A new cs.AI paper introduces OpenFinGym, a multi-task gym environment for evaluating LLM agents in quantitative finance.
The authors argue that existing finance benchmarks are fragmented across isolated tasks, while real workflows connect forecasting, portfolio construction, risk management, and execution. A more integrated testbed can expose failures that single-task benchmarks miss.
The project reflects a wider move toward verifiable agent environments where multi-step decisions can be tested against domain-relevant outcomes.