Meta says it is acquiring the Chinese-founded AI firm Manus as it looks to boost the capabilities of its tech. Bloomberg analysts and The Wall Street Journal suggested the purchase could be worth more than $2bn (£1.48bn). Meta said the deal would help improve its own AI by giving people access to agents - tools which can do complex things with minimal user interaction such as planning trips or making presentations. Manus's exceptional talent will join Meta's team to deliver general-purpose agents across our consumer and business products, including Meta AI, it said in a blog post. Barton Crockett, analyst at Rosenblatt Securities, told Reuters it was a natural fit for Meta, which extended into boss Mark Zuckerberg's vision of personal AI using agents.

Tech giant Meta has announced it will buy artificial intelligence startup Manus in a rare crossover of US and Chinese technology amid Washington and Beijing's heated tech rivalry. Meta said the acquisition would see it take over the operation of Manus's self-directing AI agent and integrate the technology into its own products. Meta, the parent company of Facebook and Instagram, said the deal would bring one of the "leading autonomous general-purpose agents" to billions of people worldwide. "Manus's exceptional talent will join Meta's team to deliver general-purpose agents across our consumer and business products, including in Meta AI," the California-based firm said in a statement on Monday. "We're excited to welcome the Manus team and help improve the lives of billions of people and millions of businesses with their technology."

Artificial intelligence (AI) is reshaping scientific discovery, evolving from specialized computational tools into autonomous research partners. We position Agentic Science as a pivotal stage within the broader AI for Science paradigm, where AI systems progress from partial assistance to full scientific agency. Enabled by large language models (LLMs), multimodal systems, and integrated research platforms, agentic AI shows capabilities in hypothesis generation, experimental design, execution, analysis, and iterative refinement -- behaviors once regarded as uniquely human. This survey provides a domain-oriented review of autonomous scientific discovery across life sciences, chemistry, materials science, and physics. We unify three previously fragmented perspectives -- process-oriented, autonomy-oriented, and mechanism-oriented -- through a comprehensive framework that connects foundational capabilities, core processes, and domain-specific realizations. Building on this framework, we (i) trace the evolution of AI for Science, (ii) identify five core capabilities underpinning scientific agency, (iii) model discovery as a dynamic four-stage workflow, (iv) review applications across the above domains, and (v) synthesize key challenges and future opportunities. This work establishes a domain-oriented synthesis of autonomous scientific discovery and positions Agentic Science as a structured paradigm for advancing AI-driven research.