Generative AI agents improve ERP systems by automating complex financial tasks.
problem Static, rule-based workflows limit adaptability and intelligence in ERP systems.
method Introducing Generative Business Process AI Agents (GBPAs) that integrate generative AI with business process modeling and multi-agent orchestration.
result GBPAs achieve up to 40% reduction in processing time and 94% drop in error rate.
Qlib aims to integrate AI into quantitative investment.
problem Challenges in applying AI to quantitative investment.
method Design and develop Qlib to accommodate AI-driven workflow.
result Qlib realizes the potential of AI technologies in quantitative investment.
A new method for releasing AI workflows to avoid premature incorrect results.
problem Statistical challenges in releasing AI workflows with adaptive scoring.
method Wrapper that calibrates and accumulates evidence from high-scoring failures.
result Reduces premature incorrect release while still releasing on moderate evidence.
AI helps simplify complex ship finance processes.
problem Complexity in ship finance due to data and regulatory requirements.
method Integrates large language models for document comprehension, information extraction, and workflow automation.
result AI-assisted systems can support maritime finance professionals in managing complex information and reporting requirements.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
problem Cognitive biases affect human-AI collaboration, leading to suboptimal outcomes.
method Randomized experiment with 2,784 participants, manipulating AI suggestion quality, task burden, and financial incentives.
result Individual attitudes toward AI are the strongest predictor of performance, influencing accuracy and overcorrection.
Blockchain helps secure payments between AI agents.
problem Ensuring secure payments between untrusted AI agents.
method Systematized four-stage lifecycle for A2A payments on blockchain.
result Challenges remain in weak intent binding, misuse, and limited accountability.
AI agents improve forecast combination in empirical economics.
problem Hidden researcher degrees of freedom in AI-generated code.
method Adapted agent-loop architecture to empirical economics, added holdout evaluation.
result Independent agent searches find better forecast methods than benchmarks.
AI agents improve forecast combination but require transparency.
problem AI coding agents increase flexibility in empirical economics, leading to hidden degrees of freedom.
method Adapted open-source agent-loop architecture to empirical economics workflow, adding post-search holdout evaluation.
result Multiple agent runs outperform standard benchmarks in rolling evaluation but not all on post-search holdout.
Rubin LSST DESC uses AI/ML for dark energy research.
problem Challenges in uncertainty quantification and model robustness for AI/ML in DESC.
method Bayesian inference, physics-informed methods, validation frameworks, active learning.
result AI/ML methods are essential but require rigorous evaluation and governance.
Commentary on Cheng's fairness comparison between tests and AI.
problem Distinction between equality and equity in fairness.
method Systematic comparison of test fairness and algorithmic fairness.
result Importance of causality in fairness research.
GAICF proposes a framework for governing generative AI in banking.
problem Generative AI's impact on financial decision-making and governance.
method SR 26-2-compatible governance framework for generative AI applications.
result GAICF aligns generative AI practices with SR 26-2 supervisory expectations.
GAICF proposes a framework for managing generative AI risks in banking.
problem Generative AI's impact on financial decision-making and governance.
method SR 26-2-compatible governance framework for generative AI.
result GAICF aligns generative AI practices with SR 26-2 supervisory expectations.
Survey of determinism issues in financial AI systems.
problem Vulnerabilities in reproducibility of financial AI systems.
method Literature review and first-party experiments on public financial datasets.
result Proposed a layered evaluation framework linking modality-specific metrics to audit readiness.
FinRobot opens-source AI for financial tasks, breaking down complex problems.
problem Barriers to AI adoption in finance due to proprietary data and specialized knowledge.
method Develops open-source AI agent platform with four layers: Financial AI Agents, LLM Algorithms, LLMOps/DataOps, and Foundation Models.
result FinRobot democratizes AI access for financial analysis.
Unified AI system for data quality control and governance in regulated environments.
problem Isolated data quality control steps in existing systems.
method AI-driven framework integrating rule-based, statistical, and AI methods.
result Empirical gains in anomaly detection, reduced manual remediation, improved auditability.
Survey of AI in quant finance, from deep learning to LLMs.
problem Improving predictive modeling and automation in asset management.
method Exploring AI contributions to quant investment pipeline, from human-crafted features to LLMs.
result AI has enabled scalable modeling and autonomous agents in quant finance.
Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algorithms, and evaluation of results. This paper discusses how such human-machine collaboration can be approached through the sta…
Study examines machine learning competitions' impact on AI development.
problem Fostering innovation and skill development in AI.
method Analysis of major competition platforms, workflows, and participant demographics.
result MLCs promote collaboration, reproducibility, and continuous innovation in AI.
Sherpa.ai framework combines federated learning and differential privacy for edge AI services.
problem Protecting data privacy in edge AI services.
method Holistic federated learning and differential privacy approach with methodological guidelines.
result Demonstrated through classification and regression use cases.
Unified Bayesian-AI framework improves epidemiological risk prediction and uncertainty quantification.
problem Lack of calibrated uncertainty in machine learning models for epidemiology.
method Combines Bayesian prediction with Bayesian hyperparameter optimization using logistic regression and Gaussian-process Bayesian optimization.
result Unified Bayesian-AI framework provides reliable coverage and improved calibration, enhancing epidemiological decision making.
AI system analyzes financial analyst recommendations and track records for portfolio construction.
problem Human PMs rely on analyst recommendations and track records for portfolio decisions.
method Develops AI-based Recommender Systems to replicate analyst conviction and track records.
result AI can improve portfolio construction by integrating analyst conviction and track records.
Paper develops framework for AI agents in financial markets.
problem Systemic implications of AI in finance depend on agent architectures.
method Four-layer architecture and AFMM model for analysis.
result AI agents can improve market efficiency and resilience.
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption. One simple idea is to use the leave-one-out error of each training …
New framework learns complex AI attitudes from heterogeneous data.
problem Heterogeneous ordinal structure in AI attitudes, poorly captured by existing methods.
method Monotone Gaussian score embedding, BNP complexity discovery, confirmatory fixed-K estimation.
result Reduced holdout MSE by 25.8% over single-graph baseline.
New benchmarks focus on LLM risk in finance, not just accuracy.
problem Standard benchmarks ignore LLM financial risks, leading to unsafe deployment.
method Three-level agenda: model, workflow, and system stress-testing.
result Hidden weaknesses in LLMs are missed by traditional benchmarks.
LLMs cause inconsistent financial outputs, smaller models are more reliable.
problem Inconsistent outputs from LLMs undermine auditability and trust in financial workflows.
method Finance-calibrated deterministic test harness, task-specific invariant checking, model classification, and cross-provider validation.
result Smaller models (Granite-3-8B, Qwen2.5-7B) achieve 100% output consistency, while larger models like GPT-OSS-120B have high drift.
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
problem Kernel learning and model selection
method GPU acceleration
result Competitive predictive performance with speedups
AI enhances pollen recognition in veterinary imaging using holographic microscopy.
problem Challenges in recognizing pollen in holographic images due to speckle noise and artifacts.
method Training YOLOv8s and MobileNetV3L on dual-modality dataset, employing WGAN-SN for synthetic data augmentation.
result GAN-based augmentation improves object detection and classification in holographic images, closing the performance gap.
New framework uses OR to ensure AI systems make safe decisions.
problem Ensuring generative AI systems make safe decisions as they gain autonomy.
method Developed a conceptual framework combining flow-based models and adversarial robustness.
result Increased autonomy requires new OR approaches for feasibility, robustness, and stress testing.
Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation work…
The field of high-energy physics (HEP), along with many scientific disciplines, is currently experiencing a dramatic influx of new methodologies powered by modern machine learning techniques. Over the last few years, a growing body of HEP literature has focused on identifying promising applications of deep learning in …
Paper presents a workflow for reliable unsupervised learning in science.
problem Lack of standardization in unsupervised learning workflows for reproducible scientific discoveries.
method Structured workflow including data preparation, modeling, validation, and communication.
result Illustrates the importance of validation in unsupervised learning.
This paper analyzes machine learning workflows in climate modeling.
problem Challenges in integrating machine learning with climate modeling.
method Analysis of case studies focusing on design patterns and workflow structure.
result Synthesis of workflow design patterns across diverse projects in ML-enabled climate modeling.
Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve data scientists from the tedious manual work. However, today's AutoAI systems often present on…
Adaptive workflow combines fast amortized inference with MCMC for many datasets.
problem Trade-off between computational speed and sampling accuracy in Bayesian inference.
method Adaptive workflow integrating amortized inference and MCMC with principled diagnostics.
result Efficiency gains with high posterior quality on tens of thousands of datasets.
Researchers found that avoiding synthetic data generation prevents model collapse in machine learning.
problem Model collapse in machine learning where models degenerate over generations.
method Comparing discard and augment workflows, focusing on Linear Regression.
result Theoretical evidence shows that for Linear Regression, test risk is bounded by π²/6 of original data alone.
SAGE generates subsurface velocity models from sparse well logs and seismic images.
problem Lack of high-quality subsurface velocity models due to limited data availability.
method Subsurface AI-driven geostatistical extraction using proxy posterior.
result SAGE produces geologically plausible and statistically accurate velocity realizations.
OOM-RL uses financial market losses to align AI agents in autonomous systems.
problem Constrained alignment of autonomous software agents in live financial markets.
method Deploying agents in live financial markets to enforce strict test-driven workflows.
result Final OOM-RL-aligned system achieved a stable equilibrium with an annualized Sharpe ratio of 2.06.
Audit financial machine learning workflows to detect spurious predictability.
problem Spurious predictability in financial machine learning models.
method Falsification audit testing predictive workflows against synthetic environments.
result Many apparent financial predictions are artifacts, not genuine.
The paper argues for prioritizing identifying structure over complex models for scientific discovery.
problem Underdetermination of mechanisms in high-dimensional data, leading to unreliable explanations.
method Proposes concrete standards for 'mechanistic ML' to avoid collapsing explanations.
result Large language models (LLMs) can collapse large equivalence classes of explanations, making it hard to distinguish between mechanisms.
New framework evaluates model explanations based on decision task improvement.
problem Evaluation of model explanations often misses practical value.
method Decision-theoretic framework quantifying three key values.
result Provides benchmarks and interprets human-AI decision support.
Survey connects XAI and surrogate modeling for better understanding of complex systems.
problem Opaque surrogate models hide input-output relationships, hindering decision-making.
method Synthesizes XAI techniques with surrogate modeling workflows.
result Surrogate models can be made more interpretable using XAI methods.
Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow d…
One of the impediments in advancing actuarial research and developing open source assets for insurance analytics is the lack of realistic publicly available datasets. In this work, we develop a workflow for synthesizing insurance datasets leveraging CTGAN, a recently proposed neural network architecture for generating …
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary cl…
Deep learning model improves seismic rock property estimation.
problem Estimating reservoir rock properties from seismic reflection data.
method Proposes a deep learning-based seismic inversion workflow that models seismic traces spatiotemporally.
result Achieves best performance on SEAM dataset with r2 coefficient of 79.77\% Deep learning speeds up pressure prediction in carbon storage reservoirs.
problem Accurately forecasting reservoir pressure in geologic carbon storage projects with sparse well data.
method Combining InSAR surface displacement data with deep learning and data assimilation techniques.
result Workflow can predict reservoir pressure with high efficiency and uncertainty quantification.
The present study provides a comparative assessment of non-invasive sensors as means of estimating the microbial contamination and time-on-shelf (i.e. storage time) of leafy green vegetables, using a novel unified spectra analysis workflow. Two fresh ready-to-eat green salads were used in the context of this study for …