Agent skills are like apps for AI helpers, but many of them are not carefully checked for safety yet.
Most text-to-image models act like word-to-pixel copy machines and miss the hidden meaning in our prompts.
DanQing is a fresh, 100-million-pair Chinese image–text dataset collected from 2024–2025 web pages and carefully cleaned for training AI that understands pictures and Chinese text together.
Large language models usually get only a final thumbs-up or thumbs-down at the end of their answer, which is too late to fix mistakes made in the middle.
ToolSafe is a new way to keep AI agents safe when they use external tools, by checking each action before it runs.
The paper introduces M^4olGen, a two-stage system that designs new molecules to match exact numbers for several properties (like QED, LogP, MW, HOMO, LUMO) at the same time.
LaViT is a new way to teach smaller vision-language models to look at the right parts of an image before they speak.
The paper introduces SIN-Bench, a new way to test AI that read long scientific papers by forcing them to show exactly where their answers come from.
FlowAct-R1 is a new system that makes lifelike human videos in real time, so the on-screen person can react quickly as you talk to them.
The paper turns messy character descriptions from stories into neat, executable rules so role‑playing AIs act like the character in each specific scene.
This paper turns a video model into a step-by-step visual thinker that makes one final, high-quality picture from a text prompt.
Big video makers (diffusion models) create great videos but are too slow because they use hundreds of tiny clean-up steps.