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This paper introduces DSA, an evidence-aware orchestration framework designed for multi-market stock research utilizing large language model (LLM) agents. The framework systematically organizes the workflow into stages such as evidence acquisition and model-routed analysis, ensuring that the generated reports are informed by structured context and risk safeguards. Key results demonstrate that while the implementation conforms to specific software contracts, it does not claim superiority in report quality or investment returns, highlighting the framework's focus on operational integrity over predictive performance.
DSA orchestrates LLM agents to enhance stock research by managing evidence and model capabilities, ensuring rigorous report generation without sacrificing operational integrity.
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.
