AI 中文总结
该研究提出DSA框架，基于LLM智能体实现多市场股票研究的证据感知编排，通过工作流组织、配置文件差异化处理及测试验证，确保系统实现一致性。
AI 中文摘要
大语言模型能够汇总金融信息，但可运行的股票研究系统必须首先整合异构证据、暴露不可用数据和模型能力，并控制生成观点对最终报告的影响。我们提出DSA，一种面向多市场股票研究的证据感知编排框架，基于大语言模型（LLM）智能体构建。DSA将工作流组织为证据获取、结构化上下文构建、模型路由分析、可选角色与策略技能推理，以及利用选定上下文和诊断信息生成报告。默认报告配置文件与可选智能体配置文件共享证据和模型路由服务，但使用配置文件特定的输出验证和风险保障措施。在智能体配置文件中，核心角色输出由角色专用解析器处理，而策略技能观点在合成前需经过额外的信号资格划分；分歧会明确提供给决策智能体，随后执行保守的风险覆盖。参考实现包含六条区域市场路径、十五种捆绑式策略技能、托管及本地模型路由，以及多种执行与交付界面。在冻结的软件快照下，选定的1457个可移植离线后端契约测试清单全部通过；596个案例被回溯映射到所报告的LLM智能体架构的六个核心契约族。该证据确立了被测软件契约的实现一致性，而非报告质量、预测准确性或投资回报的优越性。
英文摘要
Large language models can summarize financial information, but an operational stock-research system must first assemble heterogeneous evidence, expose unavailable data and model capabilities, and control how generated opinions affect a final report. We present DSA, an evidence-aware orchestration framework for multi-market stock research with large language model (LLM) agents. DSA organizes the workflow into evidence acquisition, structured context construction, model-routed analysis, optional role and Strategy Skill reasoning, and report generation with selected context and diagnostics. A default report profile and an optional agentic profile share evidence and model-routing services but use profile-specific output validation and risk safeguards. In the agentic profile, core role outputs are processed by role-specific parsers, whereas Strategy Skill opinions undergo an additional signal-eligibility partition before synthesis; disagreement is supplied explicitly to the decision agent, followed by a conservative risk override. The reference implementation includes six regional market paths, fifteen bundled Strategy Skills, hosted and local model routes, and multiple execution and delivery surfaces. At a frozen software snapshot, a selected manifest of 1,457 portable offline backend contract tests passed; 596 cases were retrospectively mapped to six contract families central to the reported LLM-agent architecture. This evidence establishes implementation conformance for the tested software contracts, not superior report quality, forecasting accuracy, or investment returns.
Comments6 pages, 2 figures, 3 tables. Code available at https://github.com/ZhuLinsen/daily_stock_analysis
