The field of financial technology is witnessing significant developments, with a focus on improving the efficiency and reliability of trading systems. Researchers are exploring new approaches to optimize autodeleveraging mechanisms, which are crucial for maintaining solvency in perpetual futures markets. The direction of the field is moving towards more robust and fair mechanisms, with a emphasis on minimizing losses for traders while ensuring exchange solvency. Noteworthy papers include: Autodeleveraging: Impossibilities and Optimization, which provides a rigorous model of autodeleveraging and proves a fundamental trilemma, and proposes optimized mechanisms to navigate this trilemma. Orchestration Framework for Financial Agents, which presents a framework for democratizing financial intelligence and demonstrates its effectiveness in trading tasks. TradeTrap, which proposes a unified evaluation framework for stress-testing autonomous trading agents and highlights their potential vulnerabilities. AuditCopilot, which investigates the use of large language models for fraud detection in double-entry bookkeeping and shows promising results.