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.
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Research1 min read
Research demonstrates a Program-Solve interface, utilizing a restricted local executor, improves deterministic arithmetic performance in Qwen2.5 models, particularly at the 32B scale. This approach offers an alternative to hardcoded calculators, but isn't a replacement for verified formulas.
From arXiv cs.AI
Research1 min read
A new scheduling method reduces tail latency in agentic LLM workflows by strategically releasing turns based on evolving tail risk. Evaluations using real execution traces show significant improvements in workflow flow time under contention.
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.
From arXiv cs.AI
Research1 min read
A new curation agent approach, ‘environment-probing,’ enhances existing agent memory systems by allowing the agent to verify and refresh its knowledge. This results in improved performance on benchmark databases and management tasks, reducing query costs and tool calls.
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.
