The field of financial AI is moving towards the development of more sophisticated and specialized systems, particularly in areas such as financial question answering, cryptocurrency return prediction, and high-frequency trading. Researchers are exploring the use of multi-agent frameworks, meta-learning, and reinforcement learning to improve the performance of large language models (LLMs) in these domains. Notable papers include: A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs, which presents a framework that leverages role-based prompting to enhance performance on domain-specific QA. Meta-Learning Reinforcement Learning for Crypto-Return Prediction, which introduces a unified transformer-based architecture that unifies meta-learning and reinforcement learning to create a fully self-improving trading agent. QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading, which introduces a multi-agent LLM framework explicitly designed for high-frequency algorithmic trading. FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning, which presents a benchmark for realistic, open-domain financial search and reasoning. VCBench: Benchmarking LLMs in Venture Capital, which introduces a benchmark for predicting founder success in venture capital.