PCWorld reports that Microsoft is enhancing Copilot with new user controls, including read-only options and the ability to lock the AI assistant to specific document sections. Microsoft is expanding Copilot's prompt box with contextually aware suggestions for Word and PowerPoint, while unifying commercial and consumer versions under single leadership. These updates aim to make AI assistance more helpful and less overwhelming for productivity tasks, with features currently being tested internally before reaching consumers. Google made headlines a short time ago for a plan to expand its Gemini prompt box as it combines AI and search. Microsoft is taking a different tack: it's also dynamically expanding its prompt box, but with an eye towards improving its productivity apps instead. Right now, Microsoft's efforts are traversing the outer reaches of its productivity solar system, being tested internally with a few targeted corporate partners, Fast Company reports .

We thank the reviewers for their encouraging and constructive comments. We are pleased that they find the paper well1 written and acknowledge the novelty and originality of the proposed task, which "has a potential to spark interest"2 (R1) and "may lead to future papers studying it" (R2). Regarding the proposed framework, R1 and R2 not only find it3 "sound" and "novel" but also stress the "re-implementation ease" from which "practitioners may benefit" (R1). Still,4 the reviewers raise points of improvement (R1, R3) and suggest a discussion about a related task (R2). We carefully5 address these comments below.

Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence. Yet it has remained underexplored with modern generative models. We study largescale language models on two new generation tasks: suggesting the next step in a mathematical proof, and full proof generation. We develop NATURALPROVER,a language model that generates proofs by conditioning on background references (e.g.

The level of autonomy is increasing in systems spanning multiple domains, but these systems still experience failures. One way to mitigate the risk of failures is to integrate human oversight of the autonomous systems and rely on the human to take control when the autonomy fails. In this work, we formulate a method of collaborative decision making through action suggestions that improves action selection without taking control of the system. Our approach uses each suggestion efficiently by incorporating the implicit information shared through suggestions to modify the agent's belief and achieves better performance with fewer suggestions than naively following the suggested actions. We assume collaborative agents share the same objective and communicate through valid actions. By assuming the suggested action is dependent only on the state, we can incorporate the suggested action as an independent observation of the environment. The assumption of a collaborative environment enables us to use the agent's policy to estimate the distribution over action suggestions. We propose two methods that use suggested actions and demonstrate the approach through simulated experiments. The proposed methodology results in increased performance while also being robust to suboptimal suggestions.

A short-lived AI tool promised to help users write like the greats--and a bunch of other random people, including me. T o me, the best first sentence of any piece of journalism is the one in Joan Didion's 1987 book,, which begins like this: "Havana vanities come to dust in Miami." I love that sentence and that propulsive first chapter so much that I once sat down to try to figure out how she did it. I looked at the sentences one at a time to assess what purpose each one was serving, and I counted how many of them Didion had needed to accomplish each thing she wanted to accomplish. Then I thought about how she figured out what order to put them in to have maximum page-turning impact.