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Research Paper#Web3 RegTech, Cryptocurrency, AML/CFT Compliance🔬 ResearchAnalyzed: Jan 3, 2026 06:23

SoK: Web3 RegTech for Cryptocurrency VASP AML/CFT Compliance

Published:Dec 31, 2025 14:31
1 min read
ArXiv

Analysis

This paper provides a systematic overview of Web3 RegTech solutions for Anti-Money Laundering and Counter-Financing of Terrorism compliance in the context of cryptocurrencies. It highlights the challenges posed by the decentralized nature of Web3 and analyzes how blockchain-native RegTech leverages distributed ledger properties to enable novel compliance capabilities. The paper's value lies in its taxonomies, analysis of existing platforms, and identification of gaps and research directions.
Reference

“Web3 RegTech enables transaction graph analysis, real-time risk assessment, cross-chain analytics, and privacy-preserving verification approaches that are difficult to achieve or less commonly deployed in traditional centralized systems.”

PermalinkArXiv
Research#Job Matching🔬 ResearchAnalyzed: Jan 10, 2026 13:24

Improving Job Matching with ESCO and EQF for Skills and Qualifications

Published:Dec 2, 2025 19:49
1 min read
ArXiv

Analysis

This ArXiv paper likely explores the application of ESCO (European Skills, Competences, Qualifications and Occupations) and EQF (European Qualifications Framework) taxonomies to enhance job matching processes. The research's potential lies in standardizing and improving the accuracy of linking skills, occupations, and qualifications, but its impact needs to be assessed based on the specific methodologies and results presented.
Reference

“The paper leverages ESCO and EQF taxonomies.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 29, 2025 06:06

RAG Risks: Why Retrieval-Augmented LLMs are Not Safer with Sebastian Gehrmann

Published:May 21, 2025 18:14
1 min read
Practical AI

Analysis

This article discusses the safety risks associated with Retrieval-Augmented Generation (RAG) systems, particularly in high-stakes domains like financial services. It highlights that RAG, despite expectations, can degrade model safety, leading to unsafe outputs. The discussion covers evaluation methods for these risks, potential causes for the counterintuitive behavior, and a domain-specific safety taxonomy for the financial industry. The article also emphasizes the importance of governance, regulatory frameworks, prompt engineering, and mitigation strategies to improve AI safety within specialized domains. The interview with Sebastian Gehrmann, head of responsible AI at Bloomberg, provides valuable insights.
Reference

“We explore how RAG, contrary to some expectations, can inadvertently degrade model safety.”

PermalinkPractical AI