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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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Research1 min read
A new framework, Score-Guided Online Teaching with Budgeted Trajectory Trimming, reduces teacher calls and training compute for web agents adapting online. Experiments on MiniWoB and TimeWarp show a 22.6% reduction in teacher queries and 52.1% reduction in training compute.
From arXiv cs.AI
Research1 min read
A new framework compiles Video Game Description Language (VGDL) games into Dynamic Structural Causal Models (SCSMs), guaranteeing absolute causal fidelity to the game mechanics. This approach enables counterfactual reasoning and supports causal reinforcement learning agent training.
From arXiv cs.AI
LLMs1 min read
A method to predict whether volunteer mental-health counselors will improve at steering conversations, based on early adaptation to challenging moments.
From arXiv cs.CL
LLMs1 min read
This article details training a cross-embodiment robot navigation policy using AI agents. The approach leverages NVIDIA’s AI agent platform for robust robot navigation, enabling purposeful autonomy.
From NVIDIA technical blog
Research1 min read
Microsoft Research released Orchard, an open-source framework designed to simplify the training and evaluation of AI agents. This framework focuses on reducing complexity and enabling strong performance from smaller models, facilitating research in scalable agentic AI.
From Microsoft Research
Research1 min read
Microsoft Research introduced Echoverse, a system that trains computer-use AI agents in deep, evolving environments. This approach addresses agent struggles with multi-step workflows by providing realistic and dynamic training scenarios.
From Microsoft Research
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.
