AI and technology, explained through what they actually mean
An ETH Zurich preprint proves four discrete diffusion methods optimize the same object — and reveals why one popular parameterization diverges at initialization
GaP, a multi-agent coding framework on arXiv, builds computation graphs for robot tasks and rehearses them in simulation to tackle variable automation's reliabi
A new arXiv preprint from UC Berkeley argues that language models measuring culture have already absorbed it — making neutral measurement impossible.
A new arXiv preprint distils 25 years of cybersecurity incident response research into a single taxonomy, comparing it against NIST and seven other frameworks —
Pre-trained Transformers detect smart grid false data injection without manual features, matching XGBoost (MCC 0.595 vs 0.604) — a shift for grid operators.
A new arXiv preprint proposes converting malware binaries into colour images via Hilbert curves and entropy, then classifying them with machine learning — but t
A new arXiv preprint finds AI coding agents like Codex and Claude Code are too stochastic for single benchmark runs, affecting how teams evaluate agent performa
Pmeta-TLA uses timbre leakage to plant numerous backdoors at once in speech classification models, sounding natural to humans — a supply-chain threat for smart
Bit2Watt preprint shows how manipulating 1,000 GPUs in a renewable-heavy data centre can push power distortion to 46.8%, threatening both compute and grid stabi
Researchers show a single email can silently poison an AI agent's long-term memory, succeeding 87.5% of the time on benchmark tests — affecting all persistent-a
An arXiv preprint defines world models as internal simulators and proposes a staged roadmap — but AI researchers across subfields still disagree on what the ter
A new arXiv preprint quantifies why trained networks outperform their kernel limit on compositional functions by up to six orders of magnitude in test error.