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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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LLMs2 min read
arXiv:2609.10830v1 Announce Type: new Abstract: When a language model finds a sentence unusually cheap to predict, it is tempting to conclude that the sentence was in its training data. Almost every published test of that inference has h...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.11020v1 Announce Type: new Abstract: We study K/V-cache interventions -- transplanting a target-conditioned K/V trajectory into a source-persona generation -- as a structured surface for persona control in decoder-only languag...
From arXiv cs.CL
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.
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LLMs1 min read
Together AI’s fine-tuning service now supports more open-weight models, offers live experiment tracking, and provides granular control over training processes. This expansion, alongside price reductions, enables faster, more efficient model development for a wider range of tasks.
From Together AI blog
Research1 min read
Google Research has developed ToolGrad, a method for generating training datasets for agents that utilize external tools. This approach uses textual "gradients" to guide tool selection, improving efficiency and reducing the need for large, manually curated datasets.
From Google Research blog
Agents1 min read
Amazon SageMaker Inference introduced prefix-aware routing, which optimizes LLM latency by directing requests with matching prompt prefixes to the same instance. This results in improved KV cache hit rates and reduced time-to-first-token, particularly for models like Llama 3.1 70B.
From AWS machine learning blog
Agents1 min read
Amazon SageMaker HyperPod now offers model caching, drastically reducing inference cold starts from minutes to seconds by pre-loading model weights. This improves the performance of large models deployed on the HyperPod.
From AWS machine learning blog
LLMs1 min read
Shopify is reverting to separate Swift and Kotlin codebases for its native iOS and Android apps. This change reflects advancements in agent technology, reducing the cost of implementation and translation, previously a key factor in maintaining separate platforms.
From Simon Willison
Agents1 min read
This blog post details a workflow for automating the process of extracting data from an RFI workbook using Amazon Quick Automate. The process involves reading from S3, using natural language prompts, refining the workflow through conversation, and generating CSV output back to S3, reducing development time from days to hours.
From AWS machine learning blog
AI1 min read
Kinaxis, a Canadian company, is deploying AI to assist businesses in managing supply chain disruptions. The system leverages machine learning to predict and mitigate risks, improving operational resilience.
From Microsoft AI news
Models1 min read
Cloudera and Mistral AI are partnering to deliver specialized AI models tailored for enterprise use, focusing on sovereign AI solutions for regulated industries. This collaboration enables innovation within specific data environments.
From Mistral AI news
Models1 min read
OpenAI and the GSA are providing US federal, state, local, and tribal governments with reduced licensing fees and enhanced cyber defense support for their AI deployments. This initiative aims to accelerate AI adoption and bolster government capabilities.
From OpenAI news
Research1 min read
Researchers introduced Cros, a risk-constrained stopping layer for clinical diagnosis agents, achieving improved exploratory performance and reduced error rates compared to existing methods. The system provides a finite-sample guarantee and offers insights into autonomous stopping reliability.
From arXiv cs.AI
LLMs1 min read
A new LLM pipeline using Hyper-Parallel Decoding achieves high extraction accuracy and significant cost reduction for e-commerce attribute extraction. This enables production-scale use for building structured product knowledge bases.
From arXiv cs.CL
LLMs1 min read
MonoTM is a new topic modeling framework that separates mixture estimation from semantic interpretation using sparse autoencoders. Experiments across benchmarks demonstrate that leveraging semantic features improves topic inference quality compared to relying solely on top words.
From arXiv cs.CL
LLMs1 min read
Edu-QuRating is a pipeline for scoring educational data using LLM judgments and distilled rater models. It enables filtering and reward shaping for language model pre-training and post-training, achieving competitive results compared to GPT-4.1-mini.
From arXiv cs.CL
LLMs1 min read
TEFM is a new framework for applying LLMs to critical domains by reducing token consumption and maintaining faithfulness. Experiments across Qwen3, Gemma-2, and Phi-4 show significant token reduction with competitive accuracy and faithful rationales.
From arXiv cs.CL
Research1 min read
Research identified seven distinct sources contributing to Physical AI capabilities: Recorded-Experience, Predictive-Modeling, Evaluative-Interaction, Surrogate-Environment, Mechanism-Grounded, Embodied-Coupling, and Evolution-Driven. This framework allows analysis of how capabilities are formed, supporting applications like explanation and transfer.
From arXiv cs.AI
LLMs1 min read
BuzzASR is a collection of 102 fine-tuned Whisper models for ASR, achieving significant improvements in CER compared to Whisper-large-v3 across many languages. The project releases models, code, and tokenizer replacement strategies for enhanced compression.
From arXiv cs.CL
Research1 min read
Experiments with a 63.9M-parameter student model using correctness-gated multi-teacher distillation showed minimal accuracy gains compared to hard filtering. The audit of the weighted arm revealed significant label loss and failed to establish a grounding effect, indicating no incremental decision benefit.
From arXiv cs.AI
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.
