Retrieval-augmented generation (RAG) based large language models (LLMs) are widely used in finance for their excellent performance on knowledge-intensive tasks. However, standardized documents (e.g., SEC filing) share similar formats such as repetitive boilerplate texts, and similar table structures. This similarity forces traditional RAG methods to misidentify near-duplicate text, leading to duplicate retrieval that undermines accuracy and completeness. To address these issues, we propose the Hierarchical Retrieval with Evidence Curation (HiREC) framework. Our approach first performs hierarchical retrieval to reduce confusion among similar texts. It first retrieve related documents and then selects the most relevant passages from the documents. The evidence curation process removes irrelevant passages. When necessary, it automatically generates complementary queries to collect missing information. To evaluate our approach, we construct and release a Large-scale Open-domain Financial (LOFin) question answering benchmark that includes 145,897 SEC documents and 1,595 question-answer pairs. Our code and data are available at https://github.com/deep-over/LOFin-bench-HiREC.

Objective: Large language models (LLMs) are attracting increasing interest in healthcare. This commentary evaluates the potential of LLMs to improve clinical prediction models (CPMs) for diagnostic and prognostic tasks, with a focus on their ability to process longitudinal electronic health record (EHR) data. Findings: LLMs show promise in handling multimodal and longitudinal EHR data and can support multi-outcome predictions for diverse health conditions. However, methodological, validation, infrastructural, and regulatory chal- lenges remain. These include inadequate methods for time-to-event modelling, poor calibration of predictions, limited external validation, and bias affecting underrepresented groups. High infrastructure costs and the absence of clear regulatory frameworks further prevent adoption. Implications: Further work and interdisciplinary collaboration are needed to support equitable and effective integra- tion into the clinical prediction. Developing temporally aware, fair, and explainable models should be a priority focus for transforming clinical prediction workflow.

As low-resourced languages are increasingly incorporated into NLP research, there is an emphasis on collecting large-scale datasets. But in prioritizing quantity over quality, we risk 1) building language technologies that perform poorly for these languages and 2) producing harmful content that perpetuates societal biases. In this paper, we investigate the quality of Machine Translation (MT) datasets for three low-resourced languages--Afan Oromo, Amharic, and Tigrinya, with a focus on the gender representation in the datasets. Our findings demonstrate that while training data has a large representation of political and religious domain text, benchmark datasets are focused on news, health, and sports. We also found a large skew towards the male gender--in names of persons, the grammatical gender of verbs, and in stereotypical depictions in the datasets. Further, we found harmful and toxic depictions against women, which were more prominent for the language with the largest amount of data, underscoring that quantity does not guarantee quality. We hope that our work inspires further inquiry into the datasets collected for low-resourced languages and prompts early mitigation of harmful content. WARNING: This paper contains discussion of NSFW content that some may find disturbing.

This paper examines how decision makers in academia, government, business, and civil society navigate questions of power in implementations of artificial intelligence (AI). The study explores how individuals experience and exercise "levers of power," which are presented as social mechanisms that shape institutional responses to technological change. The study reports on the responses of personalized questionnaires designed to gather insight on a decision maker's institutional purview, based on an institutional governance framework developed from the work of Neo Institutionalists. Findings present the anonymized, real responses and circumstances of respondents in the form of twelve fictional personas of high-level decision makers from North America and Europe. These personas illustrate how personal agency, organizational logics, and institutional infrastructures may intersect in the governance of AI. The decision makers' responses to the questionnaires then inform a discussion of the field level personal power of decision-makers, methods of fostering institutional stability in times of change, and methods of influencing institutional change in the field of AI. The final section of the discussion presents a table of the dynamics of the levers of power in the field of AI for change makers and 5 testable hypotheses for institutional and social movement researchers. In summary, this study provides insight on the means for policymakers within institutions and their counterparts in civil society to personally engage with AI governance.

Tesla chief Elon Musk's $1tn pay package has been approved. Tesla chief Elon Musk's $1tn pay package has been approved. Chants of'Elon' erupt after compensation plan approved despite opposition from several high-profile investors Tesla shareholders approved a $1tn compensation plan for CEO Elon Musk on Thursday, awarding the world's richest person what would be the largest corporate payout in history if he meets the goals necessary to receive it. The pay package, which several high-profile investors opposed, demonstrates that shareholders still believe Musk can lead the automaker in an era dominated by robotics and artificial intelligence. The result of the vote was announced at the annual shareholder event in Austin, Texas, with more than 75% of investors voting in favor of the plan.

Tesla CEO Elon Musk has scored a resounding victory as shareholders have approved a pay package of as much as $878bn over the next decade, endorsing his vision of morphing the electric vehicle (EV) maker into an AI and robotics juggernaut. Shares of Tesla rose more than 3 percent in after-hours trading after the shareholders voted on Thursday. The proposal was approved with more than 75 percent support. "What we are about to embark upon is not merely a new chapter of the future of Tesla, but a whole new book," he said. "This really is going to be quite the story."

Kim Kardashian blames ChatGPT for law school test failures while Miami-Dade Sheriff's Office tests America's first autonomous police vehicle in the latest AI news.

Zohran Mamdani ran an online campaign based on real people and a real message. There are many fair questions following Zohran Mamdani's decisive victory. Will his campaign be a template for others? Will he be able or allowed to follow through on his campaign promises? Will the Democratic establishment accept that its future could look something like this proud 34-year-old democratic socialist?

The Unesco standards define a new category of data, 'neural data', and suggest guidelines governing its protection. The Unesco standards define a new category of data, 'neural data', and suggest guidelines governing its protection. Unesco adopts global standards on'wild west' field of neurotechnology UN body's recommendations driven by AI advances and proliferation of consumer-oriented neurotech devices It is the latest move in a growing international effort to put guardrails around a burgeoning frontier - technologies that harness data from the brain and nervous system. Unesco has adopted a set of global standards on the ethics of neurotechnology, a field that has been described as "a bit of a wild west". "There is no control," said Unesco's chief of bioethics, Dafna Feinholz.

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