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Prefix Keys and Values Matter Late: RIPE Caching for Self-Speculative Decoding in Looped Language Models

arXiv:2610.34436v1 Announce Type: new Abstract: A looped language model runs one block of layers $R$ times per token, so its key-value (KV) cache holds $R$ planes per recurrent layer, and self-speculative decoding (draft with a few loops, verify with all $R$) reads them all. We find that prefix keys and values matter late: at equal perturbation size, loops 25-32 are $e^{15.0}$ (Huginn-0125) and $e^{12.8}$ (Raven-Llama) times more sensitive than loops 2-9, and the planes approach the last one geometrically, at 0.88 and 0.81 per loop. Whether the deepest plane alone suffices then depends on how ripe the late planes are: on Raven, storing only it is indistinguishable from a bf16-to-fp32 change; on Huginn, 1% of positions carry 74% of its error. We turn this into RIPE, which stores the deepest

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Parametric Session States Recover Retrieval Intent without Replaying History

arXiv:2610.33047v1 Announce Type: new Abstract: Long-running assistants often interpret follow-up requests whose referents, constraints, and active goals were established many turns earlier. The central challenge is therefore not only context length, but retrieval intent: a current query such as ``use the previous configuration'' may omit the project, entity, and version terms needed to locate relevant evidence. Long-context models can recover those terms by replaying the interaction, but repeatedly pay the cost of processing history. Conventional retrieval-augmented generation (RAG) is cheaper, yet assumes an information-sufficient query; conversational rewriting either rereads the history or depends on an explicitly maintained textual memory. We introduce \emph{Parametric Session State}

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Cutting both ways: AI sharpens systems protection while opening new exploits for malicious actors

The cybersecurity industry is discovering that powerful frontier AI models are a double-edged sword. Companies can use models to identify vulnerabilities in source code and system configurations faster than ever before. As it turns out, malicious actors can do this as well. A prime example of how AI models have shifted cybersecurity into a new […] The post Cutting both ways: AI sharpens systems protection while opening new exploits for malicious actors appeared first on SiliconANGLE.

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What to watch for as enterprises move from AI experimentation to autonomous operations

For the past three years, the enterprise artificial intelligence conversation has focused on what models can say. The next three years will be about what systems are allowed to do. That’s a harder problem that better models alone won’t solve. On Wednesday, Oct. 28, I’ll be in Boston for the Bloomberg Tech Event: Building the […] The post What to watch for as enterprises move from AI experimentation to autonomous operations appeared first on SiliconANGLE.

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Release: v5.21.0

# Release v5.21.0 ## New Model additions ### MiniCPM-OCR MiniCPM-OCR is a document-understanding model from OpenBMB's MiniCPM-V family. It keeps the MiniCPM-V 4.6 architecture, which pairs a SigLIP vision encoder and a window-attention merger with a Qwen3.5 language model. It is specialized for reading text-dense images such as scanned pages, screenshots, receipts and tables. **Links:** [Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/minicpm_ocr) | [Paper](https://huggingface.co/papers/2509.18154) * Feat test (#49920) by @guarin in [#49920](https://github.com/huggingface/transformers/pull/49920) ### Flint Flint is Recursive's mixture-of-experts language model. Compared to a standard SwiGLU MoE transformer such as Qwen3-MoE, it uses hybrid attention (sliding-windo

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

Fleet-wide computer vision needs a model per camera, not one model for the whole fleet. This post presents a working Amazon SageMaker SDK v3 reference architecture that trains, quality-gates, compiles per-camera models with SageMaker Neo, and versions thousands of them, with an AWS Step Functions layer pacing fleet-scale retraining within account quotas.

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