# FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering

arXiv: https://arxiv.org/abs/2601.06992 · HTML: https://mlsanity.com/paper/2601.06992/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 11 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [FinanceBench: A New Benchmark for Financial Question Answering](https://mlsanity.com/paper/2311.11944/) (2023)
- [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023)
- [ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://mlsanity.com/paper/2210.03849/) (2022)
- [A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models](https://mlsanity.com/paper/2310.09497/) (2023)
- [TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy](https://mlsanity.com/paper/2406.11678/) (2024)
- [FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering](https://mlsanity.com/paper/2508.14052/) (2025)
- [Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance](https://mlsanity.com/paper/2502.18772/) (2025)
- [MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models](https://mlsanity.com/paper/2509.23725/) (2025)
- [Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization](https://mlsanity.com/paper/2510.06732/) (2025)
- [Modeling Uncertainty Trends for Timely Retrieval in Dynamic RAG](https://mlsanity.com/paper/2511.09980/) (2025)
- [Beyond Isolated Investor: Predicting Startup Success via Roleplay-Based Collective Agents](https://mlsanity.com/paper/2512.22608/) (2025)
- [The Gaining Paths to Investment Success: Information-Driven LLM Graph Reasoning for Venture Capital Prediction](https://mlsanity.com/paper/2512.23489/) (2025)

## Built on by (most cited first, 16 of 16)

- [FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosures](https://mlsanity.com/paper/2604.05966/) (2026)
- [Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS](https://mlsanity.com/paper/2604.10734/) (2026)
- [Data Selection for Multi-turn Dialogue Instruction Tuning](https://mlsanity.com/paper/2604.07892/) (2026)
- [Instruction Data Selection via Answer Divergence](https://mlsanity.com/paper/2604.10448/) (2026)
- [RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game](https://mlsanity.com/paper/2604.10740/) (2026)
- [The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems](https://mlsanity.com/paper/2602.10886/) (2026)
- [SQLStructEval: Structural Evaluation of LLM Text-to-SQL Generation](https://mlsanity.com/paper/2604.06736/) (2026)
- [FinPersona-Bench: A Benchmark for Longitudinal Psychometric Stability of Autonomous Financial Agents](https://mlsanity.com/paper/2606.31522/) (2026)
- [FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filings](https://mlsanity.com/paper/2608.07400/) (2026)
- [Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering](https://mlsanity.com/paper/2607.19856/) (2026)
- [Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering](https://mlsanity.com/paper/2607.19867/) (2026)
- [Reconcile Once, Write Anytime: A Trust-Tiered Librarian and a Multi-Agent Writer for Drift-Free, Point-in-Time Research](https://mlsanity.com/paper/2608.12984/) (2026)
- [What the Reranker Sees: Multi-Aspect Page Annotation for Long-Document Multimodal Question Answering](https://mlsanity.com/paper/2608.14841/) (2026)
- [FinExam-10K: When Retrieval Helps Financial Reasoning?](https://mlsanity.com/paper/2608.28155/) (2026)
- [Right Frame, Wrong Rule: Cultural Cues Expose the Financial Knowledge Gap They Were Meant to Close](https://mlsanity.com/paper/2609.00999/) (2026)
- [FinCUABuild: Can Agents Build Reliable Benchmarks for Dynamic Financial Computer Use?](https://mlsanity.com/paper/2609.07603/) (2026)

## Abstract (arXiv)

Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics, fiscal periods, and numeric values. However, existing LLM-based rerankers primarily optimize semantic relevance, leading to unstable rankings and opaque decisions on long documents. We propose FinCards, a structured reranking framework that reframes financial evidence selection as constraint satisfaction under a finance-aware schema. FinCards represents filing chunks and questions using aligned schema fields (entities, metrics, periods, and numeric spans), enabling deterministic field-level matching. Evidence is selected via a multi-stage tournament reranking with stability-aware aggregation, producing auditable decision traces. Across two corporate filing QA benchmarks, FinCards substantially improves early-rank retrieval over both lexical and LLM-based reranking baselines, while reducing ranking variance, without requiring model fine-tuning or unpredictable inference budgets. Our code is available at https://github.com/XanderZhou2022/FINCARDS.