LLMs are applied in a generative pipeline for extracting, normalizing, and interpreting eligibility criteria from securities prospectuses, achieving up to 91% precision in document-level decisions with a conservative bias.
arXiv preprint arXiv:2511.08621 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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FinAcumen introduces selective experience memory that distills prior trajectories into reusable strategies and cautionary rules to improve tool-augmented multimodal financial reasoning.
citing papers explorer
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LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank
LLMs are applied in a generative pipeline for extracting, normalizing, and interpreting eligibility criteria from securities prospectuses, achieving up to 91% precision in document-level decisions with a conservative bias.
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FinAcumen: Financial Multimodal Reasoning via Self-Evolving Experience Memory Harness
FinAcumen introduces selective experience memory that distills prior trajectories into reusable strategies and cautionary rules to improve tool-augmented multimodal financial reasoning.