Knowledge catalogue
arXiv:2607.13839v1 Announce Type: cross Abstract: This study examines the integration of AI into Human Resource Management in German companies. We ask if and how AI-based technologies are enquote{augm
arXiv:2608.25717v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is widely assumed to mitigate factual errors in large language models (LLMs), but it remains unclear whether retrie
arXiv:2604.09650v1 Announce Type: cross Abstract: Accurately predicting stock repurchases is crucial for quantitative investment and risk management, yet traditional static models fail to capture the
arXiv:2609.33143v1 Announce Type: cross Abstract: Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrativ
arXiv:2606.28355v1 Announce Type: cross Abstract: Selecting which companies to approach is a central challenge in business-to-business (B2B) sales, where decisions are often based on manual research a
arXiv:2610.06980v1 Announce Type: new Abstract: Sensing technologies have advanced rapidly across industries ranging from energy to automotive manufacturing. These systems generate high-dimensional (H
arXiv:2609.13334v1 Announce Type: cross Abstract: Enterprise AI agents often succeed in a demonstration and then stall once they must operate day after day. An industry report estimates that most pilo
arXiv:2609.15219v1 Announce Type: new Abstract: Founders face two linked decisions: whether to pursue an idea before founding, and which operating actions and capital partners fit afterward. We presen
arXiv:2608.00568v1 Announce Type: new Abstract: Fairness audits are increasingly mandated in high-stakes applications such as hiring, lending, and automated decision-making. Recent work has establishe
arXiv:2608.01322v1 Announce Type: new Abstract: Shadow trading -- trading in a peer firm's securities on the basis of material nonpublic information (MNPI) about an 'economically linked' company -- is
arXiv:2608.01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma. Each company can develop slowly and safely, or move faster while taking a risk that may r
arXiv:2608.07508v1 Announce Type: cross Abstract: Large language models are already advisors to millions of people of faith who bring them real decisions. The pressing question for a person of faith i
arXiv:2608.08407v1 Announce Type: cross Abstract: A bidder can quietly buy a stake in a company before making an offer for it. That stake, a toehold, is supposed to pay for itself twice: it makes the
arXiv:2603.04299v3 Announce Type: replace Abstract: Large Language Models (LLMs) often exhibit highly agreeable and reinforcing conversational styles, also known as AI-sycophancy. Although this behavi
arXiv:2605.01041v1 Announce Type: cross Abstract: In the envisioned future dense urban airspace, multiple companies will operate heterogeneous fleets of small unmanned aerial systems (sUASs), where ea
arXiv:2604.22446v1 Announce Type: new Abstract: Individual agent capabilities have advanced rapidly through modular skills and tool integrations, yet multi-agent systems remain constrained by fixed te
arXiv:2603.02702v3 Announce Type: replace Abstract: The financial domain involves a variety of important time-series problems. Recently, time-series analysis methods that jointly leverage textual and
arXiv:2603.19225v3 Announce Type: replace-cross Abstract: Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamenta
arXiv:2609.09853v1 Announce Type: new Abstract: LLM agents for enterprise systems of record cannot be evaluated on customer production data, and no existing substitute provides ground truth. We presen
arXiv:2606.25495v1 Announce Type: new Abstract: Saudi Telecom Company (STC) is among the most popular companies in Saudi Arabia, with many customers. Yet, there is still a big room for improvement in
arXiv:2609.30055v1 Announce Type: cross Abstract: In the Era by Eon benchmark, each question states the rules for its answer, and code computes the answer from a generated company's data. When agents
arXiv:2609.30012v1 Announce Type: cross Abstract: Language models advise people, keep them company, and write software while they sleep. Measuring what they do is hard: behavior has to be sampled repe
arXiv:2605.13743v1 Announce Type: new Abstract: Open datasets and benchmarks for entity-level carbon-emission prediction remain fragmented across access, scale, granularity, and evaluation. We introdu
arXiv:2604.24966v1 Announce Type: cross Abstract: Frontier AI companies first deploy their most advanced models internally, for weeks or months of safety testing, evaluation, and iteration, before a p
arXiv:2609.36054v1 Announce Type: cross Abstract: In contrast to even a year ago, AI systems now write most of the code inside the companies that build them. As more of the AI research and development
arXiv:2609.16082v1 Announce Type: cross Abstract: Social media platforms are popular channels for disseminating information, owing to their large user bases and ease of access. Companies also use soci
arXiv:2609.11286v1 Announce Type: new Abstract: Synthetic relational data is normally produced by a model trained on a real dataset, and its quality is measured as the distance to that dataset. This p
arXiv:2609.11321v1 Announce Type: new Abstract: Artificial intelligence is changing both software production and the economics of software-based business models. Classical technology due diligence mai
arXiv:2609.07334v1 Announce Type: new Abstract: The Asset Administration Shell (AAS) is a cornerstone of Industry 4.0 and the Digital Product Passport, providing standardized digital representations o
arXiv:2603.20957v4 Announce Type: replace-cross Abstract: Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely
arXiv:2608.29560v1 Announce Type: new Abstract: A company with a fixed artificial intelligence (AI) budget must decide which large language model (LLM) handles each recurring workload. What it lacks i
arXiv:2608.12594v1 Announce Type: cross Abstract: As more investors contemplate private markets and contend with limited transparency, sparse disclosures, and infrequent transactions, identifying econ
arXiv:2608.07037v1 Announce Type: cross Abstract: Small businesses often have only 12-24 months of accounting history, yet planning and risk workflows require coordinated forecasts across financial st
arXiv:2607.01867v1 Announce Type: cross Abstract: The use of LLMs in software development has become increasingly widespread on tasks such as code generation and summarization. Reports from large tech
arXiv:2605.03147v1 Announce Type: new Abstract: Earnings calls are a key source of financial information about public companies. However, extracting information from these calls is difficult. Unlike t
arXiv:2604.08525v1 Announce Type: cross Abstract: Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginni
arXiv:2610.17039v1 Announce Type: cross Abstract: Tool-using LLM agents are increasingly used to automate workflows governed by procedural manuals, such as company rules for booking travel or procurin
arXiv:2610.16070v1 Announce Type: cross Abstract: Frontier models are now trained for complex multi-agent tasks, from running a company to engaging in negotiations. These tasks necessitate training mo
arXiv:2610.09529v1 Announce Type: new Abstract: There are more than 2,000 listed companies on the UK's London Stock Exchange, divided into 11 sectors who are required to communicate their financial re
arXiv:2610.06204v1 Announce Type: cross Abstract: LLM agents are starting to own the full customer experience. Soon, LLMs may be selling and buying on behalf of companies and customers respectively. S
arXiv:2510.06105v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive adv
arXiv:2507.21112v4 Announce Type: replace Abstract: With the rapid rise of InsurTech, traditional insurance companies are increasingly exploring alternative data sources and advanced technologies to s
arXiv:2609.21859v1 Announce Type: new Abstract: Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely on
arXiv:2609.18729v1 Announce Type: cross Abstract: Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this
arXiv:2609.02512v1 Announce Type: new Abstract: Beauty assessments from Multimodal Large Language Models (MLLMs) are increasingly popular amongst users, companies, and aestheticians. This raises the q
arXiv:2609.00718v1 Announce Type: new Abstract: Many automobile and mobility companies deploy learned driving policies on embedded computers with limited memory and power. Pruning, knowledge distillat
arXiv:2609.01563v1 Announce Type: new Abstract: Semiconductor supply chains face escalating risks from geopolitical tensions, geographic concentration, and rapid technological shifts, yet no scalable
arXiv:2608.27391v1 Announce Type: new Abstract: LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don't want to shar
arXiv:2608.26372v1 Announce Type: cross Abstract: Large language models are increasingly deployed as autonomous agents serving users on behalf of companies, placing them in settings where user and dep
arXiv:2307.09295v3 Announce Type: replace Abstract: We study a ranking and selection problem of learning from choice-based feedback with dynamic assortments. In this problem, a company sequentially di
arXiv:2608.23320v1 Announce Type: new Abstract: Industrial demand changes the paradigms of production. Due to smaller batch sizes and more variations in products, companies face a growing challenge to
arXiv:2606.13892v2 Announce Type: replace-cross Abstract: The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exa
arXiv:2608.19526v1 Announce Type: cross Abstract: Stock market analysts and investors face a daily challenge: too much financial news, too little time. Manually reading and synthesizing hundreds of co
arXiv:2607.14345v4 Announce Type: replace-cross Abstract: People use language models for practical questions whose answers are difficult to verify. We show that models exhibit covert value leakage: th
arXiv:2608.10050v1 Announce Type: new Abstract: Small and medium-sized businesses need timely financial guidance, yet historical accounting logs record self-selected and often co-occurring business ch
arXiv:2608.10431v1 Announce Type: cross Abstract: Responsible AI (RAI) has become a central concern for technology companies, regulators, and the public. How industry practitioners interpret, implemen
arXiv:2607.29383v1 Announce Type: new Abstract: In recent years, with the development of big data technology, increasingly more companies use HDFS for data processing and storage. As a result, the mai
arXiv:2607.28946v1 Announce Type: new Abstract: Despite its many benefits, widespread access to individuals' personal data also causes severe privacy concerns for consumers, companies, and policymaker
arXiv:2607.24889v1 Announce Type: cross Abstract: Financial models combine public disclosures with analyst assumptions to produce forecasts and valuations. While some components can be checked mechani
arXiv:2607.25398v1 Announce Type: new Abstract: Language-model agents are increasingly deployed under standing instructions: a system prompt, a policy file, or a skills document is placed in context,