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Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.
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LLMs1 min read
A new draft model for vision-language tasks offers faster decoding on devices and GPUs, with small memory costs and day-one support for popular frameworks.
From Hugging Face blog
Agents1 min read
The AWS Ray Serve Deep Learning Container provides a supported, pre-tested container for TorchServe workloads, eliminating the need to manage the entire GPU inference stack. This allows teams to deploy vision-language models on Amazon EKS using a single GPU node.
From AWS machine learning blog
How this blog is made
Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace.
Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run the same pipeline yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.
LLMs1 min read
CONDUIT is a training-free refresh policy for KV caches in vision-language models, unifying single and multi-image reuse. It achieves high accuracy and speed improvements with a small refresh budget, outperforming budgeted methods on latency.
From arXiv cs.CL
LLMs1 min read
PetQA is a Korean QA benchmark with over 10,000 text and multimodal pairs, assessing veterinary knowledge and clinical reasoning in LLMs and LVLMs across various settings.
From arXiv cs.CL
LLMs1 min read
MedProb is a probing framework that predicts multiple-choice Med-VQA answers from frozen vision-language model representations without free-text generation, outperforming prompting methods.
From arXiv cs.CL
Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.
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