Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows
Score
78首次系统揭示LLM金融分析中的检索-整合鸿沟，提出工作流架构决定性观点，信息增量明显。
实验设计严谨，多模型多任务交叉验证，因果干预方法扎实，论证逻辑清晰。
为AI金融系统评估与工作流优化提供直接可操作的改进方向，对产业应用具有指导意义。
契合当前LLM在金融领域规模化应用的现实，评估缺陷亟待修正，时效性高。
来自arXiv预印本，作者研究方法正规，结论可复现，未发现明显偏倚。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper reveals a critical failure mode of LLMs in financial analysis and offers actionable workflow fixes, making it highly innovative and practically useful.
AI commentary
「This paper hits a blind spot in current AI finance products: we often score models by retrieval accuracy while forgetting whether they actually use that information in decisions. Teams building investment-research agents should pay special attention to this retrieval-integration gap—your system may pinpoint risk clauses yet completely ignore them in final judgments. I recommend auditing your workflow now for similar information eviction effects.」
Quick overview
This paper identifies a retrieval-integration gap in LLM-based financial analysis: even when models accurately retrieve risk disclosures, those disclosures may fail to influence investment judgments. By varying unrelated context from 2,000 to 128,000 tokens while holding focal-firm information fixed, the authors find that the influence of a risk disclosure on investment judgments falls to the experimental noise floor even as direct retrieval remains accurate. The pattern replicates across model families, judgment tasks, and experiments removing real disclosures from actual 10-K filings. Causal memory interventions show that compressed summaries and source-text lookup jointly transmit disclosures into judgments, yet workflow architecture determines whether transmission succeeds: chunk-and-summarize pipelines evict relevant information, while a targeted, structured restatement adjacent to the decision restores its influence. The study concludes that AI analyst performance is jointly determined by model capability and workflow architecture, and retrieval-based evaluations can certify systems whose investment judgments ignore information they demonstrably retrieved.
- Identifies a retrieval-integration gap: in long contexts, models can retrieve risk disclosures yet their judgments are unaffected.
- Robust across model families, judgment tasks, and experiments with real 10-K filings.
- Causal memory interventions show compressed summaries and source-text lookup jointly transmit disclosures, but workflow architecture determines integration success.
- Chunk-and-summarize pipelines evict relevant information, while targeted structured restatement near the decision restores influence.
Full text
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