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  1. 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.

  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.

  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.

  1. Mistral AI71

    Mistral releases Timestral time-series foundation models, topping GIFT-Eval, TIME and fev-bench

    Mistral released Timestral, its first family of time-series foundation models, in four sizes from 5M to 209M parameters. The largest model ranks first on all three main public benchmarks, GIFT-Eval, TIME and fev-bench, among pre-trained zero-shot models from a single checkpoint without ensembling, using two-thirds of the parameters of the previous state of the art. Timestral produces probabilistic multivariate forecasts with 21 quantiles, supports past-only and known-future covariates, runs on CPU without a GPU, and is available through Mistral Forge for fine-tuning, with a GitHub repository and technical report published under a modified MIT licence.

    Why it matters: Mistral's first time-series foundation model family claims top zero-shot rankings on GIFT-Eval, TIME and fev-bench with a 209M-parameter checkpoint, offering a size-accuracy tradeoff reference for forecasting work.

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