Azure Blob, Cloudflare R2, CoreWeave buckets, or local directories, with chunk-level deduplication so updates upload only changed bytes, and mounts start in seconds without a download step. Existing weights-block deployments will be migrated automatically, and the feature requires Baseten CLI 1.1.0+ and Truss 0.18.30+ if applicable.

  2. AWS Machine Learning Blog34

    Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3

    This post updates a 2023 MLOps pipeline pattern for visual quality inspection to Amazon SageMaker Python SDK v3, replacing the deprecated SageMaker Edge Manager with SageMaker Neo for edge optimization. It describes a dual-branch SageMaker Pipeline that trains an anomaly detector and a defect classifier per camera, gates models on their exported ONNX artifacts, compiles them with Neo, and registers them with camera metadata, while a Step Functions and DynamoDB semaphore layer paces fleet training. The sample deploys via AWS CDK and a full synthetic-data run costs about $0.07 in compute.

Oct 24Sat
  1. AWS Machine Learning Blog22

    Orchestrating SageMaker AI endpoint auto scaling with AWS CDK

    Updating a SageMaker AI real-time endpoint configuration fails through CloudFormation or CDK because SageMaker AI requires the scalable target to be deregistered first, and manual deregistration leaves CloudFormation unable to track Application Auto Scaling resources. The post presents a reusable CDK construct that replaces the native scalable target and scaling policy resources with three custom resources—Deregister, Register Scalable Target, and Attach Scaling Policy—ordered with explicit dependencies and driven by a ConfigHash so endpoint and auto scaling changes deploy together. It includes a walkthrough using the sample-sagemaker-endpoint-autoscaling GitHub repository and guidance on integrating the construct into an existing stack.

Oct 23Fri
  1. AWS Machine Learning Blog58

    5 production challenges in multi-agent systems and how Deep Insight solved them

    AWS's Solutions Architecture team describes Deep Insight, an open-source multi-agent system (Coordinator, Planner, Supervisor, Coder, Validator, Reporter, Tracker) that turns a CSV file and a natural-language prompt into a validated DOCX analysis report with charts. The post, part one of three, maps five production challenges to a stack: Strands Agents SDK Graph and Agents-as-Tools patterns for execution flow, role-specific Claude models on Amazon Bedrock, Amazon Bedrock AgentCore for managed deployment, AWS Fargate plus Application Load Balancer for isolated code execution, and DynamoDB, Amazon SNS and Amazon Cognito for monitoring. It also cites LG Electronics' ChatInsight and TechRecon as reuse of the same architecture.

  2. Hugging Face Blog58

    OlmoEarth v1.3: State-of-the-art embeddings for the whole planet

    OlmoEarth v1.3 is a new satellite embedding model that reads a year of Sentinel-1, Sentinel-2 and Landsat time series to produce one 128-dimensional vector per 10-meter pixel, with the first 64 dimensions usable as a smaller embedding. Precomputed annual global embeddings are shared on Source Cooperative, and a browser-based OlmoEarth Embeddings Explorer lets users explore maps, find similar places, and train small classifiers without a GPU or sign-in. The 128-dimensional embeddings scored highest on 20 of 27 evaluations against AlphaEarth Foundations and Tessera v2, and the model took 53% of v1.2's computing time and 18% of v1's, with weights, pretraining data and code all open.

Oct 22Thu
  1. Hugging Face Blog38

    Ensemble forecasting with IBM Granite time-series models

    IBM introduces Granite Timeseries Ensemble, a recipe that combines four pre-trained Granite models (PatchTST-FM-r1, PatchTST-FM-r2, FlowState-r1.1, and TTM-r3) through one extensible forecasting interface to return median and quantile forecasts without retraining or fitted ensemble weights. The default Probabilistic Ensemble pools member quantiles with equal influence, while an adaptive IQR Ensemble weights members by forecast spread; on 97 GIFT-Eval configurations both ensembles beat all four individual members on aggregate MASE and average MASE Rank, with the Probabilistic Ensemble reaching 0.683 MASE versus 0.685 for the best single member. The authors note the MASE margin is small, that PatchTST-FM-r2 retains the lowest aggregate CRPS/MWQL, and that the recipe forecasts channels independently and loads four checkpoints, increasing inference time and memory.

  2. NVIDIA Blog40

    Pioneers Across Europe Deploy NVIDIA Earth-2 Open Models to Power Real-World Weather and Climate Prediction

    NVIDIA Earth-2 offers developers and researchers a family of open AI models and tools, running on NVIDIA computing, for global weather prediction, detailed regional forecasts and environmental research. StormScope EU, a regional short-term forecasting model trained on European weather and climate data, uses EUMETSAT satellite observations to predict cloud conditions across 16 Meteosat channels at 10-minute intervals, and is being shown in a live interactive demo at GTC Berlin built with Earthmover. Organizations including AXA, Vaisala Xweather, the University of Manchester, TotalEnergies and Space42 are adapting Earth-2 models such as FourCastNet 3, CorrDiff and PhysicsNeMo for cyclone risk, lightning-enhanced forecasting, U.K. air quality, solar forecasting and radar-based surface-height estimation.

Oct 21Wed
  1. Hugging Face Blog58

    Granite Speech 5.0: Transcribe, Translate, and Attribute more Accurately and Faster

    IBM released granite-speech-5.0-2b, a new speech-language model trained on 390,000 hours of public audio that transcribes English, French, German, Spanish, Portuguese, and Japanese, translates to and from English in those languages plus English to Italian and Mandarin, and supports language-ID prompting and keyword lists up to 2,000 terms. It also updated granite-speech-5.0-470m-turboctc, which adds multilingual transcription and native punctuation and capitalization and reaches 19,870 RTFx on the Open ASR Leaderboard (about 1.6x faster than at launch), and granite-speech-4.1-2b-plus, which cuts word diarization error rate and speaker-attributed WER by 7% each and long-form WER by 24%. All three ship under Apache 2.0 with support in Transformers, vLLM, llama.cpp, and mlx-audio.

  2. NVIDIA Blog73

    NVIDIA Open Models Advance Europe’s Sovereign Intelligence Across Industries

    NVIDIA is expanding its family of open models across language, robotics, biology and quantum computing, including the new Nemotron 3.5 Super VL and Nemotron 3.5 Bolt models, Nemotron Retriever models, the Kumo family for structured business data, Cosmos 3.5 world models, Alpamayo 2 Edge for autonomous vehicles, BioNeMo for healthcare and science, and Ising for quantum computing. The company says the open models give organizations a foundation they can customize with local data and deploy on infrastructure they control, with Nemotron built using data and expertise from the NVIDIA Nemotron Coalition including European partners Black Forest Labs, H-Company and Mistral. Availability details include Nemotron 3.5 Super VL on Hugging Face, ModelScope, OpenRouter and build.nvidia.com, with Nemotron 3.5 Bolt E4B 4GB expected Friday, Oct. 23.

    Why it matters: The piece is a broad product roundup that shows how NVIDIA is positioning open models as a sovereignty-friendly foundation for European enterprises and researchers.

Oct 20Tue
  1. Hugging Face Blog65

    Hugging Face ❤️ TorchTPU: TPUs, Natively

    TorchTPU adds TPU as a native PyTorch backend, letting Transformers, Diffusers and Kernels run on TPU by sending models to "tpu" instead of going through torch_xla or JAX, and Optimum TPU is no longer required. The post shows loading a model with device_map="tpu", running Diffusers pipelines with pipe.to("tpu") plus optional torch.compile, and using Pallas kernels from the Kernels Hub, with reported speedups of 21.5× on Qwen3-30B-A3B and 12.8× on Gemma 4 26B-A4B at batch size 64. It also reports that 96.5% of tests run on TPU pass, with 34 issues filed on the TorchTPU repo and 19 already fixed.

    Why it matters: It documents a new native PyTorch TPU backend and concrete usage patterns for the Hugging Face stack, useful for readers weighing TPU support.

Oct 15Thu
  1. Mistral AI67

    Introducing Timestral

    Mistral releases Timestral, its first family of time-series foundation models in four sizes from 5M to 209M parameters, with the largest ranking first on GIFT-Eval, TIME and fev-bench among pre-trained zero-shot models. Timestral produces probabilistic multivariate forecasts, supports past-only and known-future covariates, and runs on CPU without a GPU. It combines synthetic-first pre-training, covariate-aware attention and decoupled horizon forecasting, and can be fine-tuned on proprietary data through Mistral Forge.

    Why it matters: Useful for teams weighing time-series foundation models, since it reports benchmark standings, a size-versus-accuracy trade-off and CPU latency figures alongside the adaptation options.

Oct 8Thu
  1. Microsoft Research71

    Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

    Microsoft Research Asia introduces Harnessed Agentic RL, a paradigm in which the same agent harness used in deployment participates directly in reinforcement learning, and open-sources Agent Lightning v1.0, a roughly 3,500-line framework with an API Gateway, Rollout Controller, and Customized Trainer built on verl. Agents run as standard Kubernetes jobs without commercial sandbox services, and Collocated Async RL shares GPUs between rollout and updates, achieving about a 2x end-to-end speedup over synchronous RL. An end-to-end coding agent pipeline raised Qwen3.5-9B from 41.8% to 56.4% Pass@1 on SWE-bench Verified using about 6,000 training samples.

    Why it matters: It offers a concrete, reproducible recipe for training deployed agents in place, with code size, infrastructure, and benchmark numbers stated explicitly.

Jul 29Wed
  1. Berkeley AI Research60

    From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon

    IBM Research extended K-Search, an evolutionary LLM-driven kernel search framework from UC Berkeley Sky Lab, with a native MLX backend and a structured CUDA-to-MLX translation layer that maps CUDA primitives, constraints and optimization patterns onto Apple Silicon equivalents. On Apple Silicon the approach reached 0.97x the speed of the native MLX Attention kernel and up to a 20x prefill speedup over the community mlx-lm implementation on the Mamba SSM kernel, attributed largely to adding a parallel scan. The authors say the bottleneck was not the LLM's ability to write Metal code but the quality of context and constraints supplied to it.

    Why it matters: Describes a concrete method for transferring CUDA kernel expertise to non-NVIDIA hardware, with reported benchmark numbers and an open-source reproduction path.

Mar 13Fri
  1. Berkeley AI Research38

    Identifying Interactions at Scale for LLMs

    This blog post introduces SPEX and ProxySPEX, algorithms that use strategically selected ablations and sparse recovery from signal processing and coding theory to identify influential feature, data, and model-component interactions at scales far beyond prior methods, with ProxySPEX exploiting hierarchy to match SPEX using roughly 10x fewer ablations. The authors demonstrate the framework on sentiment analysis, a modified trolley problem with GPT-4o mini, CIFAR-10 data attribution with ResNet, and attention head pruning on MMLU, where a ProxySPEX-informed pruning strategy improved target-task performance. Code for both algorithms is available in the SHAP-IQ repository.

Jan 10Sat
  1. Berkeley AI Research34

    Information-Driven Design of Imaging Systems

    Researchers present a framework that estimates mutual information directly from noisy measurements using a known noise model and a learned model of measurement variation, enabling direct evaluation and optimization of imaging systems by information content. In their NeurIPS 2025 paper, information estimates predicted decoder performance across color photography, radio astronomy, lensless imaging, and microscopy, and their IDEAL method optimized encoder parameters via gradient ascent, matching end-to-end optimization on color filter design without requiring a decoder network. The authors suggest the approach could extend to other sensing domains modeled as deterministic encoding with known noise characteristics.