Why AI Needs a “Genie Coefficient”

Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie…

Source: IEEE Spectrum

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

The Download: Chinese AI divides the White House, and a record copyright payout

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. China’s AI models have Trump’s AI world at war with itself Last weekend, several current and former advisers to President Donald…

Source: MIT Technology Review

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

Chinese AI Model Uses Less Muscle for Coding Tasks

Zain Hasan, an AI engineer at Together AI, has taught himself to use AI coding assistants while still keeping an eye on cost. He directs difficult problems to a frontier model, meaning one near the current state of the art in reasoning and capability, such…

Source: IEEE Spectrum

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

Advancing next-gen AI with materials science innovation

The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology…

Source: MIT Technology Review

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

IdeaTrail: Full-Process Agent Trajectories for Scientific Ideation

arXiv:2607.10144v3 Announce Type: replace Abstract: Scientific ideation unfolds over multiple stages, including literature search, paper reading, tool use, claim checking, cross-paper synthesis, brainstorming, rejection of weak directions, and iterative writing. Yet most existing resources capture isolated components or final artifacts rather than the process connecting…

Source: cs.AI updates on arXiv.org

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

AI Contagion in Social Networks

arXiv:2606.15206v2 Announce Type: replace-cross Abstract: We study how artificial intelligence (AI) interacts with social communication networks to shape the stability of collective knowledge. Agents exchange information through a network while AI systems generate content and retrain on the aggregate informational environment they influence. This interaction…

Source: cs.AI updates on arXiv.org

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

L2GTX: From Local to Global Time Series Explanations

arXiv:2603.13065v2 Announce Type: replace-cross Abstract: Deep learning models achieve high accuracy in time series classification, yet understanding their class-level decision behaviour remains challenging. Explanations for time series must respect temporal dependencies and identify patterns that recur across instances. Existing approaches face three limitations: model-agnostic XAI…

Source: cs.AI updates on arXiv.org

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