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The Answer Engine AI News · Sydney

AI news, read the way machines read it. Sydney.

Curated daily by AISearch Global. Every story links to its original source — we don't republish, we round up.

AI News

PrismML brings its tiny LLMs to Qualcomm-powered smart glasses

Prism's larger goal is open-weight AI that runs on devices and makes better use of the computing power they already have.

AI News · Human-AI Research

The Download: a bid to scrap the virtual wall and AI hits Climate Week

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. A congressional representative just proposed killing America’s border tower program Delia Ramirez, a Democratic US representative from Illinois, has announced plans to introduce legislation to terminate the surveillance tower program along…

AI News · Human-AI Research

AI is dominating the conversation at Climate Week

This week, world leaders descended on Manhattan for the UN General Assembly. It’s also New York Climate Week—investors, policymakers, advocates, and journalists are colliding at panels, talks, and fancy dinners. With so many climate voices in one place, the discourse can feel a little louder than usual. This year, the unavoidable topic is artificial intelligence.…

Human-AI Research

4DGS-JEPA: Temporally Compositional Joint-Embedding Prediction for Dynamic Gaussian Splatting

arXiv:2609.25036v1 Announce Type: new Abstract: Dynamic Gaussian Splatting provides an explicit representation of evolving 3D scenes, but existing approaches are primarily optimized for reconstruction, future-state generation, or rendering rather than for learning reusable predictive dynamics. We propose 4DGS-JEPA, a Gaussian-native joint-embedding predictive architecture for causal multi-horizon prediction over dynamic Gaussian scenes. The model uses a hierarchical scene-, motion-group-, and Gaussian-level representation together with a horizon-conditioned transition operator that supports both direct prediction and recursive rollout. Its central principle is temporal composition: different chronological transition paths reaching the same future endpoint should produce compatible predictive states. Endpoint and multi-horizon path supervision anchor these predictions to future target embeddings, while a selective geometry decoder and geometry-level composition ground the learned dynamics in consistent group motion and Gaussian geometry without requiring complete future appearance reconstruction. We further introduce a hybrid correspondence mechanism that combines persistent canonic

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