VLM

TL;DR for operators A manipulation agent can reach the requested final state and still have executed the task unsafely. That creates a measurement problem for teams using task-completion rates to decide whether an embodied model, prompt, or policy update is ready for deployment. SafeRelBench tests this gap directly. Across seven evaluated VLM-driven agents, the spatial-relation cases produced task Success Rates (SR) of 0.52–0.73 but Safety Success Rates (SSR) of only 0.16–0.40. In matched non-spatial settings, SR rose to 0.83–0.94 and SSR reached as high as 0.91.1 The benchmark therefore measures something final-state success can miss: whether the agent satisfied the relevant safety prerequisite before taking the risky action. ...

Preference Signals, Not Preference Theater Businesses are currently learning an expensive lesson: user behavior is not the same thing as user preference. A person clicks because the button was large. A driver brakes because the situation was unclear. A customer accepts a chatbot answer because the refund is small and arguing is tedious. A manager approves a workflow because the dashboard made the alternative invisible. The log file looks objective. It is also quietly contaminated by habit, uncertainty, exploration, friction, fatigue, and the occasional human desire to end the meeting before lunch. ...

Review queue. Someone has to decide whether an image is “unsafe,” “misleading,” “healthy,” “premium,” “clickbait,” “brand-safe,” or “not really our vibe.” The label sounds simple until the first borderline case appears. A salad with too much cream. A gaming ad that hints at easy money but never quite says it. A before-and-after photo where the “achievement” is visible only if one is feeling generous. ...

A next-generation vision-language model by Google, combining Gemma LLM and SigLIP vision encoder for image captioning, VQA, and image-text reasoning tasks.