SOURCE-LINKED INTELLIGENCE
DSA: Evidence-Aware LLM-Agent Orchestration for Multi-Market Stock Research
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 def
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T11:37:13.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.