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Nov 14Sat
  1. AWS Machine Learning Blog24

    Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1

    GS Caltex connects models built by process engineers in Amazon SageMaker Canvas to production workflows in Amazon SageMaker Pipelines, with ML engineers handling deployment and scheduled inference. The company automated the handoff so ML engineers now deploy models within a few hours instead of about a week, and it runs batch inference every 30 minutes, which it reports has meaningfully lowered inference costs. The post details the reference architecture, including reusing Data Wrangler preprocessing flows, registering and approving models in Model Registry, and scheduling runs with Amazon EventBridge.

  2. AWS Machine Learning Blog36

    OpenFold3 on Amazon SageMaker JumpStart: Predict biomolecular structures

    Amazon SageMaker JumpStart now lets you deploy OpenFold3-preview, an open source all-atom biomolecular structure prediction model from the OpenFold Consortium and Columbia University's AlQuraishi Lab, to predict 3D structures of proteins, DNA, RNA, small-molecule ligands and their complexes. The post walks through deploying it on an asynchronous inference endpoint, submitting prediction jobs, interpreting mmCIF outputs with pLDDT, PAE and PDE confidence metrics, running parallel predictions on multi-GPU instances, supplying your own MSAs, and scaling the endpoint to zero when idle.

  3. AWS Machine Learning Blog51

    Build a model improvement flywheel with Amazon SageMaker HyperPod Data Capture

    Amazon SageMaker HyperPod now offers Data Capture, which records live inference inputs and outputs to Amazon S3 at three independent tiers (SageMaker endpoint, load balancer, and model pod) with minimal latency impact and no impact on model availability. The post walks through a single-cluster example in which a home-appliance support router's production traffic is captured at the model-pod tier, analyzed with Amazon Athena to surface a new-product blind spot, labeled with a SQL policy, and used to fine-tune the router on a second GPU. Accuracy is measured before and after on data that could not have existed before deployment, with scripts, manifests, SQL, and synthetic datasets in an accompanying GitHub repository.

  4. Hugging Face Blog40

    XYBench: Helping with the problem behind the question

    XYBench is a new benchmark of 8,115 real-world questions, drawn from Stack Overflow, Stack Exchange, curated examples, and WikiHow, that tests whether AI assistants recognize when a user's request rests on a misconception (the "XY problem") and redirect them toward their actual goal. Evaluating Claude Sonnet 4.6, GPT-5, Gemini 3.1 Pro, Qwen3.5-9B, and DeepSeek-V4 Pro, the authors found every model was more likely to address the stated request than the underlying goal, and all trailed human reference answers on misconception identification. The paper won the Best Resource Paper Award at EMNLP 2026.

  5. Allen Institute for AI Blog44

    XYBench: Helping with the problem behind the question

    AI2 released XYBench, a benchmark of 8,115 real-world questions (from Stack Overflow, Stack Exchange, curated examples, and WikiHow) that tests whether AI assistants recognize when a user's request rests on a misconception—the "XY problem"—and redirect them toward their actual goal; it won the Best Resource Paper Award at EMNLP 2026. Evaluating Claude Sonnet 4.6, GPT-5, Gemini 3.1 Pro, Qwen3.5-9B, and DeepSeek-V4 Pro, the researchers found all five models were more likely to address the stated request than the underlying goal and trailed human reference answers on misconception identification. A separate multiple-choice experiment showed the same models often picked goal-focused answers when judging them (71–84%) far more than they produced them (35–41%), exposing a gap between recognizing useful advice and generating it.

Nov 13Fri
  1. NVIDIA Newsroom22

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

    NVIDIA describes how healthcare systems and startups use AI to reduce routine chart work, citing Dyania Health's Synapsis AI, a clinical reasoning engine that runs on NVIDIA-accelerated computing inside a hospital's firewall and surfaces source-traceable clinical signals for clinician review. Cleveland Clinic used it to support a study of more than 7,800 aortic valve replacement patients from 2005 to 2021, building a predictive model of valve deterioration and reintervention risk at three, five and ten years.

  2. Databricks Blog22

    Dispelling LTAP myths

    This post rebuts four common misconceptions about LTAP: that it is CDC replication, that a single copy forces slow lookups or scans, that open table formats mean eventual consistency, and that Postgres logical replication suffices. It argues Lakebase keeps one logical table with the lake as its durable tier and caches in front of it, so no layer adds a second catalog or source of truth. Each myth is traced to the same misunderstanding of treating every materialization of the data as a second database.

  3. AWS Machine Learning Blog36

    Tokenomics at scale: Track and cap Amazon Bedrock spend per IAM principal with CUR 2.0 and AWS Budgets

    This AWS post describes how to get per-identity Amazon Bedrock cost visibility and enforceable spend limits using only AWS-native features: IAM principal and session tags for attribution, Cost Explorer or CUR 2.0 exports (with the caller identity option) for reporting, and AWS Budgets with budget actions that attach a Deny IAM policy when a threshold is crossed. It notes that budget data refreshes up to three times a day, cost allocation tags are not retroactive, and a Deny action on a shared role blocks everyone using it.

  4. Databricks Blog28

    Eclipse Aerospace simplifies flight-data ingestion with Databricks

    Eclipse Aerospace worked with Databricks to replace a multi-stage AWS Lambda and Avro pipeline with an Arrow-native PySpark custom data source that wraps its existing Rust flight-data decoder, reading proprietary telemetry files directly in Spark on serverless compute. The approach, enabled by the Python Data Source API available from Databricks Runtime 15.4 LTS, cut latency, removed duplicate Avro storage and a Lambda tier, and brought decoding into the Spark UI and job metrics. Eclipse reports the connector can process records within a minute of upload and commonly handle multi-gigabyte batches in parallel in under two minutes.

  5. 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 data consistent shared meaning across domains, choosing Databricks partly because it interoperates with existing architecture rather than requiring a rip-and-replace. The platform rests on conformed semantic definitions, a semantic knowledge layer with selective Data Vault modeling, and Unity Catalog for lineage, fine-grained access and ownership of meaning, with early use cases including a nuclear engineering assistant built with Databricks Genie, outage planning, and sales intelligence.

  6. Hugging Face Blog66

    LLMON: An LLM-Native Markup Language, now in Granite 4.1 3B and 8B

    IBM researchers introduce LLMON (LLM Object Notation), an XML-like markup language that uses reserved special-token delimiters to explicitly mark instructions, data, requirements and execution bindings in an LLM's context, addressing the brittleness of JSON-style formats for causal models. They release two experimental preview models derived from Granite 4.1 base checkpoints (granite-4.1-3b-LLMON-preview and granite-4.1-8b-LLMON-preview) plus a GitHub toolkit, reporting that on the OPI benchmark LLMON models avoid 47% more indirect prompt injection attacks than the best frontier model tested while maintaining general capability on par with Granite 4.1 Instruct. The models ship with their own chat template and lossless JSON-to-LLMON converters, and the post notes the previews are for exploration, not production.

    Why it matters: Useful for teams weighing structured context formats against JSON and for anyone evaluating prompt-injection defenses in small open models.

  7. AWS Machine Learning Blog34

    Optimizing cost per task and proving agent ROI on Amazon Bedrock

    This AWS post argues that model cost should be measured per completed task rather than per token, citing four hidden cost drivers—tokenizer efficiency, output verbosity, agentic loop efficiency, and reasoning overhead—and an illustrative example where a model with double the list price costs 65% less per task. It describes levers for reducing cost per task (prompt caching, intelligent prompt routing, output constraints, batch inference) and Bedrock features such as CountTokens, the usage block, and invocation logging for measuring token and turn counts. For ROI, it recommends documenting and measuring a human baseline before launch, instrumenting workflows as experiments with run records, joining cost to outcomes via Athena, and using a five-dimension scorecard with green/yellow/red thresholds.

  8. Databricks Blog58

    Lakehouse//RT: Scale real-time analytics, not your bill

    Databricks introduces Lakehouse//RT, a real-time analytics engine built to serve high-concurrency, low-latency workloads directly on governed lakehouse data without costs scaling proportionally with query volume. It claims sub-100 millisecond latency at 12,000 queries per second, node-level autoscaling, and elimination of separate serving layers like Druid or Pinot, with cited customers reporting 6x to 10x lower infrastructure costs. Lakehouse//RT is available in beta, with a 30% discount through January 2027.

  9. PyTorch Blog38

    Fast by Default: Compile-Time Heuristics for Helion Kernels

    Helion now uses compile-time heuristics that analyze a kernel's IR and dataflow to classify it as matmul, reduction, or pointwise and emit block sizes, warps, and stages without autotuning. On H100, the computed configs reach 1.18x hand-written FLA Triton for linear attention, 1.27x vLLM's handwritten CUDA reduction kernels, and 1.09x torch.compile max-autotune on a general reduction suite. The heuristics are merged into Helion and enabled by default on H100 and B200 with the Triton backend.

Nov 12Thu
  1. Hugging Face Blog38

    The Definitive Fine-Tuning Guide for 3D Generation Models

    This blog provides fine-tuning scripts and Docker containers for adapting TRELLIS.2 to custom 3D assets, runnable locally or on Hugging Face Jobs. Using LoRA adapters on only the sparse-structure and shape flow DiTs, trained on 188 GSO footwear objects, the authors report the mean F-score on nine held-out objects rising from 0.276 to 0.803, Chamfer distance falling from 0.174 to 0.033, and normal consistency rising from 0.530 to 0.804. The texture flow DiT is frozen because the dataset lacks the separate material maps that stage requires.

  2. PyTorch Blog58

    Training a 1T-Parameter MoE on EU public infrastructure with PyTorch and TorchTitan

    The EUROPA consortium (Domyn, Fraunhofer IAIS and IIS) describes scaling pretraining of a ~951.9B-parameter MoE model (32.8B active per token) on JUPITER, the EuroHPC exascale supercomputer at Forschungszentrum Jülich, raising throughput from 90 to ~1,300 tokens/sec per GPU across 384 NVIDIA GH200 GPUs per DP replica. Key optimizations built on PyTorch and TorchTitan include FlexShard placement for the Muon optimizer, activation-checkpoint offloading to the Grace CPU, and running FSDP's all-gather setup on the compute stream via unshard_async_op, freeing ~14 GiB of HBM per GPU. The fixes were upstreamed to PyTorch and TorchTitan, with training reaching ~1,300 tokens/s per GPU on 1,536 GPUs across 4 DDP replicas.

  3. MIT News AI38

    New research could lead to faster, trustworthy AI models for climate prediction

    MIT researchers developed a training technique that generates optimized synthetic climate scenarios, letting a climate emulator be trained with far less data and only one full earth system model run instead of six to eight. The resulting emulator accurately predicted scenarios it had not seen during training, while traditional emulators failed to adapt, and it captured effects such as local pollutant controls amid rising other emissions. The work was published in the Proceedings of the National Academy of Sciences.

  4. OpenRouter Announcements58

    Self-Hosted LLM vs API: When Does Self-Hosting Pay Off

    At October 8, 2026 prices, a 27B model on one rented RTX 3090 breaks even against API pricing at roughly 6–12% load, while DeepSeek V4.1 Flash on four rented H200s never beats the cheapest zero-data-retention API endpoint. The post computes break-even load as GPU hourly rent divided by the API cost of the same hour of tokens, using live per-provider prices and benchmark throughput, and notes that published self-hosting costs near a cent per million tokens mostly count cached input tokens that are also nearly free via API. It also covers privacy options such as provider.zdr and EU/US in-region domains, and suggests self-hosting a steady base load while sending peaks to the API.

  5. AWS Machine Learning Blog46

    Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod

    This post describes adding three checks to a PyTorch DDP training loop to detect silent data corruption (SDC), where hardware such as a GPU returns a wrong but plausible number without raising an error: a non-finite gradient check, a ring replay audit that recomputes another node's micro-batch and compares loss bits and gradient fingerprints, and a weight fingerprint comparison after the optimizer step. When a check fires, the suspect rank is mapped to its node via the Kubernetes node name, the node is cordoned, and SageMaker HyperPod replaces it, with training resuming from a checkpoint saved before the suspect window. The checks were tested with software-injected faults on a HyperPod cluster with 4 ml.p5.48xlarge instances (32 NVIDIA H100 GPUs) and repeated on three instances, with full code in an accompanying GitHub repository.

  6. Databricks Blog28

    Detecting credential-driven attacks on Databricks

    Databricks describes how credential-driven attacks unfold on its platform in six non-linear stages—initial access with valid credentials, environment mapping, credential harvesting, execution via UDFs and jobs, persistence, and data collection with cleanup—and what to look for in verbose audit logs at each stage. The post explains how to investigate these attacks with Genie and the Detection App, how to respond once a compromise is confirmed, and which preventive controls such as context-based ingress, least privilege, and credential rotation limit what a stolen credential can do.

  7. AWS Machine Learning Blog12

    The attendee’s guide to the AWS re:Invent 2026 AI/ML track

    AWS will hold re:Invent from November 30 to December 4, offering over 2,000 learning sessions, with more than 140 in the AI/ML track spanning breakouts, chalk talks, builder sessions, workshops, code talks, and lightning talks. The guide organizes AI/ML sessions around the AWS AI stack—global infrastructure, AI chips (Trainium and NVIDIA GPUs), data foundation, models/training/inferencing (Amazon Bedrock and SageMaker AI), agent development and management (AgentCore), and agentic solutions—plus sessions on trust in agents, sustainability, and Local Zones and AI Factories. Breakout sessions are recorded and posted to the AWS Events YouTube channel a few days later, while other session types are live and not recorded.

  8. Google Research58

    Using wearables to enable cardiovascular-kidney-metabolic (CKM) risk profiling for personalized coaching

    Google researchers describe an investigational framework that builds a "coachable health age" from wearable signals such as resting heart rate, steps, sleep duration and Active Zone Minutes, plus a Sensor World Model trained on over 2.2 million person-weeks of Fitbit data to simulate 12-week "what-if" habit trajectories. In blinded evaluations, lifestyle coaches preferred counterfactual-grounded plans in 71.4% of cases, and physicians rated 99.2% of those plans within safe exercise intensity limits. A companion study extends the approach to multi-organ CKM health by combining wearables with routine blood biomarkers including cholesterol-to-HDL ratio, eGFR and HOMA-IR.

  9. Apple Machine Learning Research34

    Weighted State Souping for Efficient Long-Context Retrieval

    The paper proposes weighted state souping, a method that learns query-dependent weights for composing cached state encodings from an SSM, addressing the degradation of state souping and vanilla SSMs on long, crowded contexts. The authors report substantial long-context improvements across single- and multi-hop document QA, needle-in-a-haystack retrieval, variable tracking, and tool use, plus a sparse variant with near-constant encoding cost relative to context length. They also present theoretical arguments on why souping works and when it may fail.

  10. Apple Machine Learning Research34

    On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models

    This paper argues that the gating mechanism in modern recurrent networks explains both the successes and failures of State Space Models (SSMs) on in-context learning and retrieval tasks: theory and experiments show gating causes SSMs to first learn an in-weights "memorization" solution while delaying or preventing convergence to a correct in-context learning solution, even when the architecture and memory capacity impose no fundamental limits. The authors also find that gating is often beneficial for improving generalization to long sequence lengths.

  11. Apple Machine Learning Research34

    Evaluating the Evaluators: Investigating LLM Judges for Personalized Writing Style Assessment

    This NeurIPS 2026 workshop paper examines how well LLM judges evaluate personalized writing tasks, testing how the writing task, generator-judge pairing, evaluation objective, and general reasoning ability affect judge performance across four LLMs and three long-form writing tasks with human pairwise labels. The authors find judge quality correlates weakly with MMLU-measured ability (Pearson's ρ ≤ 0.2), varies by task and criteria, and that paired comparisons yield more consistent results than independent scoring, while the strongest LLMs do not most reliably reproduce human evaluations.

  12. Meta Engineering40

    Intelligent Delivery: Making Every Unit of Compute Serve Better Ads

    Meta describes Intelligent Delivery, an ML-driven system that allocates compute across its ads serving stack toward a system-wide optimum, contributing roughly 10% incremental ranking performance on top of model and system advancements from 2017 through the first half of 2026. It combines a shared Resource Core Model (6B sparse and 20M dense parameters) that predicts each request's outcome, compute consumption, and latency impact, with a hierarchy of allocation engines spanning region, hardware SKU, and host-level decisions. Agentic AI now helps propose and test optimizations, contributing to about 20% of Intelligent Delivery optimizations in the first half of 2026 and about 80% of engineering effort on the most automated mechanism, with engineers approving every change.

  13. Databricks Blog38

    Air gapped, not data dark: how Bright Machines gives customers live factory dashboards without opening the plant

    Bright Machines captures PostgreSQL changes at the factory edge with Debezium, writes them as Apache Iceberg, pushes them out over a single auditable outbound path, and reads them in place in Databricks via Unity Catalog federation, with zero inbound ports to the plant. A row committed on the line is queryable in Databricks in one to two minutes, dashboards refresh every ten minutes, and a heartbeat check detects a broken pipeline in under thirty minutes. The first customer-visible dashboard went live about two months after kickoff, replacing nine loosely coupled services with one governed platform plus one edge path.

Nov 11Wed
  1. Google DeepMind71

    AlphaGenome 2: Deciphering how cells read DNA to better understand disease

    Google DeepMind announced AlphaGenome 2, a conditional DNA sequence model trained on over 107 million individual cell profiles across 68 tissues that predicts how genetic variants affect gene expression in specific human cell types and states. The model identified nearly twice as many causal variants (30% vs. 16%) and directional gene effects (43% vs. 23%) as the original AlphaGenome fine-tuned on single-cell data on the TenK10K immune cell dataset, and can generalize to unseen single-cell experiments without retraining. It is available for non-commercial research via the AlphaGenome API and in private preview for commercial research.

    Why it matters: It documents a concrete advance in cell-type-specific variant effect prediction, with benchmark numbers and disease examples useful for readers tracking AI in genomics.

  2. Databricks Blog58

    Genie with Genie Ontology Delivers 1.6× the Accuracy with Less than Half the Tokens

    Databricks developed a new enterprise data-agent benchmark with Turing, run in a live Databricks workspace, in which Genie achieved a 91% pass rate versus 45% and 58% for two frontier agents while using fewer than half the tokens and completing tasks 3–4x faster. The benchmark, built around a fictional UK energy supplier called Calderwick Energy, includes notebooks, dashboards, saved SQL queries and documents alongside hundreds of tables, testing whether agents can find and use business context that table schemas alone cannot provide. Genie Ontology, now generally available, automatically extracts and organizes this knowledge and surfaces relevant context for Genie.

  3. Databricks Blog58

    Bring your business context to ChatGPT, Claude, Microsoft Copilot Cowork and Grok Bot

    Databricks introduced Genie plugins that give agents in ChatGPT, Claude, Microsoft Copilot Cowork and Grok Bot a governed interface to enterprise context, drawing on Genie Ontology for definitions, relationships and source context. Genie interprets business terms, finds permitted data, writes and runs SQL, routes specialized questions to domain Genie Agents, and returns answers with citations, with interactive visualizations in clients supporting the MCP Apps extension. Agents without native plugins can access Genie through the MCP server.

  4. Databricks Blog60

    From enterprise context to action: Genie Agents now analyze more data sources to transform your work

    Databricks announced that Genie Agents can now analyze unstructured files in Unity Catalog volumes, connect to enterprise tools via generally available native MCP connectors, and be shared outside an organization through OpenSharing and Genie Clean Rooms (both in beta). The post cites customers including Unilever, HelloFresh, S&P Global Energy, Atlassian, General Motors and ModMed, and describes how agents parse documents, cite specific pages, and combine file content with governed tables.

    Why it matters: Useful for readers tracking how enterprise AI agents are expanding from structured-data querying to document analysis, tool actions and cross-organization sharing.

  5. Databricks Blog69

    Announcing GA of Genie Ontology

    Databricks announced the General Availability of Genie Ontology, an agentic ontology layer that agents build by learning from dashboards, SQL queries, notebooks and BI semantic models, while people govern definitions through Unity Catalog semantics. The release adds Genie Search (GA), Ontology Snippets (GA), external knowledge sources (Beta, starting with Power BI), organizational directories (GA, starting with Workday), Domains and Pages (GA), and Ontology Manager (coming soon in Beta). In a benchmark built with Turing simulating a Databricks workspace for a fictional UK energy supplier, Genie answered correctly in 91% of runs versus 45% and 58% for two frontier agents, using less than half the tokens and finishing three to four times faster.

    Why it matters: It details a major enterprise context-layer release and includes benchmark numbers comparing agents with and without the ontology.

  6. Hugging Face Blog38

    Token Pooling-Aware Training for Multi-Vector Embedding Models

    This post explains token pooling for multi-vector embedding models, which merges similar document token vectors via Ward hierarchical clustering to cut storage, and introduces pooling-aware training in Sentence Transformers that applies the retrieval loss to both original and pooled representations. At pool_factor=4, the pooling-aware model retains 97.8% of its unpooled NanoBEIR nDCG@10 versus 94.6% for a normally trained model, with a 4x reduction in document vectors. It includes a reproducible training example, optional kernel acceleration, and evaluation results across pool factors, fixed caps, and calibrated adaptive thresholds.

  7. Databricks Blog47

    Unity Catalog Metric Views, now in Power BI: one unified, governed semantic layer for all your reporting and AI

    Databricks introduces Publish to Power BI, which connects Unity Catalog metric views to Power BI semantic models so each metric view becomes a table, its dimensions become columns and its measures become DAX measures, with Power BI querying the metric view itself. Publishing can be done on demand from Catalog Explorer or automated via Power BI tasks in Lakeflow Jobs, and DirectQuery queries honor Unity Catalog access controls. The feature is available in Private Preview.

  8. Baseten Blog69

    How Baseten and Genspark trained a deep research model to outperform Opus 5.5

    Genspark worked with Baseten Applied Research to post-train Qwen3.5-397B into a specialized Deep Research model that scores 0.759 on Genspark's quality grader, up from 0.612 for the base model and above Opus 5.5 (0.738), Sonnet 5.5 (0.742), GPT-5.6 Sol (0.637), Kimi K3 (0.724) and DeepSeek V4 Pro (0.675). The recipe splits the task into research, trained with reinforcement learning against a shaped semi-verifiable reward, and synthesis, trained mainly with supervised fine-tuning on high-quality reports written from the model's own research trajectories, followed by end-to-end RL. The model crawls 5× more sources per query than the base model and runs with NVFP4 weights on 2×B200 GPUs per replica, now serving as the default for Genspark's Deep Research agent in production.

    Why it matters: A detailed, reproducible account of post-training an open model for a judged production workload, useful for teams weighing RL versus SFT and reward shaping.

  9. AWS Machine Learning Blog40

    Amazon Bedrock tokenomics data lake

    AWS describes a centralized observability data lake that pulls Amazon Bedrock model invocation logs from linked accounts into a central account via CloudWatch subscription filters, processes them through Lambda and Amazon Data Firehose into Parquet in S3, and exposes them through Athena as a single queryable table. Per invocation, the flattened table captures the prompt, response, input and output token counts, model, stop reason, account, timestamp, and IAM role, and can be joined to CUR 2.0 data on the IAM ARN for per-invocation cost attribution. The solution is deployed with two CloudFormation templates from the AWS Sample Bedrock Invocation Data Lake GitHub repository, with an optional Amazon Quick Sight dashboard.

  10. Claude Blog75

    Introducing Claude Whiteboards, Claude Sheets, and Claude Data

    Anthropic launched Claude Whiteboards and Claude Sheets in beta and made Claude Dashboards generally available as Claude Data, all on paid plans. Whiteboards lets teams brainstorm with Claude on a shared board and export to tools like Lucid, Miro, or Vani by Zoho; Sheets generates and edits spreadsheets with tabs, formulas, formatting, and drop-downs, and can export to .xlsx or CSV. Claude Data adds certified queries, data stories, live sharing, design systems, caching, and tracked changes, with partners including Amplitude, Mixpanel, Omni, PostHog, and Sigma.

    Why it matters: It details new Claude workspace features and their availability, useful for teams deciding whether to adopt them.

  11. Amazon Science62

    Mitra-v2: Scaling synthetic pretraining for tabular foundation models

    Mitra-v2 is a second-generation tabular foundation model for classification and regression, pretrained entirely on synthetic tables with 10x as many rows and roughly 3x as many columns as Mitra-v1, plus a new hybrid SCM prior and hybrid optimizer. On TabArena it leads the single-model field, beating Mitra-v1 on 32 of 33 datasets where both run natively and completing all 51 datasets within the one-hour budget, and fine-tuned Mitra-v2 ranks first on ScoringBench. The fine-tuned models and inference/fine-tuning code are released under Apache-2.0 on Hugging Face.

    Why it matters: Useful for readers tracking tabular foundation models, as it reports concrete benchmark rankings and an open Apache-2.0 release.

Nov 10Tue
  1. Google DeepMind58

    Opening the datasets behind our AI weather models

    Google's WeatherNext team released SatWeave, an hourly global mosaic of geostationary satellite imagery across 17 channels blending six satellites, and PARDIG, an AI-based global precipitation dataset at 15-minute and roughly 10-kilometer resolution trained on NASA GPM radar observations. Both datasets, key to the WeatherNext 3 model, are available as live feeds and historical archives on Google Cloud Storage, with PARDIG catalogs also in Google Earth Engine. PARDIG reportedly improves hourly precipitation detection skill over NASA's IMERG-Final by more than 30% on light-to-moderate precipitation.

  2. MIT News AI40

    MIT-led team receives funding to develop next-generation alternatives to modern weed killers

    MIT researchers will lead an effort of up to $53 million, awarded by ARPA-H under its STREAM program, to develop next-generation weed killers that help farmers and protect human health. The project, Precision Learning for On-target Weed control (PLOW), will use generative AI to predict chemical compounds or siRNA that disrupt the same plant enzymes targeted by glyphosate and other herbicides, with candidate molecules screened and then evaluated in greenhouse studies. The team also plans to develop living microbial systems that degrade agrochemicals before they reach food, soil, or waterways.