Researchers propose diffusion-guided learning to improve fraud detection in transaction networks despite sparse labels and imbalanced data, addressing real-world challenges in financial system secu...
Researchers propose probabilistic multi-agent systems to automate labor-intensive IT-Grundschutz audits under NIS-2 Directive, reducing manual documentation and validation burden for compliance.
Researcher evaluates Claude and ChatGPT on IPO financial analysis using S-1 filings, finding Finance Agent v2 inadequate for forward-looking IPO tasks; proposes automated rubric generation as impro...
Research paper evaluates frontier LLMs (Claude, ChatGPT) on IPO financial analysis tasks using automated rubric generation, extending Finance Agent v2 benchmarks to SEC S-1 filings and SpaceX IPO c...
VADAOrchestra combines LLM-based agentic reasoning with symbolic logic to enable adaptive, auditable decision-making in business processes—addressing a core tension in AI-driven accounting workflows.
VADAOrchestra combines symbolic logic with LLM-based reasoning to enable adaptive business workflows that maintain auditability—addressing a key gap for regulated industries like accounting and fin...
AgentFinVQA combines on-premise deployability with auditability for financial chart QA, addressing regulated auditors' need for explainable AI without accuracy compromise.
Stanford researchers release SEFD, a layout-faithful, open-source dataset of 4.4M SEC filings reconstructed into MultiMarkdown format for pretraining financial LLMs at scale.
Stanford researchers publish SEFD, an open-source dataset reconstructing SEC EDGAR filings into layout-faithful MultiMarkdown to address scarcity of high-quality long-context training data for fina...
arXiv paper argues agentic AI can reduce administrative burden and accelerate routine processes for small-to-medium companies through multi-step task planning and enterprise system integration.
Researchers propose VeriGraph, a framework to make LLM-based agents' outputs auditable by separating deterministic data computations from semantic reasoning, addressing a key challenge for accounti...
Academic paper quantifies autonomous AI agent failure risk through trace-level underwriting to enable profitable, insurable deployment in operational systems including accounting workflows.
Mojo, Modular's Python-like systems language, addresses the 30-year 'two-language tax' in quantitative finance where Python research models are rewritten in C++ for production, introducing numerica...
Researchers propose Green SARC, a governance framework that enforces financial and environmental cost controls within agentic AI loops before execution, addressing runaway spending in multi-agent s...
An audit of 1 billion tokens reveals a major gap between token compression rates (99%) and actual billing, suggesting potential overcharges or token counting inconsistencies in LLM API pricing.
AATF proposes open standard for recording AI agent reasoning to enable audit trails and explainability—critical for accounting firms deploying autonomous AI systems.
Software engineer proposes 30-item audit checklist for validating AI-generated code across programming languages, addressing quality and safety concerns in agent-driven development.
Blue J and CPA.com survey shows tax firms have moved from experimenting with AI to embedding it as core workflow—adoption rate nearly doubled year-over-year.
Researchers propose Sovereign Assurance Boundary (SAB), a certificate-based control system for AI agents managing high-stakes financial infrastructure, addressing audit and authorization gaps in no...
Researchers introduce Trace2Policy, an AI system that extracts tacit decision rules from audit and compliance experts, then iteratively refines them through error analysis—automating expert judgmen...