Blog

Results for “uncertainty quantification”

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.

All Models LLMs Agents Research AI

#models 211 #agents 148 #llm 134 #research 112 #ai 92 #inference 73 #news 33 #evaluation 32 #benchmark 29 #training 16 #open-weights 15 #automation 13 #deployment 13 #security 13 #optimization 12 #aws 10 #safety 9 #generative-ai 8

ResearchSep 9, 20261 min read

CUSP: System-Level Uncertainty for Multi-Agent Reasoning

CUSP provides a training-free framework to quantify uncertainty in multi-VLM ensembles. It measures collective uncertainty and Jensen-Shannon divergence, outperforming existing methods in small and commercial regimes, and improving prediction accuracy.

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.

How this blog is made

Every post is a routed request

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.