Search: “finance”

arXiv:2606.00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artifici

arXiv:2605.31381v1 Announce Type: new Abstract: We evaluate the consistency of automated judges in conducting a multi-dimensional safety evaluation in a reference-free setup. Our results indicate that

arXiv:2506.12060v2 Announce Type: replace-cross Abstract: Cybersecurity organizations are adapting to GenAI integration through modified frameworks and hybrid operational processes, with success influ

arXiv:2603.14778v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enables large language models to use external knowledge, but outsourcing the RAG service raises privacy c

arXiv:2605.28899v1 Announce Type: cross Abstract: Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses si

arXiv:2605.29420v1 Announce Type: new Abstract: Persona prompting is widely used to steer large language models, yet its practical value remains unclear. Prior work often evaluates persona prompting u

arXiv:2605.27571v1 Announce Type: new Abstract: Modern analytics systems are fundamentally reactive, requiring users to define queries over increasingly complex and continuously evolving data. In real

arXiv:2601.19302v3 Announce Type: replace Abstract: This paper introduces Formula Prompting (FP) and Formula-One Prompting (F-1), two single-call methods that elicit governing equations before solving

arXiv:2605.28315v1 Announce Type: new Abstract: General-purpose machine translation benchmarks such as FLORES-200 have reached a saturation regime on Chinese-English pairs, where modern large language

arXiv:2605.27489v1 Announce Type: cross Abstract: Multi-agent LLM systems decompose workflows across agents, tools, shared context, memory, and decision gates. This modularity improves interpretabilit

arXiv:2510.07231v4 Announce Type: replace-cross Abstract: Socio-economic causal effects depend heavily on their institutional and environmental contexts. The same intervention can produce different, e

arXiv:2605.27063v1 Announce Type: new Abstract: The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node f

arXiv:2509.09544v3 Announce Type: replace Abstract: Financial NLP has evolved rapidly since late 2022, outpacing narrative surveys. We introduce MetaGraph, a methodology for extracting typed knowledge

arXiv:2605.26903v1 Announce Type: cross Abstract: Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutuall

arXiv:2302.13473v2 Announce Type: replace Abstract: Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In

arXiv:2605.23946v1 Announce Type: cross Abstract: Climate volatility, regional production concentration, labor constraints, cyber risk, and dependence on long-distance fresh-produce supply chains expo

arXiv:2605.25826v1 Announce Type: cross Abstract: We develop a branched signature kernel solver for linear and nonlinear ordinary differential equations driven by a single observed trajectory of a pos

arXiv:2605.25297v1 Announce Type: cross Abstract: Effective features are crucial for predictive model performance, but creating them often requires domain expertise, limiting scalability across applic

arXiv:2507.02215v2 Announce Type: replace-cross Abstract: Motivated by the need for efficient estimation of conditional expectations, we consider a least-squares function approximation problem with he

arXiv:2605.26074v1 Announce Type: cross Abstract: Existing financial NLP benchmarks often rely on labels supplied by outside observers, measuring how language is perceived rather than what speakers ha

arXiv:2605.22672v2 Announce Type: replace Abstract: We document inverse scaling in LLMs on forecasting problems whose underlying time series exhibit superlinear growth and tail risk of regime change,

arXiv:2509.09215v2 Announce Type: replace Abstract: Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent c

arXiv:2605.21748v1 Announce Type: new Abstract: As interactive LLM-based applications are created and refined, model developers need to evaluate the quality of generated text along many possible axes.

arXiv:2508.16453v2 Announce Type: replace-cross Abstract: Distrust of public serving institutions and anti-establishment views are on the rise (especially in the U.S.). As people turn to social media

arXiv:2509.21698v2 Announce Type: replace Abstract: Risk categorization in 10-K risk disclosures matters for oversight and investment, yet no public benchmark evaluates unsupervised topic models for t

arXiv:2603.23638v2 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly tested on complex tasks, but their ability to allocate scarce resources over long horizons remain

arXiv:2605.16303v1 Announce Type: cross Abstract: Large language models (LLM) agents may offer tools to predict human responses to surveys. A common technique for defining these agents uses only demog

arXiv:2602.05287v2 Announce Type: replace Abstract: This position paper argues that the pursuit of 'Universal Foundation Models for Time Series' rests on a fundamental category error, mistaking a stru

arXiv:2605.17034v1 Announce Type: cross Abstract: Standard PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individual

arXiv:2510.18941v2 Announce Type: replace-cross Abstract: Evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses, limiting assessments to task

arXiv:2605.16407v1 Announce Type: cross Abstract: We present a framework for verifying the deterministic structured computations surrounding a large language model rather than the model itself, extend

arXiv:2605.17271v1 Announce Type: cross Abstract: Bi-causal optimal transport (OT) is a natural framework for comparing and coupling stochastic processes under nonanticipative information constraints,

arXiv:2512.00417v5 Announce Type: replace Abstract: This paper introduces CryptoBench, the first expert-curated, dynamic benchmark designed to rigorously evaluate the real-world capabilities of Large

arXiv:2605.15482v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being applied to financial analysis, reporting, investment decision support, risk management, compliance,

arXiv:2605.15488v1 Announce Type: new Abstract: Survival analysis provides a powerful statistical framework for modeling time-to-event outcomes in the presence of censoring. However, selecting an appr

arXiv:2605.14488v1 Announce Type: new Abstract: Large Language Models (LLMs) augmented with Retrieval-Augmented Generation (RAG) techniques are revolutionizing applications across multiple domains, su

arXiv:2603.16659v3 Announce Type: replace Abstract: Reinforcement-learned reasoning has powered recent AI leaps on verifiable tasks, including mathematics, code, and structure prediction. The harder b

arXiv:2605.06132v2 Announce Type: replace Abstract: In agent memory systems, the reranking model serves as the critical bridge connecting user queries with long-term memory. Most systems adopt the 're

arXiv:2605.12882v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have significantly advanced document understanding, yet current Doc-VQA evaluations score only the final answ

arXiv:2605.11526v1 Announce Type: cross Abstract: Training a deep neural network with the outputs of selected layers satisfying linear constraints is required in many contemporary data-driven applicat

arXiv:2605.12225v1 Announce Type: new Abstract: Understanding the internal machinations of deep Transformer-based NLP models is more crucial than ever as these models see widespread use in various dom

arXiv:2605.09855v1 Announce Type: new Abstract: Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-II

arXiv:2506.08136v3 Announce Type: replace Abstract: We introduce EconWebArena, a benchmark for evaluating autonomous agents on complex, multimodal economic tasks in realistic web environments. The ben

arXiv:2605.09408v1 Announce Type: new Abstract: Link prediction (inferring missing or future connections between nodes in a graph) is a fundamental problem in network science with widespread applicati

arXiv:2605.08400v1 Announce Type: cross Abstract: Dynamics of interacting systems in engineering, society, and nature often evolve over latent networks that govern which entities can interact. We stud

arXiv:2605.08646v1 Announce Type: cross Abstract: Large language model (LLM) agents face a structural tension: cloud agents provide strong reasoning but expose user data, while on-device agents preser