News

AI news, roundups, and analysis from Report AI — each story tied back to the primary-source statistics behind the headline. New analysis is published here as major AI data, funding, and policy events break. For the full set of sourced, dated AI numbers, browse the Library at report-ai.org/library/.

The Download: AI agents for science, and the “censorship-industrial complex”

by

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI for science needs reasoning, not just data —Eric Schmidt, the former CEO of Google and the cofounder of Schmidt Sciences,…

Source: MIT Technology Review

Automatically aggregated summary — full article and all rights belong to the original publisher.

These startups are chasing the next big thing in LLMs

by

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention…

Source: MIT Technology Review

Automatically aggregated summary — full article and all rights belong to the original publisher.

AI for science needs reasoning, not just data

by

Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the “facts of physical science have all been discovered.” In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of…

Source: MIT Technology Review

Automatically aggregated summary — full article and all rights belong to the original publisher.

H+ Embedding: Harmonizing Global and Token-Level Retrieval with Context-Dependent Phrases

by

arXiv:2608.00065v3 Announce Type: replace Abstract: Terminology-intensive retrieval, especially in medical settings, depends on preserving multi-word entities, abbreviations, numerical constraints, and compositional concepts. However, existing representations lie at two extremes: single-vector retrievers often over-compress local relevance signals, while token-level late interaction retains every tokenizer subword at…

Source: cs.AI updates on arXiv.org

Automatically aggregated summary — full article and all rights belong to the original publisher.

Learning to Walk With Less: A Dyna-Style Approach to Quadrupedal Locomotion

by

arXiv:2509.06296v2 Announce Type: replace-cross Abstract: Traditional on-policy reinforcement learning (RL) controllers for quadrupedal locomotion often suffer from low data efficiency, requiring millions of interactions with simulated environments to achieve stable control. We integrate model-based techniques that improve sample efficiency by augmenting PPO rollouts with synthetic…

Source: cs.AI updates on arXiv.org

Automatically aggregated summary — full article and all rights belong to the original publisher.

Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations

by

arXiv:2608.07385v1 Announce Type: cross Abstract: Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing. However, existing approaches do not operate as full-stack wearable processors, i.e., they do not simultaneously address task-specific classification performance, disentangled and interpretable…

Source: cs.AI updates on arXiv.org

Automatically aggregated summary — full article and all rights belong to the original publisher.