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DSA: Evidence-Aware LLM-Agent Orchestration for Mul... | AI Research

Key Takeaways

  • DSA: Evidence-Aware LLM-Agent Orchestration for Multi-Market Stock Research explained DSA is an orchestration framework designed to manage the complexities o...
  • 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.
  • The reference implementation includes six regional market paths, fifteen bundled Strategy Skills, hosted and local model routes, and multiple execution and delivery surfaces.
Paper AbstractExpand

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.

DSA: Evidence-Aware LLM-Agent Orchestration for Multi-Market Stock Research explained

DSA is an orchestration framework designed to manage the complexities of using Large Language Models (LLMs) for stock research. The system aims to solve the challenges of assembling heterogeneous financial data, identifying missing information, and controlling how AI-generated opinions influence final research reports.

How the framework works

The DSA workflow follows a structured sequence: evidence acquisition, context construction, model-routed analysis, and report generation. The system offers two operational profiles:

  • Default Report Profile: Focuses on standard report generation using shared evidence and model-routing services.

  • Agentic Profile: Uses specialized role-based parsers for core outputs and a "signal-eligibility partition" for Strategy Skill opinions. If the agents disagree, the system provides this information to a decision agent, which then applies a conservative risk override to the final output.
    The reference implementation supports six regional market paths, fifteen bundled Strategy Skills, and both local and hosted model routes.

Implementation and testing

The authors, Linsen Zhu and Yi Shi, evaluated the framework by testing its software architecture rather than its financial performance. They utilized a manifest of 1,457 portable offline backend contract tests. Of these, 596 cases were mapped to six core contract families that define the LLM-agent architecture.

Important considerations

The authors explicitly state that their testing establishes "implementation conformance" for the software contracts. The paper does not claim that the DSA framework produces superior report quality, improved forecasting accuracy, or better investment returns. The system is designed to provide a structured, evidence-aware process for managing LLM agents in a financial research context.

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