Search: “finance”

arXiv:2605.03426v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints rend

arXiv:2412.02408v3 Announce Type: replace-cross Abstract: The advent of smart contracts has enabled the rapid rise of Decentralized Finance (DeFi) on the Ethereum blockchain, offering substantial rewa

arXiv:2605.00257v1 Announce Type: new Abstract: The inception of Large Language Models (LLMs) has catalyzed AI adoption in the finance sector, yet their reliability in complex, jurisdiction-specific t

arXiv:2411.16121v3 Announce Type: replace-cross Abstract: In recent years, the growth of data across various sectors, including healthcare, security, finance, and education, has created significant op

arXiv:2604.26188v1 Announce Type: new Abstract: The growing reliance of machine learning models in high-stakes, highly regulated domains such as finance and insurance has created a growing tension bet

arXiv:2604.26213v1 Announce Type: cross Abstract: Loading high dimensional distributions is an important task for utilizing quantum computers on applications ranging from machine learning to finance.

arXiv:2602.01433v2 Announce Type: replace Abstract: Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-te

arXiv:2511.23036v2 Announce Type: replace-cross Abstract: Explaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where temporal and context

arXiv:2406.14075v2 Announce Type: replace Abstract: It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, financ

arXiv:2509.10546v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in finance, where unsafe behavior can lead to serious regulatory risks. However, most r

arXiv:2510.15096v2 Announce Type: replace Abstract: Real-world settings where language models (LMs) are deployed -- in domains spanning healthcare, finance, and other forms of knowledge work -- requir

arXiv:2604.20374v1 Announce Type: new Abstract: Automated Market Makers (AMMs), as a core infrastructure of decentralized finance (DeFi), uniquely drive on-chain asset pricing through a deterministic

arXiv:2601.17609v2 Announce Type: replace Abstract: In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that str

arXiv:2602.12708v2 Announce Type: replace Abstract: Vertical Federated Learning (VFL) has emerged as a critical paradigm for collaborative model training in privacy-sensitive domains such as finance a

arXiv:2508.01371v3 Announce Type: replace-cross Abstract: Smart contracts are important for digital finance, yet they are hard to patch once deployed. Prior work has mainly explored LLMs for smart con

arXiv:2604.16719v1 Announce Type: new Abstract: Time-series forecasting is central to many scientific and industrial domains, such as energy systems, climate modeling, finance, and retail. While forec

arXiv:2601.05403v2 Announce Type: replace Abstract: Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-au

arXiv:2601.04740v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied in specialized domains such as finance and healthcare, where they introduce unique safety risk

arXiv:2604.16748v1 Announce Type: new Abstract: Time series forecasting plays a pivotal role in critical sectors such as finance, energy, transportation, and meteorology. However, Long-term Time Serie

arXiv:2604.16325v1 Announce Type: new Abstract: Multivariate time series forecasting is fundamental to numerous domains such as energy, finance, and environmental monitoring, where complex temporal de

arXiv:2512.12858v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in business-critical domains such as finance, education, healthcare, and customer suppo

arXiv:2604.14583v1 Announce Type: new Abstract: Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to

arXiv:2604.13392v1 Announce Type: new Abstract: Tabular data remains prevalent in high-stakes domains such as healthcare and finance, where predictive models are expected to provide both high accuracy

arXiv:2604.13068v1 Announce Type: new Abstract: When do large language models decide to hallucinate? Despite serious consequences in healthcare, law, and finance, few formal answers exist. Recent work

arXiv:2604.12168v1 Announce Type: cross Abstract: The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance, transport

arXiv:2604.10865v1 Announce Type: new Abstract: Deep Clustering (DC) has emerged as a powerful tool for tabular data analysis in real-world domains like finance and healthcare. However, most existing

arXiv:2601.08209v3 Announce Type: replace Abstract: In domains such as materials science, biomedicine, and finance, high-stakes deployment of large language models (LLMs) requires injecting private, d

arXiv:2604.09601v1 Announce Type: new Abstract: Discovering predictive alpha factors in quantitative finance remains a formidable challenge due to the vast combinatorial search space and inherently lo

arXiv:2604.06177v1 Announce Type: cross Abstract: Specialized web tasks in finance, biomedicine, and pharmaceuticals remain challenging due to missing domain priors: queries drift, evidence is noisy,

arXiv:2610.11333v1 Announce Type: new Abstract: AI services incur inference costs during execution, while revenue may arrive later. Changing API prices, limited upfront capital, and service failures c

arXiv:2609.39701v1 Announce Type: new Abstract: Value steering should change an LLM's normative priorities while preserving the scenario, facts, and task constraints underlying its answer. Conventiona

arXiv:2609.32746v1 Announce Type: cross Abstract: While symbolic regression (SR) has been successfully used in science to discover new equations, its use in financial valuation is hindered by several

arXiv:2609.30940v1 Announce Type: new Abstract: Individually protective decisions can produce avoidable collective failures. As large language model (LLM) agents take on greater roles in financial dec

arXiv:2609.28335v1 Announce Type: new Abstract: Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is

arXiv:2609.27051v1 Announce Type: new Abstract: Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retire

arXiv:2609.22360v1 Announce Type: new Abstract: Electrical submersible pumps (ESPs) are essential in offshore oil production, where unexpected failures can result in significant operational and financ

arXiv:2609.17632v1 Announce Type: new Abstract: Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-

arXiv:2609.19096v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly integrated into healthcare systems worldwide, yet its successful clinical adoption depends critically on

arXiv:2609.16069v1 Announce Type: cross Abstract: Synthetic tabular data can match real data distributions while still violating the semantic constraints that govern valid tabular rows. This reveals a

arXiv:2609.03218v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly use user context such as memory, profiles, and role prompts to personalize their responses. This personalizati

arXiv:2609.00818v1 Announce Type: new Abstract: We argue that financial report generation should operate at the analytical rather than structural level, composing content from data-derived insights ra

arXiv:2608.24621v2 Announce Type: replace Abstract: Can language models be trusted in safety- critical operations? In such settings, strong per- formance on semantic metrics does not guaran- tee opera

arXiv:2608.26119v1 Announce Type: new Abstract: Whether large language models can be prompted to generate rhetorical fallacies on demand, and whether current safety post-training constrains this behav

arXiv:2608.13484v1 Announce Type: cross Abstract: When asked about entities outside their knowledge boundary, LLMs routinely fabricate plausible-sounding details rather than backing off to safer, more

arXiv:2503.05061v3 Announce Type: replace Abstract: Reliable evaluation of large language models (LLMs) is critical as their deployment rapidly expands, particularly in high-stakes domains such as bus

arXiv:2608.07493v1 Announce Type: cross Abstract: Current AI disclaimers often fail to function as intended due to warning habituation and a transparency paradox. As AI-generated information becomes p