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VERSA: Verbatim Extraction via Rephrasing and Self-Aggregation for Financial Causality

Financial causality detection,the task of identifying and extracting verbatim causal spans from financial narratives, remains a challenging problem in Natural Language Processing (NLP). Large Language Models (LLMs), while powerful reasoners, frequently paraphrase source text or produce imprecise span boundaries when used in zero-shot extraction settings, leading to poor Exact Match scores. In this paper, we present VERSA, our system for the FinCausal 2026 Shared Task, a multi-agent pipeline that integrates two complementary inference strategies: Rephrase-and-Respond (RaR) and Recursive Self-Aggregation (RSA). The pipeline decomposes the extraction task into five sequential stages, each handled by a specialised agent: (1) causal structure analysis, (2) question reformulation via RaR, (3) diverse candidate population generation, (4) iterative refinement through RSA, and (5) verbatim validation with word-boundary alignment. We evaluate our approach on both the English and Spanish subsets of the FinCausal 2026 dataset. An ablation study demonstrates the individual and combined ontributions of RaR and RSA, showing that the full pipeline substantially outperforms a zero-shot baseline in Exact Match and token-level F1.