How RecursiveMAS speeds up multi-agent inference by 2.4x and reduces token usage by 75%

UIUC and Stanford's RecursiveMAS lets AI agents collaborate in embedding space instead of text, cutting token usage by 75% ...
unite

Recursive Superintelligence Raises $650 Million to Pursue Self-Improving AI

A new frontier AI company called Recursive Superintelligence has emerged from stealth with $650 million in funding and an unusually ambitious goal: building AI systems capable of improving themselves ...

What happens when AI starts building itself?

Richard Socher's new $650 million startup wants to build an AI that can research and improve itself indefinitely — and he ...
IEEE

Recursive Confidence Training for Pseudo-Labeling Calibration in Semi-Supervised Few-Shot Learning

Abstract: Semi-Supervised Few-Shot Learning (SSFSL) aims to address the data scarcity in few-shot learning by leveraging both a few labeled support data and abundant unlabeled data. In SSFSL, a ...

Recursive Superintelligence raises $650M to build self-improving AI models

Recursive Superintelligence Inc., a startup that hopes to develop self-improving artificial intelligence models, launched ...

Notable Researchers Join $4 Billion Effort to Build Self-Improving A.I.

Recursive Superintelligence, founded by former Google, Meta and OpenAI researchers, is part of a growing effort to automate ...