The field is moving towards quantum-secure audit trails and advanced cryptocurrency analysis, with a focus on post-quantum instantiations and migration strategies for deployed audit logs. Recent research has introduced new frameworks and benchmarks for evaluating the capabilities of Large Language Model (LLM) agents in cryptocurrency analysis, highlighting the need for more advanced analytical capabilities. Noteworthy papers include: CryptoBench, which provides a dynamic benchmark for expert-level evaluation of LLM agents in cryptocurrency. Leveraging Large Language Models to Bridge On-chain and Off-chain Transparency in Stablecoins, which introduces a framework for automated analysis of on-chain and off-chain data in stablecoins. CryptoQA, which presents a large-scale question-answering dataset for AI-assisted cryptography and evaluates the performance of state-of-the-art LLMs on cryptographic tasks.