Despite extensive efforts to create fairer machine learning (ML) datasets, there remains a limited understanding of the practical aspects of dataset curation.

However, a pivotal aspect of alignment for honesty involves discerning an LLM's

The Tokyo District Public Prosecutor's Office's special investigation squad arrested Kazutaka Yonekura, 48, the founder and former president of Japanese artificial intelligence developer Alt, and three others on Thursday on suspicion of padding the firm's sales in violation of the financial instruments and exchange law. The other three include Yusuke Hioki, 34, also a former president of the Tokyo-based company. The special squad did not reveal whether the suspects have admitted the allegations against them. They allegedly submitted to the Kanto Local Finance Bureau in September 2024 financial statements, in which the company's sales in the period from January 2022 to June 2024 were inflated by about ¥8.4 billion. In March this year, after Alt's listing on the Tokyo Stock Exchange's Growth section for startup in October 2024, the suspects submitted a statement that overstated sales for the business year to December 2024 by about ¥4.9 billion, according to the special squad.

Japan's top bank CEOs push for AI, soothing worry over human work Japan's top financial leaders are working to ease fears that AI will cost jobs, emphasizing its role in boosting efficiency and transforming work. The heads of Japan's biggest financial firms are going out of their way to assuage worries that artificial intelligence will take away jobs. I don't think humans will lose their value. Humans have ability for dialogue, empathy, creativity and ethics," Mizuho Chief Executive Officer Masahiro Kihara said on Thursday at an event hosted by the Nikkei. People might say, 'what about my job if we use more AI?' I think they can aim for more value-added work."

Recent advances in Large Language Models (LLMs) and Reinforcement Learning (RL) have led to strong performance in open-domain question answering (QA). However, existing models still struggle with questions that admit multiple valid answers. Standard QA benchmarks, which typically assume a single gold answer, overlook this reality and thus produce inappropriate training signals. Existing attempts to handle ambiguity often rely on costly manual annotation, which is difficult to scale to multi-hop datasets such as HotpotQA and MuSiQue. In this paper, we present A$^2$Search, an annotation-free, end-to-end training framework to recognize and handle ambiguity. At its core is an automated pipeline that detects ambiguous questions and gathers alternative answers via trajectory sampling and evidence verification. The model is then optimized with RL using a carefully designed $\mathrm{AnsF1}$ reward, which naturally accommodates multiple answers. Experiments on eight open-domain QA benchmarks demonstrate that A$^2$Search achieves new state-of-the-art performance. With only a single rollout, A$^2$Search-7B yields an average $\mathrm{AnsF1}@1$ score of $48.4\%$ across four multi-hop benchmarks, outperforming all strong baselines, including the substantially larger ReSearch-32B ($46.2\%$). Extensive analyses further show that A$^2$Search resolves ambiguity and generalizes across benchmarks, highlighting that embracing ambiguity is essential for building more reliable QA systems. Our code, data, and model weights can be found at https://github.com/zfj1998/A2Search