arXiv cs.AI论文
FinSkillBench：评估投资管理中的AI智能体与领域技能
本文提出FinSkillBench，一个用于评估语言模型智能体在投资管理任务中运用金融领域技能能力的基准。基准涵盖投资组合构建、风险管理和基本面分析三个领域，包含12个子任务和2603个任务片段。实验比较了无技能、预置技能包和自生成技能三种条件，发现预置技能包能显著提升性能（平均得分从0.366升至0.528），而自生成技能效果甚微且计算成本高。独立验证也复现了该模式，表明可靠的程序性技能对投资管理智能体至关重要。
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Abstract:Investment management is a high-stakes domain in which agentic AI systems must do more than generate plausible text. They must retrieve point-in-time data, assemble correct computational inputs, invoke specialized methods, and produce auditable structured outputs. We introduce FinSkillBench, an evaluation suite designed to measure whether language model agents can effectively use financial domain skills to solve investment management tasks. The benchmark spans three domains, portfolio construction, risk management, and fundamental analysis, and includes 12 subtasks with 2,603 task episodes. Each episode provides point-in-time inputs, hidden ground truth, and a task-specific this http URL compare three conditions: no skill, curated skill packages consisting of procedural documents and executable components, and self-generated skills in which the agent writes and reuses its own procedures within an episode. Across 9 models and a large-scale evaluation, curated skills consistently improve performance, raising mean scores from 0.366 to 0.528, with the largest gains in portfolio construction and risk management. In contrast, self-generated skills provide little benefit despite higher computational cost. An independent evaluation using a separate agent framework (Hermes Agent, 8 models, 5,280 episodes total) reproduces the directional pattern across all three domains, with the magnitude of skill effects varying by subtask and harness. These results showthat in investment management agents, access to reliable procedural skills can be as important as model choice, while naive self-generation of skills is often ineffective. We release the benchmark, evaluation tools, curated skill packages, and full trajectories to support further research.