Test-Time Search

TL;DR for operators A robot action can look reasonable from the current camera image and still fail because its physical consequence is wrong, its coordinates are slightly off, or familiar skills must be composed in an unfamiliar order. World Action Planner (WAP)1 addresses that gap by putting prediction and search between action proposal and execution: a vision-language model proposes an action, a world model imagines its consequences, semantic feedback can revise the proposal, and local search resolves finer manipulation choices. ...

From One Shot to Many: Why AI Should Stop Guessing and Start Exploring One answer is tidy. One answer is easy to grade. One answer also happens to be a strangely fragile way to use AI. That is not just a philosophical complaint about creativity, brainstorming, or whether a chatbot sounds confident enough while being quietly wrong. It becomes a technical problem when AI systems generate artifacts that other systems must consume: code, formal specifications, compliance rules, database transformations, contracts, workflows, or mathematical statements. In those settings, the generated object is not merely a sentence. It is an interface. ...