A newsletter breaking down AI research, technology, and Australian property in plain English
AI and technology, explained through what they actually mean. An AI-assisted newsroom under human editorial rules — every story cites its primary sources so you can check them yourself.
306 stories
A new multi-agent AI framework converts natural-language Operations Research problems into executable math, hitting state-of-the-art on 3 of 4 benchmarks — and
Indic AI researchers propose 'Culture Sensing,' a hermeneutic approach to building foundation models that serve India's low-resource languages without erasing c
New arXiv preprint proposes representing assets from any blockchain as ERC-20 tokens on one backend chain, enabling cross-chain lending and privacy apps for dev
A new arXiv preprint constrains model fine-tuning to a trusted adapter subspace, blocking backdoor attacks to 8% while matching full LoRA on clean data.
Researchers show human-readable text in a robot's view can trigger 6.96x latency in vision-language models, with physical prints still hitting 4.74x.
A July 2026 arXiv preprint introduces GPBACC, a coded-computing technique that tackles privacy leakage and malicious workers simultaneously across federated and
A new study of 85 Chrome extension wallets finds routine operations silently connect users' addresses, enabling cross-site tracking and possible deanonymization
A July 2026 arXiv paper formalises the interval-width trade-off in polynomial activation replacement for encrypted AI inference, affecting privacy-preserving ML
Unpeer-reviewed July 2026 arXiv preprint proposes treating LLM workflows as persistent, inspectable knowledge objects. Here's what it means for builders.
UniClawBench, a new arXiv benchmark with 400 bilingual tasks, tests proactive AI agents in live containers with simulated human feedback — vital for anyone depl
A new arXiv benchmark shows reinforcement-learning agents in decentralised energy markets exploit invalid generation when physics constraints are lifted — and a
New arXiv preprint proposes a twin-aware framework to protect federated reinforcement learning in self-driving cars from malicious parameter injection.
