arxiv: 2605.05409 · v1 · submitted 2026-05-06 · 💻 cs.AI · cs.CL

Recognition: unknown

Agentic Retrieval-Augmented Generation for Financial Document Question Answering

Yang Shu , Yingmin Liu , Zequn Xie

Authors on Pith no claims yet

Pith reviewed 2026-05-08 16:58 UTC · model grok-4.3

classification 💻 cs.AI cs.CL

keywords financial document QAagentic RAGretrieval-augmented generationprogram of thoughtnumerical reasoningself-verificationcontrastive retrieval

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The pith

An agentic RAG framework with iterative loops, contrastive retrieval, and code-based reasoning lifts accuracy on financial document question answering by 5 to 9 points.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces FinAgent-RAG to tackle multi-step numerical reasoning over scattered evidence in corporate filings, where tables, text, and footnotes must be combined accurately. Standard single-pass retrieval methods often fail on these compositional chains. The new framework replaces them with repeated retrieval-reasoning cycles that include self-verification, a specialized retriever tuned on hard financial negatives, executable Python code for arithmetic, and a router that scales effort to question difficulty. These changes matter because financial analysis depends on error-free calculation of values drawn from heterogeneous sources, and even modest accuracy gains can matter in high-stakes settings.

Core claim

FinAgent-RAG is an agentic RAG framework that orchestrates iterative retrieval-reasoning loops with self-verification for financial document QA. It adds a Contrastive Financial Retriever trained with hard negative mining, a Program-of-Thought module that emits executable Python code rather than relying on mental arithmetic, and an Adaptive Strategy Router that assigns resources according to question complexity. Across FinQA, ConvFinQA, and TAT-QA the system records execution accuracies of 76.81 percent, 78.46 percent, and 74.96 percent, exceeding the strongest prior baseline by 5.62 to 9.32 points while cutting API costs by 41.3 percent on FinQA and showing stability across four different LL

What carries the argument

The central mechanism is the orchestration of iterative retrieval-reasoning loops with self-verification, supported by a contrastive financial retriever, Program-of-Thought code generation for exact arithmetic, and an adaptive strategy router that allocates effort based on question complexity.

Load-bearing premise

That the iterative self-verification loops and Program-of-Thought code generation reliably produce correct multi-step numerical answers without introducing new errors from incorrect code or flawed retrieval, and that the reported gains generalize beyond the three benchmarks.

What would settle it

A new test collection of financial questions that require multi-table and footnote reasoning where the generated code or retrieved passages produce wrong arithmetic results and overall accuracy falls to or below the strongest baseline.

Figures

Figures reproduced from arXiv: 2605.05409 by Yang Shu, Yingmin Liu, Zequn Xie.

**Figure 1.** Figure 1: Motivating comparison between single-pass RAG and FinAgent-RAG on a financial CAGR question. Recent advances in agentic AI—where LLM-based agents iteratively plan, act, and reflect (Yao, Zhao, Yu, Du, Shafran, Narasimhan and Cao, 2023; Shinn, Cassano, Gopinath, Narasimhan and Yao, 2023)—offer a promising direction. Agentic RAG extends this paradigm with iterative retrievalreasoning loops (Singh, Ehtesham,… view at source ↗

**Figure 2.** Figure 2: Overall architecture of FinAgent-RAG. Formally, we seek: 𝑎 ∗ = arg max 𝑎 𝑃 (𝑎 ∣ 𝑞, ) (1) 3.2. System Overview Financial documents present unique preprocessing challenges due to their heterogeneous structure (narrative sections, structured tables, footnotes, appendices). We design a three-stage pipeline: (1) document parsing into typed segments (text, tables, headers); (2) table linearization using a he… view at source ↗

**Figure 3.** Figure 3: Training pipeline of the Contrastive Financial Retriever with four types of domain-specific hard negatives. • Entity-swap negatives: Passages about the same metric from a different entity (e.g., subsidiary vs. parent company). Given a query-passage pair (𝑞, 𝑑+) and a set of hard negatives {𝑑 − 1 , 𝑑− 2 , …, 𝑑− 𝑛 }, the contrastive loss is: contrast = − log 𝑒 sim(𝐞𝑞 ,𝐞𝑑+)∕𝜏 𝑒 sim(𝐞𝑞 ,𝐞𝑑+)∕𝜏 + ∑𝑛 𝑖=1𝑒 sim(… view at source ↗

**Figure 4.** Figure 4: Program-of-Thought (PoT) reasoning pipeline with sandboxed code execution. The overall verification decision is: 𝑣𝑘 = { ACCEPT if 𝑣suff ∧ 𝑣num ∧ 𝑣cross REJECT otherwise (14) When 𝑣𝑘 = REJECT, the Query Refiner generates refined sub-questions based on the verification feedback: 𝐒 (𝑘+1) = LLM(promptrefine, 𝑞, 𝑎𝑘 , 𝑣𝑘 , ) (15) This enables targeted re-retrieval addressing the specific deficiencies identified… view at source ↗

**Figure 5.** Figure 5: Effect of maximum iteration depth 𝐾 on FinQA execution accuracy. Y. Shu, Y. Liu and Z. Xie: Preprint submitted to Elsevier Page 20 of 19 view at source ↗

**Figure 6.** Figure 6: Error distribution before and after FinAgent-RAG on FinQA view at source ↗

**Figure 9.** Figure 9: Effect of confidence threshold 𝜃 on execution accuracy and average API calls view at source ↗

**Figure 8.** Figure 8: Per-question-type accuracy comparison between CRAG and FinAgent-RAG on FinQA view at source ↗

**Figure 11.** Figure 11: Structured prompt templates for FinAgent-RAG’s three reasoning modules: CoT (left), PoT (center), and SelfVerification (right). Y. Shu, Y. Liu and Z. Xie: Preprint submitted to Elsevier Page 22 of 19 view at source ↗

read the original abstract

Financial document question answering (QA) demands complex multi-step numerical reasoning over heterogeneous evidence--structured tables, textual narratives, and footnotes--scattered across corporate filings. Existing retrieval-augmented generation (RAG) approaches adopt a single-pass retrieve-then-generate paradigm that struggles with the compositional reasoning chains prevalent in financial analysis. We propose FinAgent-RAG, an agentic RAG framework that orchestrates iterative retrieval-reasoning loops with self-verification, specifically engineered for the precision requirements of financial numerical reasoning. The framework integrates three domain-specific innovations: (1) a Contrastive Financial Retriever trained with hard negative mining to distinguish semantically similar but numerically distinct financial passages, (2) a Program-of-Thought reasoning module that generates executable Python code for precise arithmetic rather than relying on error-prone LLM-based mental computation, and (3) an Adaptive Strategy Router that dynamically allocates computational resources based on question complexity, reducing API costs by 41.3% on FinQA while preserving accuracy. Extensive experiments on three benchmark datasets--FinQA, ConvFinQA, and TAT-QA--demonstrate that FinAgent-RAG achieves 76.81%, 78.46%, and 74.96% execution accuracy respectively, outperforming the strongest baseline by 5.62--9.32 percentage points. Ablation studies, cross-backbone evaluation with four LLMs, and deployment cost analysis confirm the framework's robustness and practical viability for financial institutions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

FinAgent-RAG adds a contrastive financial retriever, Program-of-Thought code execution, and an adaptive router to an agentic loop, with reported accuracy gains and cost cuts on standard benchmarks, but the reliability of the code generation and verification steps remains the open question.

read the letter

The paper's core contribution is a specific agentic RAG pipeline for financial QA that combines three pieces: a contrastive retriever trained on hard negatives to pull numerically distinct passages, a Program-of-Thought module that emits executable Python instead of free-form text, and an adaptive router that decides when to iterate or spend more tokens based on question complexity. It shows 76-78% execution accuracy on FinQA, ConvFinQA, and TAT-QA, beating the strongest baseline by 5-9 points, plus a 41% cost drop on one dataset. That combination is not in the cited prior work, and the practical angle on cost and multi-LLM testing is useful for anyone who actually deploys these systems on filings. The benchmarks are the right ones for the domain, and the ablation and backbone sweeps give some sense of robustness. The soft spot is exactly the one the stress-test flags: the gains rest on the claim that the generated code plus self-verification produces fewer arithmetic or logic errors than direct LLM reasoning or plain RAG. If the verifier misses off-by-one indexing, wrong joins on footnotes, or scoping bugs in the Python, the execution accuracy can look good while the underlying steps are still brittle. The abstract does not give error breakdowns, failure cases, or how often verification actually corrects versus reinforces mistakes, so it is hard to judge whether the improvements are stable or sensitive to prompt details. The baselines and data splits are also not visible in what we have, which makes the 5-9 point margin harder to interpret. This is worth a serious referee for the financial NLP crowd and for groups building production RAG agents; the method is concrete enough that reviewers can check the code-generation reliability directly. I would send it to review rather than desk-reject, with the expectation that the authors will need to add targeted diagnostics on the PoT and verification loop.

Referee Report

2 major / 2 minor

Summary. The paper claims to introduce FinAgent-RAG, an agentic RAG framework for financial document question answering that employs iterative retrieval-reasoning loops with self-verification. It incorporates a Contrastive Financial Retriever using hard negative mining, a Program-of-Thought module for generating executable Python code to handle precise arithmetic, and an Adaptive Strategy Router to optimize computational costs. Experiments on FinQA, ConvFinQA, and TAT-QA benchmarks yield execution accuracies of 76.81%, 78.46%, and 74.96%, respectively, exceeding the strongest baselines by 5.62 to 9.32 percentage points, with a reported 41.3% reduction in API costs on FinQA. Ablation studies and evaluations across multiple LLMs are mentioned to support robustness.

Significance. If the results are confirmed with rigorous controls, this approach could significantly improve the reliability of LLM-based systems for complex financial QA tasks involving numerical reasoning over heterogeneous documents. The cost savings and domain-specific adaptations make it particularly valuable for real-world applications in financial institutions, potentially setting a new standard for agentic systems in specialized domains.

major comments (2)

[Abstract and Results] The headline performance numbers (76.81% on FinQA, etc.) and improvements (5.62-9.32 pp) are stated without reference to specific baseline models, their scores, data split details, or statistical measures such as standard deviations from multiple runs. This information is essential to evaluate whether the gains are attributable to the proposed innovations rather than experimental artifacts.
[Methods section on Program-of-Thought and self-verification] The claim that the PoT code generation combined with iterative self-verification reliably avoids introducing new errors in multi-step financial calculations (such as table joins or ratio computations) is central to the performance gains. However, the manuscript does not provide a dedicated error analysis or examples of how the verification step identifies and corrects logic flaws in the generated code, leaving open the possibility that high execution accuracy masks underlying reasoning issues.

minor comments (2)

[Related Work] The discussion of existing RAG approaches could be expanded to include more recent agent-based methods for comparison.
[Tables and figures] Ensure all tables reporting accuracies include the full set of baselines and metrics for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights opportunities to improve the clarity and evidentiary support in our manuscript. We address each major comment below and will incorporate revisions to strengthen the presentation of results and the analysis of the Program-of-Thought module.

read point-by-point responses

Referee: [Abstract and Results] The headline performance numbers (76.81% on FinQA, etc.) and improvements (5.62-9.32 pp) are stated without reference to specific baseline models, their scores, data split details, or statistical measures such as standard deviations from multiple runs. This information is essential to evaluate whether the gains are attributable to the proposed innovations rather than experimental artifacts.

Authors: We agree that explicit references to baselines and statistical details would aid evaluation. The results section (Section 4) contains Tables 1-3 that report exact scores for all baselines (including ReAct, CoT, and domain-adapted RAG variants) on the standard benchmark splits. However, standard deviations from multiple runs were omitted. In the revised manuscript we will add a footnote in the abstract referencing the primary baselines and their scores, and we will augment the results tables with standard deviations computed over three independent runs with different random seeds. revision: yes
Referee: [Methods section on Program-of-Thought and self-verification] The claim that the PoT code generation combined with iterative self-verification reliably avoids introducing new errors in multi-step financial calculations (such as table joins or ratio computations) is central to the performance gains. However, the manuscript does not provide a dedicated error analysis or examples of how the verification step identifies and corrects logic flaws in the generated code, leaving open the possibility that high execution accuracy masks underlying reasoning issues.

Authors: We recognize that a dedicated error analysis with concrete examples would more directly substantiate the self-verification mechanism. The current manuscript supports the contribution of this component via ablation studies (Section 5.2) that quantify accuracy drops when self-verification is removed. To address the referee's concern, we will add a qualitative analysis subsection (or appendix) containing specific examples of logic errors in generated Python code (e.g., incorrect joins or ratio formulas) and illustrate how the iterative verification loop detects execution failures and triggers corrective re-generation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark evaluation of agentic RAG framework

full rationale

The paper proposes FinAgent-RAG with three components (Contrastive Financial Retriever, Program-of-Thought module, Adaptive Strategy Router) and reports execution accuracies on FinQA (76.81%), ConvFinQA (78.46%), and TAT-QA (74.96%). No equations, fitted parameters renamed as predictions, self-definitional loops, or load-bearing self-citations appear in the abstract or described framework. Central claims rest on external benchmark comparisons rather than quantities derived by construction from the paper's own inputs or prior self-referential results. The derivation chain is self-contained as an empirical proposal and evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that iterative retrieval-reasoning with code execution and self-verification improves compositional numerical reasoning over single-pass RAG; no free parameters or invented entities are described in the abstract.

axioms (1)

domain assumption Iterative retrieval-reasoning loops with self-verification improve accuracy on compositional financial questions
Invoked as the core motivation for moving beyond single-pass RAG.

pith-pipeline@v0.9.0 · 5561 in / 1249 out tokens · 73414 ms · 2026-05-08T16:58:53.284286+00:00 · methodology

discussion (0)

Reference graph

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author Yang, Y. , author Uy, M.C.S. , author Huang, A. , year 2020 . title FinBERT : A pretrained language model for financial communications . journal arXiv preprint arXiv:2006.08097

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author Zhu, F. , author Lei, W. , author Huang, Y. , author Wang, C. , author Zhang, S. , author Lv, J. , author Feng, F. , author Chua, T.S. , year 2021 . title TAT-QA : A question answering benchmark on a hybrid of tabular and textual content in finance , in: booktitle Proceedings of the 59th Annual Meeting of the Association for Computational Linguisti...

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