Large reasoning models (LRMs) like OpenAI-o1 have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning. However, their extended reasoning processes often suffer from knowledge insufficiency, leading to frequent uncertainties and potential errors. To address this limitation, we introduce \textbf{Search-o1}, a framework that enhances LRMs with an agentic retrieval-augmented generation (RAG) mechanism and a Reason-in-Documents module for refining retrieved documents. Search-o1 integrates an agentic search workflow into the reasoning process, enabling dynamic retrieval of external knowledge when LRMs encounter uncertain knowledge points. Additionally, due to the verbose nature of retrieved documents, we design a separate Reason-in-Documents module to deeply analyze the retrieved information before injecting it into the reasoning chain, minimizing noise and preserving coherent reasoning flow. Extensive experiments on complex reasoning tasks in science, mathematics, and coding, as well as six open-domain QA benchmarks, demonstrate the strong performance of Search-o1. This approach enhances the trustworthiness and applicability of LRMs in complex reasoning tasks, paving the way for more reliable and versatile intelligent systems. The code is available at \url{https://github.com/sunnynexus/Search-o1}.

Artificial intelligence–connected sensors, tracking wristbands, and data analytics: We've seen this type of tech pop up in smart homes, cars, classrooms, and workplaces. And now, we're seeing these types of networked systems show up in a new frontier--prisons. Specifically, China and Hong Kong have recently announced that their governments are rolling out new artificial intelligence (AI) technology aimed at monitoring inmates in some prisons every minute of every day. In Hong Kong, the government is testing Fitbit-like devices to monitor individuals' locations and activities, including their heart rates, at all times. Some prisons will also start using networked video surveillance systems programmed to identify abnormal behavior, such as self-harm or violence against others.

It was because of the letter K that I found my youn ger sister, but for 14 years, it was also the letter K that kept us apart. I'd been searching for her online under variations of the name Maria Christina Sugatan since we lost touch in 1997, after our mom refused to let me speak to her. She was Maria at school but Chris at home and, later, Chrissy. It became my ritual to search for variations of her name online. Meredith Talusan is a freelance writer focusing on minority issues.

One challenge of building and maintaining large-scale data management systems is managing data fusion from multiple data sources. Often times, different data sources may represent the same data element in a slightly different way. These differences may represent an error in the data or a disagreement between sources on the correct value that best represents the data point. When the quantity of data managed and fused becomes sufficiently large, manual review becomes impossible, and automated systems must be built to manage data fusion. Some of the traditional solutions use simple voting theory, Dempster-Shafer theory, fuzzy matching and incremental learning. This paper presents a novel approach to data fusion in the domain of business listings. The task at hand, business listing categorization, suffers from conflicting and incomplete data from disparate data sources. Given the need for a high degree of accuracy in this task, we use a combination of case-based reasoning, joint probability, and domain-specific rules to improve data accuracy above other methods.