Blog
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.
Get the daily issue
Every new post of the day, in one email. Confirmation required.
Research1 min read
PerfReasoning assesses LLMs as performance reasoners and code generators, with models achieving up to 90% accuracy on reasoning tasks. Construction of models remains challenging, especially for smaller configurations.
From arXiv cs.AI
LLMs1 min read
A unified assessment shows post-training methods generally improve cross-lingual consistency across models and benchmarks, with some sensitivity to answer format and language coverage.
From arXiv cs.CL
LLMs1 min read
A new benchmark evaluates whether large language models can interpret social meaning in Chinese online comments, focusing on indirect and playful language. The strongest model achieves 81.42% accuracy.
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
Agents1 min read
A continuous pipeline for building physical AI systems is demonstrated using NVIDIA Cosmos 3 on SageMaker HyperPod, focusing on synthetic data, training, and evaluation with GPU goodput as a key metric.
From AWS machine learning blog
LLMs1 min read
BenchMIRT investigates the alignment between LLM benchmark tasks and real-world capabilities, highlighting potential discrepancies for model evaluation.
From Hugging Face blog
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.
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.
