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November 13Friday
  1. NVIDIA Newsroom22

    How AI Is Helping Care Teams Get Patients the Right Care, Sooner

    Dyania Health's Synapsis AI clinical reasoning engine reads every note, report and lab result in a patient's record, connecting findings years apart to flag which patients need attention and why, with each signal traceable to its source. It runs on NVIDIA-accelerated computing inside a health system's own firewall, so patient data never leaves the hospital. Cleveland Clinic used it to review records of more than 7,800 aortic valve replacement patients from 2005 to 2021 and build a predictive model of valve deterioration and reintervention risk at three, five and ten years.

  2. Databricks Blog36

    Bright Machines uses Debezium and Iceberg to give customers live dashboards from air-gapped factories

    Bright Machines now gives customers live, governed dashboards of factory data from air-gapped plants without opening any inbound access. It captures PostgreSQL changes at the edge with Debezium, writes them as Apache Iceberg, pushes them out over one auditable outbound path, and reads them in place in Databricks via Unity Catalog federation. The first customer-visible dashboard went live about two months after kickoff, with committed rows queryable in one to two minutes and dashboards refreshing every ten minutes.

  3. Microsoft Research22

    Microsoft Research Focus Edition #98: AI Translation, KLS Conjecture Proof, and Faster Coding Agents

    Microsoft Research Focus Edition #98 highlights AI advances bringing universal translation closer to reality and a new proof resolving the 31-year-old KLS conjecture in high-dimensional geometry. The edition also covers CompactionBench and APEC, which cut coding-agent context compaction costs by up to 25.4% and execution time by 9.6%, plus the Latent Core Tokenizer, which improved tokenization across 104 languages.

  4. Databricks Blog30

    Databricks Debunks Four LTAP Myths About Lakebase Architecture

    Databricks responds to four common misreadings of LTAP, the Lakebase architecture that keeps one governed copy of operational data serving both Postgres and analytics without CDC. The post argues LTAP is not replication: layers in front of the lake are caches, not a second database with its own catalog. It also says heap pages and Parquet are two lossless encodings of the same rows, so point lookups and scans each read the layout fast for them.

  5. AWS Machine Learning Blog38

    Tokenomics at Scale: Tracking and Capping Amazon Bedrock Spend per IAM Principal with CUR 2.0 and AWS Budgets

    AWS shows how to track and cap Amazon Bedrock generative AI spend per IAM principal using only native features: Bedrock automatically records the IAM user or role on every inference request, and those tags become cost allocation tags readable in Cost Explorer or CUR 2.0 exports. An AWS Budget scoped to Bedrock and filtered to one identity or tag value can attach a budget action that applies a Deny IAM policy once a threshold is crossed, with no application code for the common case.

  6. Hugging Face Blog69

    IBM releases LLMON preview models built on Granite 4.1 3B and 8B

    IBM released two experimental preview models, granite-4.1-3b-LLMON-preview and granite-4.1-8b-LLMON-preview, derived from Granite 4.1 base checkpoints and trained to consume and generate LLMON, an LLM-native markup language that marks instructions, data and requirements with reserved special-token delimiters. On the OPI benchmark the models avoid 47% more indirect prompt injection attacks than the best frontier model tested and 95% more than the best open-source model, while performing on par with Granite 4.1 Instruct on standard benchmarks. The release includes a GitHub toolkit with lossless JSON-to-LLMON converters, and the authors note the preview is for exploration, not production.

    Why it matters: IBM trained a preview model on Granite 4.1 that can read and write LLMON tags, using explicit instruction-data separation to reduce the indirect prompt injection success rate on the OPI benchmark to near zero while maintaining general capabilities.

  7. Databricks Blog22

    How Constellation is building a governed data foundation on Databricks

    Constellation, the largest US nuclear fleet operator, is building a governed Enterprise Data Platform on Databricks to give the business one shared meaning for its data. The platform uses Unity Catalog for lineage, fine-grained access and a single permission model, plus Data Vault modeling for critical reusable entities. Early use cases include a nuclear engineering assistant built with Databricks Genie and outage planning.

  8. Databricks Blog25

    Eclipse Aerospace uses Databricks PySpark custom data sources for flight-data ingestion

    Eclipse Aerospace is using Databricks PySpark custom data sources to ingest nearly 20 years of proprietary flight data, over 3TB spanning more than a million flight hours, without always-on dedicated compute. The Rust-backed Python package delivered twice the benchmarked performance of a similar PyArrow-based pure-Python data source and nearly 20x that of a native Python implementation without Arrow. The API, introduced in Apache Spark 4.0 and available in Databricks Runtime 15.4 LTS, replaces the prior Lambda-to-Avro pipeline.

  9. Databricks Blog22

    How H6.ai builds AI-powered financial automation with Lakebase Postgres

    H6.ai automates accounts payable on top of customers' existing ERPs, using a dedicated Lakebase Postgres database per customer as its system of record. Because invoice data keeps changing, the team uses Lakebase copy-on-write branches to freeze snapshots of customer databases and run each regression test on its own short-lived branch, so the live database is never written to. Each run is judged by deterministic assertions plus a panel of AI judges from different model providers.

  10. Databricks Blog58

    Databricks launches Lakehouse//RT to serve real-time analytics without proportional cost growth

    Databricks introduced Lakehouse//RT, a real-time analytics engine that runs high-concurrency workloads directly on lakehouse data and is now available in beta. The company says it delivers sub-100 millisecond latency at 12,000 queries per second, adds capacity one node at a time, and avoids a separate serving layer; customers Aurasell, VisionHeight and nOps report 6x to 10x lower infrastructure costs after migrating from multi-cluster setups, Apache Pinot and Apache Druid.

November 12Thursday
  1. Meta Newsroom · AI18

    Meta Signs Long-Term Purchase Agreements for Lower Carbon Cement, Steel, Trucking and Recycled Plastics

    Meta has signed a set of long-term purchase commitments covering cement, steel, trucking and recycled plastics to bring clean manufacturing to commercial scale in its data center supply chains. The deals include at least 180,000 tons of lower carbon cement from Sublime Systems, near-zero-emission steel from Sweden-based Stegra, an expanded zero-emission freight route with Nevoya along I-10 in California and Arizona, and 5,000 tons per year of recycled PET with MacroCycle Technologies.

  2. Hugging Face Blog36

    Hugging Face Releases Fine-Tuning Guide and Scripts for TRELLIS.2 3D Generation

    Hugging Face published fine-tuning scripts and containers for TRELLIS.2, a 3D generation model, runnable locally or on Hugging Face Jobs. The guide uses LoRA adapters via the PEFT library on only the sparse-structure flow DiT and shape flow DiT, leaving the material stage frozen. On a 188-object footwear subset of the GSO dataset, the pretrained model often rendered incorrect soles, which the LoRA fine-tune targets.

  3. AWS Machine Learning Blog46

    Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod

    A PyTorch DDP training loop on Amazon SageMaker HyperPod gains three checks that catch silent data corruption: a non-finite gradient check, a ring replay audit comparing loss bits and gradient fingerprints across nodes, and a weight fingerprint comparison after the optimizer step. Tested with software-injected faults on 4 ml.p5.48xlarge instances (32 NVIDIA H100 GPUs), the checks name a suspect rank, map it to a node, and let HyperPod replace the node and resume from a checkpoint.

  4. AWS Machine Learning Blog12

    AWS re:Invent 2026 AI/ML Track: Attendee's Guide

    AWS will hold its annual re:Invent conference from November 30 to December 4, with the AI/ML track offering more than 140 unique sessions out of over 2,000 total learning sessions. The guide maps sessions to the AWS AI stack, spanning AI chips (AWS Trainium and NVIDIA GPUs), models and inference on Amazon Bedrock and SageMaker AI, and agent development with Amazon Bedrock AgentCore. Sessions are organized from newcomer content to technical deep dives, and many can be viewed on demand.

  5. Google Research58

    Google Research uses wearables to build CKM risk profiles and personalized coaching

    Google Research introduced an investigational framework that turns consumer wearable data into a coachable health age and simulates personalized 12-week what-if health trajectories. The SensorWM world model was pretrained on 2.28 million person-weeks of de-identified Fitbit data from roughly 100,000 users and forecasts 20 physiological markers under different step, sleep and cardio routines. In a blinded study, lifestyle coaches chose the counterfactual-grounded plan as best overall in 71.4% of evaluations, and physicians rated 99.2% of plans within safe exercise intensity limits; the authors note the models are research prototypes, not clinical diagnostic tools.

  6. Meta Engineering Blog40

    Meta Intelligent Delivery: ML System Allocating Compute for Ads Ranking

    Meta detailed Intelligent Delivery, an ML-driven system that allocates compute across its ads serving stack toward the system-wide optimum, contributing roughly 10% incremental ranking performance on top of model and system advancements from 2017 through the first half of 2026. Its Resource Core Model uses 6B sparse and 20M dense parameters to predict each request's marginal utility, and Meta says it improved identification of high-impact ad requests by about 20% in offline benchmarks. The system also relies on multi-layered allocation mechanisms and increasingly agentic AI to adapt as models and hardware change.

November 11Wednesday
  1. Google DeepMind72

    DeepMind releases AlphaGenome 2, a DNA model predicting variant effects in specific cell types

    Google DeepMind announced AlphaGenome 2, a next-generation DNA sequence model that predicts how DNA sequences and their variants affect specific human cell types. Trained on more than 107 million individual cell profiles across 68 tissues, the conditional model identified nearly twice as many expression-changing variants as the previous model, and on the TenK10K immune cell dataset it recovered 30% of causal variants versus 16% for the original AlphaGenome fine-tuned on single-cell data. It is available for non-commercial research via the AlphaGenome API and in private preview for commercial research.

    Why it matters: AlphaGenome 2 shifts from bulk-tissue averages to single-cell conditioning, roughly doubling causal variant recall on immune cell data, which matters for linking GWAS signals to specific cell types.

  2. Hugging Face Blog41

    Pooling-Aware Training for Multi-Vector Embedding Models with Sentence Transformers

    Hugging Face shows how pooling-aware training lifts retrieval quality for multi-vector embedding models at a fixed storage budget. With pool_factor=4, a normally trained model keeps 94.6% of its unpooled NanoBEIR nDCG@10 while cutting document vectors 4x, and pooling-aware training raises that retention to 97.8%. The post walks through HierarchicalTokenPooling, budget calibration and a reproducible Sentence Transformers example.

  3. inclusionAI Models40

    inclusionAI Releases ZoomGain-9B and ZoomGain-4B Models with ZoomGainBench and RL Training Data

    inclusionAI released the ZoomGain-9B and ZoomGain-4B vision-language model checkpoints, alongside the ZoomGainBench evaluation benchmark and ZoomGain RL training data. ZoomGain mines global-to-zoom perception gaps to build fine-grained visual supervision, and ZoomGain-9B reached superior performance on multiple fine-grained and general perception benchmarks. ZoomGainBench contains 1,000 questions over 941 images, while the RL dataset holds 9,000 training records and 7,713 source images.

  4. inclusionAI Models38

    inclusionAI Releases ZoomGain-4B and ZoomGain-9B with ZoomGainBench and RL Training Data

    inclusionAI released the arXiv paper, ZoomGainBench evaluation benchmark, RL training dataset, and ZoomGain-4B/ZoomGain-9B model checkpoints. The best of 28 models scored 61.08 on ZoomGainBench, while ZoomGain-9B reached superior performance on multiple fine-grained and general perception benchmarks. ZoomGain mines global-to-zoom perception gaps to build fine-grained visual supervision for vision-language models.

  5. Microsoft Research75

    Microsoft researchers describe Lazarus, a backdoor that reprograms dormant neurons in AI agents

    Microsoft researchers have described Lazarus, a backdoor technique that reprograms fewer than 10 dormant neurons in an LLM so an agent built on it emits attacker commands when a hidden trigger appears, with no retraining required. The modified model still passes standard evaluations and behaves normally until the trigger, which can be as simple as a date change, and the agent then runs the downloaded code with whatever access it was given. The authors say detection requires diffing against the original weights, and Microsoft Foundry already ties open-weight models to their publishers and scans their containers, while red-teaming harnesses, agent-based weight scanning and signed models with tamper-evident provenance are in development.

    Why it matters: Microsoft researchers show that reprogramming fewer than 10 dormant neurons can backdoor an LLM agent without retraining, which makes weight provenance and integrity a practical security requirement.

November 10Tuesday
  1. Amazon Science62

    Amazon releases Mitra-v2 tabular foundation model with scaled synthetic pretraining

    Amazon introduced Mitra-v2, a second-generation tabular foundation model for classification and regression, released with fine-tuned models and inference and fine-tuning code under the Apache-2.0 license on Hugging Face. It is pretrained on synthetic tables with 10 times as many rows and roughly three times as many columns as Mitra-v1, using a new hybrid SCM prior and hybrid optimizer while keeping the Transformer architecture unchanged. On TabArena, a public benchmark of 51 real-world datasets, Mitra-v2 leads the single-model field, beats Mitra-v1 on 32 of the 33 datasets both run natively, and finishes all 51 within the one-hour budget; fine-tuned Mitra-v2 also ranks first on ScoringBench.

    Why it matters: Mitra-v2 shows that widening synthetic pretraining data, rather than deepening the network, lifts a tabular foundation model to the top of TabArena's single-model field.

  2. Apple Machine Learning Research34

    Weighted State Souping for Efficient Long-Context Retrieval

    Apple researchers and Harvard collaborators propose weighted state souping, a method that learns query-dependent weights for recombining cached SSM state encodings, improving long-context performance on single- and multi-hop document QA, needle-in-a-haystack retrieval, variable tracking, and tool use. A sparse variant achieves near-constant encoding cost with respect to context length while preserving or sometimes improving performance. The paper was accepted at the PALM workshop at NeurIPS 2026.

  3. Apple Machine Learning Research38

    Apple Study: Gating Mechanism Explains Why SSMs Memorize Instead of Learning In-Context

    An Apple and Harvard study shows that the gating mechanism in State Space Models (SSMs) causes them to first learn an in-weights "memorization" solution, delaying or even preventing convergence to correct in-context learning. This occurs even when the architecture and memory capacity impose no fundamental limits. Gating, however, often improves generalization to long sequence lengths.

  4. Apple Machine Learning Research31

    Apple study evaluates LLM judges for personalized writing style assessment

    Apple researchers tested how well LLM judges assess whether generated text matches a user's preferred writing style, generating text with four LLMs across three long-form writing tasks and comparing judge annotations against human pairwise labels. Judge quality correlated only weakly with general LLM ability on MMLU (Pearson's ρ ≤ 0.2) and varied by writing task and evaluation criteria, and paired comparisons proved more consistent than independent scoring. The strongest LLMs did not most reliably reproduce human evaluations, possibly due to exaggerated sensitivity to subtle details.

  5. Google Research25

    Google's Language Explorer Visualizes the World's Linguistic Diversity

    Google has launched Language Explorer, an interactive web platform that lets researchers, educators, and language enthusiasts discover and visualize linguistic diversity across thousands of languages. Built as part of Google's 1,000 Languages Initiative and based on the open-source LinguaMeta dataset, it is featured in a new exhibition at the Museum of Communication Berlin opening this week. Google Translate now supports nearly 250 languages, while Gboard covers 900+ language varieties.

  6. Goodfire Research62

    Goodfire studies Qwen3.6-35B-A3B neurons as an interpretable basis

    Goodfire Research published a study arguing that individual MLP neurons in Qwen3.6-35B-A3B are more interpretable than assumed, based on sparsification, attribution tools and research agents. Ablating the 92.2% weakest MoE neuron activations per forward pass raised language model loss by only 2.9%, and 69% of neuron poles had a single mode responsible for at least 90% of their causal effect. Natural-language neuron descriptions let a judge model predict firing with 95% accuracy against random text and recover 73% of ablation damage through simulation.

    Why it matters: Goodfire's study of Qwen3.6-35B-A3B neurons argues that strong activations alone carry most of the model's computation, offering a concrete alternative to feature-dictionary interpretability.

November 9Monday
  1. Hugging Face Blog38

    NVIDIA and Elith Release Nemotron-Personas-Finance-Japan Synthetic Dataset

    Elith and NVIDIA released Nemotron-Personas-Finance-Japan, an open synthetic persona dataset for Japan's financial sector built on official government statistics. It contains 9M personas (1M records × 9 personas) across 50 fields and ~1.5B tokens, generated with NeMo SDG-PGMs and NeMo Data Designer, and is available under CC BY 4.0 for commercial use.

  2. inclusionAI Repositories38

    ZoomGain: Mining Global-to-Zoom Perception Gaps for Fine-Grained Visual Supervision

    ZoomGain constructs questions about a full image from visual details that only become clear when zooming in, using this global-to-zoom perception gap as supervision for vision-language models. The release includes the arXiv paper, the ZoomGainBench benchmark (1,000 questions over 941 images covering attribute recognition, counting, and OCR), 9,000 RL training records over 7,713 source images, and the ZoomGain-4B and ZoomGain-9B checkpoints. The best of 28 models scored 61.08 on ZoomGainBench, while ZoomGain-9B reached superior performance on multiple fine-grained and general perception benchmarks.

  3. Preferred Networks News26

    PFN's AI Writing Instruction System Ronjiro Wins AI Special Award (Learning Support) at 23rd Japan e-Learning Grand Prize

    Preferred Networks' AI writing instruction system Ronjiro has won the AI Special Award (Learning Support) at the 23rd Japan e-Learning Grand Prize. Ronjiro uses generative AI including PFN's domestically developed PLaMo foundation model to support high school students' essay and composition learning, providing feedback on logical structure and improvement points in as little as one minute. It has been offered since March 2026 through YP Switch, a joint venture with the Yarakis Switch Group, and is now being introduced in some School IE classrooms nationwide.

November 6Friday
  1. Hugging Face Blog58

    Ai2 releases ToyBlocks, an open simulated block-manipulation world for robot learning

    Ai2 released ToyBlocks, an open simulation framework built around a robot arm and blocks on a tabletop, as the first of its FoundationWorlds for controlled robot learning and evaluation. The framework auto-generates skills and demonstrations via RL and motion planning in Isaac Lab, reaching up to 24x real-time on a single GPU, and ships with 85,336 long-horizon demonstrations covering nearly 1,600 hours and 144 million frames. In its experiments, ManiFlow's atomic-skill success was 80.0% in simulation and 81.1% on a real Franka FR3, while the Molmo 2 plus ManiFlow system succeeded in 55.5% of simulated and 50.0% of real-world episodes.

  2. vLLM Blog77

    REINFORCE Zero: Kernel Alignment Enables Stable Async RL at Policy Lag 64, Extends to DeepSeek-V4.1 and Kimi K3.1

    NVIDIA Research and Inferact's REINFORCE Zero paper shows that aligning rollout and trainer kernels lets unclipped token-level importance sampling correct the remaining weight lag, training stably for 20,000 updates at policy lag 64 and reaching 2.40x the training-step throughput of synchronous training with aligned kernels. With standard kernels, the median rollout-to-learner KL rises 83.33x across the run and online AIME24 pass@1 falls from 34.27% to 13.96%, while a sequence gate stabilizes training but rejects 24.48% of sequences by the last 1,000 updates. The zero-mismatch setup now covers DeepSeek-V4.1-Flash, Kimi K3.1 on release day, and DeepSeek-V4-Flash and Kimi K3.1 on AMD MI355X, with code in Inferact/zero-mismatch-rl and NVIDIA-NeMo/labs-molt.

    Why it matters: NVIDIA Research and Inferact split the async RL rollout-learner gap into weight lag and kernel mismatch, showing that aligning kernels lets unclipped importance sampling hold stable training at policy lag 64.

  3. vLLM Blog55

    VeRL-Omni v0.3.0 released with async training and broader multimodal post-training

    VeRL-Omni v0.3.0 was released with asynchronous and decoupled RL execution paths, the V1 trainer as the default, and expanded post-training coverage across image, video, audio, and multimodal understanding. The release adds fully async Qwen3-Omni text and audio training, separate-async Omni and diffusion training with a replay buffer, and new recipes including MiniMax H3 with DiffusionNFT and MiniCPM-o 4.5 GSPO. In a MiniCPM-o 4.5 AVQA experiment on 4×H800 80 GB, separate-async 3+1 was about 9% slower per prompt than colocated 4+0 at natural response length, roughly tied at 512 tokens, and about 6.7% faster at 1024 tokens.

  4. Anthropic Research60

    Anthropic explains the components of Claude agent memory

    Anthropic outlined how it designs memory for Claude agents, defining agent memory as knowledge retained across sessions and separating it from weight changes, in-context learning and compaction. 5 matched it with memory. 7 wrote 413 files across 225 folders and earned one badge.

    Why it matters: Anthropic details how Claude's agent memory works, comparing models' note-taking on CL-Bench and Pokémon Red and showing how offline dreaming corrects and organizes stored notes.

November 5Thursday
  1. Amazon Science18

    Amazon Scientist Hamed Karimi Wins 2026 ECML PKDD Test-of-Time Award

    Amazon senior applied scientist Hamed Karimi and coauthors Julie Nutini and Mark Schmidt received the 2026 ECML PKDD Test-of-Time Award for their 2016 paper on linear convergence of gradient and proximal-gradient methods under the Polyak–Łojasiewicz condition. The paper showed that this 1963 inequality guarantees linear convergence even when models lack a single best solution, unifying scattered optimization results and extending them to nonsmooth landscapes and algorithms such as coordinate, sign, and stochastic gradient descent. It has been cited over 2,000 times and appears in graduate textbooks.

  2. inclusionAI Repositories38

    ASpec: A High-Throughput Training Framework for Speculative Decoding Draft Models

    ASpec is a high-throughput training framework for speculative decoding draft models, built from the TorchSpec codebase with optimized attention, fused kernels and selective head/loss computation. On a Qwen3-8B 4K run, Draft-only training reaches 15,256.44 input tokens/s on one GPU, 1.96× TorchSpec, while the 8-GPU online steady window hits 77,721.37 tokens/s, 2.28× TorchSpec. It supports DSpark, DFlash, EAGLE3/4 and native MTP drafts, with Qwen3-8B and Ling tiny quickstarts.

  3. Apple Machine Learning Research38

    Apple's Learned Support Functions Speed Up RAG Retrieval

    Apple researchers presented "Amortizing Maximum Inner Product Search with Learned Support Functions" at ICML 2026, training small neural networks offline to route queries toward their best-matching keys faster. SupportNet learns the support function via input convex neural networks (ICNNs) to improve cluster routing, while KeyNet directly outputs the best-matching key vector, kept geometrically grounded by a consistency loss from Euler's theorem. Both are trained on the actual query distribution, trading an upfront amortization cost for faster, more accurate retrieval at inference time.

  4. Google Research47

    Google Research's Agile Deliberation helps humans define subjective AI categories

    Google Research introduced Agile Deliberation, a framework that turns subjective AI classifier creation into an iterative conversation where the AI helps users sharpen their own definitions. It splits the task into concept scoping and concept iteration, using vision-language models to surface borderline images and route feedback into an automatic prompt optimization loop. In 18 live 1.5-hour user sessions, the approach reduced user fatigue and anxiety and produced more consistent models that outperform standard baselines; the code is open-sourced on GitHub.

  5. Anthropic Research58

    Anthropic studies how researchers use Claude across scientific fields and tasks

    Anthropic analyzed 207,049 Claude and Claude Code research sessions from August 2 to September 30, 2026, using privacy-preserving classifiers, and found scientists use Claude most for data analysis and writing and least for data gathering and ideation. Data analysis accounts for 33.7% of Claude sessions and 48.1% of Claude Code sessions, against 27.6% of researcher time, while ideation takes 4.2% and 2.1% of sessions versus 7.1% of researcher time. The report estimates that wide adoption of current capabilities could roughly double research output in some fields, and full automation of non-physical work could lift fields such as computer science and statistics as much as 15-fold.

  6. Goodfire Research58

    Goodfire Research proposes mechanistic shaping to steer models via internal representations

    Goodfire Research proposes mechanistic shaping, a framework that adds penalties on a model's internal concept directions during training so it learns to rely on or express specific concepts rather than just maximize task reward. In chest X-ray experiments, an LLM agent used sparse autoencoder features to find tube-like shortcut directions, and reliance shaping beat both standard training and a balanced-data baseline.