Document-Level Numerical Reasoning across Single and Multiple Tables in Financial Reports

Chu-Song Chen; Wei-An Wang; Yi-Cheng Wang

REVIEW 3 major objections 2 minor 13 references

Document-Level Numerical Reasoning across Single and Multiple Tables in Financial Reports

T0 review · 3 major / 2 minor · reviewed 2026-07-13 · grok-4.5

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Pith's one-line read A multi-agent multi-round RAG system improves reliable numerical QA over long financial reports by iterating retrieval, intermediate calculation, and verification.

desk verdict We only have the FinLongDocQA abstract; the attached full text is a different paper (Explainable PQC), so the dataset and agent claims cannot be audited. read the letter →

arxiv 2604.03664 v1 submitted 2026-04-04 cs.CL

Yi-Cheng Wang , Wei-An Wang , Chu-Song Chen This is my paper · ORCID

classification cs.CL

keywords document-levelnumericalreasoningfinancialreportscross-tableQAlong-contextLLMsretrieval-augmentedgenerationmulti-agentsystemsFinLongDoccontextrot

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Large language models still fail at trustworthy numerical question answering over long, structured financial annual reports, where answers require arithmetic over evidence scattered across multiple tables and narrative text. Existing benchmarks mostly stay in single-table settings, so the authors introduce FinLongDocQA to cover both single-table and cross-table numerical reasoning inside full-length reports. Evaluating closed- and open-source LLMs on this dataset surfaces two bottlenecks: reports often exceed roughly 129k tokens, which worsens context rot when locating the right tables, and even after evidence is found models still err on multi-step arithmetic. FinLongDocAgent addresses both problems with a multi-agent multi-round retrieval-augmented generation loop that repeatedly retrieves, computes intermediate results, and verifies them. Experiments show that this iterative retrieve–calculate–verify pattern is essential for more reliable numerical QA on long financial documents.

What carries the argument

FinLongDocAgent: a Multi-Agent Multi-Round RAG pipeline that loops over evidence retrieval, intermediate numerical calculation, and cross-round verification rather than answering in a single pass.

What would settle it

Re-run the same models and agent on a held-out set of real analyst-written cross-table questions from full annual reports (with independent human numerical ground truth) and check whether the iterative retrieve–calculate–verify loop still yields the claimed accuracy lift over single-pass RAG and raw long-context LLMs.

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Extended reading notes

Core claim

FinLongDocQA exposes that document-level financial numerical reasoning fails for two distinct reasons—context rot when locating relevant tables in reports longer than about 129k tokens, and multi-step arithmetic errors even after the right evidence is retrieved—and that a multi-agent multi-round RAG agent that iteratively retrieves evidence, performs intermediate calculations, and verifies results measurably improves reliability on both single-table and cross-table questions.

Load-bearing premise

The dataset’s questions and scoring truly reflect how analysts do cross-table numerical work, and the reported gains are not artifacts of how retrieval, baselines, or answer normalization were set up.

Editorial extensions

If this is right

Benchmarks that stop at single tables will systematically understate failure modes that appear only when evidence is scattered across a full report.
Simply stuffing an entire annual report into a long context window is insufficient; locating the right tables remains a first-order failure mode above roughly 129k tokens.
Even perfect table retrieval is not enough: multi-step arithmetic still needs intermediate calculation and verification steps.
Agent designs for financial QA should treat retrieval, calculation, and verification as separate iterative stages rather than a single generation pass.
The same iterative pattern is a concrete direction for other long, table-heavy document domains that demand exact numerical answers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If context rot is the dominant retrieval failure, hybrid table-index or layout-aware retrieval may reduce the need for very long raw contexts more than larger context windows alone.
Separating calculation into an explicit, checkable intermediate step suggests numerical QA systems may benefit from tool-use or program-of-thought style execution rather than pure free-form generation.
Cross-table financial QA is a natural stress test for any long-context or agentic RAG claim, because wrong table selection and arithmetic drift are independently measurable.
Future work could ablate which agent role (retriever vs. calculator vs. verifier) contributes most of the gain to isolate whether the multi-agent design is necessary or whether multi-round single-agent verification would suffice.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The package is broken in a simple way: the target is arXiv 2604.03664 (FinLongDocQA / FinLongDocAgent), but the full manuscript body is Explainable PQC (2604.03665). Treat this as abstract-only for the financial QA paper. Do not read the PQC text as evidence for Wang et al.

From the abstract alone, the intended contribution is clear and useful for a narrow audience. Existing financial numerical QA suites are mostly single-table. They claim a long-report dataset covering single- and cross-table numerical reasoning, diagnose two bottlenecks (context rot past ~129k tokens when finding tables; multi-step arithmetic errors even after evidence is found), and propose a multi-agent multi-round RAG loop that retrieves, computes intermediate values, and verifies across rounds. That problem framing matches real analyst work better than single-table suites, and iterative retrieve–calculate–verify is a sensible engineering pattern for this setting.

What is new is the setting and the dataset claim, not the agent architecture. Multi-round agentic RAG with tools and verification is an expected extension of current RAG/tool-use lines. The abstract’s “experiments highlight importance of iterative retrieval and verification” is the load-bearing result, and we cannot check it: no N, no single- vs cross-table split, no annotation protocol, no metrics, no baselines, no ablations, no answer normalization, no release artifacts. The weakest assumption is that FinLongDocQA questions and labels faithfully track real cross-table analysis and that reported gains are not setup artifacts. That is unchecked, not disproven.

Who this is for: financial NLP and long-context RAG people, if and when the real paper, data, and code appear. From this package, there is nothing to run, cite, or stress-test. A serious editor would send a complete FinLongDocQA paper with proper evaluation to referees; they would desk-reject this mismatched submission. Recommendation: do not engage further until the correct full manuscript and artifacts are available. Then re-score from the experiments, not the abstract.

Referee Report

3 major / 2 minor

Summary. The submission under review is titled and abstracted as FinLongDocQA / FinLongDocAgent: a dataset and multi-agent multi-round RAG system for single- and cross-table numerical reasoning over long financial annual reports, claiming two bottlenecks (context rot beyond ~129k tokens; multi-step arithmetic errors after evidence is found) and gains from iterative retrieval, intermediate calculation, and verification. The full manuscript body supplied with the submission is, however, an entirely different paper—“Explainable PQC,” a conceptual layered interpretive framework for lattice-based post-quantum cryptography (complexity vocabulary, exploratory combinatorial Hodge theory, and low-dimensional Julia LLL/BKZ experiments)—with no FinLongDocQA data, annotation protocol, baselines, ablations, or numerical-QA results. Consequently the claimed dataset, bottlenecks, and agent improvements cannot be audited from the materials provided.

Significance. If the FinLongDocQA claims were supported by a matching manuscript (dataset statistics, annotation process, single- vs. cross-table splits, metrics, baselines, ablations, and answer-normalization protocol), the work would address a genuine gap in long-document financial numerical reasoning and could be a useful empirical contribution. As submitted, that significance cannot be assessed: the body is a different paper whose own contribution is conceptual/organizational rather than a new hardness result or attack, and it does not substantiate any of the abstract’s ML claims. No machine-checked proofs, reproducible FinLongDocQA code, or falsifiable numerical-QA results for the stated paper are present in the provided text.

major comments (3)

Title/abstract vs. full text mismatch: the abstract and paper_id claim FinLongDocQA and FinLongDocAgent (cs.CL financial numerical QA), but the full manuscript is “Explainable PQC” (arXiv:2604.03665, lattice PQC interpretability). No section of the body defines FinLongDocQA, reports N, single/cross-table splits, annotation, metrics, baselines, or agent ablations. The central claims (two bottlenecks; multi-agent multi-round RAG gains) are therefore unauditable and load-bearing evidence is missing.
Even restricting attention to the abstract alone, the evaluation protocol is unspecified: no dataset size, construction/annotation process, answer-normalization rules, retrieval setup, or comparison baselines. Without these, neither the “context rot beyond ~129k tokens” bottleneck nor the claimed benefit of iterative retrieval/verification can be checked for artifacts—the weakest assumption flagged for this paper remains unchecked.
If the intended submission were instead the provided Explainable PQC body, its own central claim (a layered interpretive framework for PQC communication) is explicitly non-proof and non-parameter; Sections 3–4 remain exploratory/illustrative (low-dim 10–40 lattices far below NIST ranges; Hodge/local-generation link to hardness left open). That paper does not support the FinLongDocQA abstract and cannot substitute for it.

minor comments (2)

Abstract of the claimed paper is clear on motivation but gives no quantitative preview (dataset size, accuracy deltas, model list), which would normally be expected even in a short abstract for an empirical systems paper.
In the mismatched body (Explainable PQC), Figure 1 and Tables 1–2 are useful for the interpretive triple (Sc, Sq, Sr), but the Julia toolkit results (Figure 3, 10D/40D) need clearer units and hardware details if that paper were under review separately.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation in FinLongDocQA/Agent abstract; wrong full text (Explainable PQC) also shows no load-bearing circularity.

full rationale

The target paper (FinLongDocQA / FinLongDocAgent) is available only as an abstract: it introduces a long-document financial numerical-reasoning dataset, reports two empirical bottlenecks (context rot beyond ~129k tokens; multi-step arithmetic errors after evidence is found), and proposes a multi-agent multi-round RAG agent with intermediate calculation and verification. That pipeline is a standard empirical ML contribution. There is no equation, fitted parameter, uniqueness theorem, or self-citation chain that forces the claimed bottlenecks or agent gains by construction; nothing in the abstract reduces a “prediction” to its own inputs. The CACHEABLE full manuscript is a different paper (Explainable PQC, arXiv 2604.03665), which is an explicitly scoped interpretive framework that repeatedly disclaims new hardness results, reductions, or security estimates; its three layers (complexity vocabulary, exploratory Hodge/fan geometry, low-dimensional Julia LLL/BKZ illustrations) are presented as communication tools, not as first-principles derivations of cryptographic security, so they likewise exhibit no self-definitional or fitted-as-prediction circularity. Residual risks (benchmark construction fidelity, unstated baselines, answer normalization) are evaluation-validity concerns, not circularity under the stated patterns. Score 0; steps empty.

Assumptions & free parameters 0 free parameters · 4 assumptions · 2 invented entities

Abstract-only review of an empirical NLP systems paper. Load-bearing background is standard domain practice (LLMs struggle on long structured numerical QA; RAG and multi-agent loops can help; financial reports mix tables and text). No free parameters or fitted constants appear in the abstract. Invented entities are the dataset and agent names. No formal hardness axioms or physical constants are involved.

assumptions (4)

domain assumption Existing financial/table QA benchmarks are largely single-table and therefore under-test cross-table document-level numerical reasoning.
Stated as the gap motivating FinLongDocQA; not evidenced in the abstract itself.
domain assumption Annual reports often exceed ~129k tokens and thereby induce context-rot failures when locating relevant tables.
Empirical premise used to define bottleneck (1); no measurement protocol in the abstract.
domain assumption Even with relevant evidence located, LLMs remain prone to multi-step numerical reasoning errors on financial indicators.
Bottleneck (2); treated as established evaluation finding without numbers in the abstract.
ad hoc to paper Iterative multi-agent retrieval, intermediate calculation, and multi-round verification improve reliability over one-shot LLM answering on this task.
Central methodological claim of FinLongDocAgent; depends on unreported experimental design.

invented entities (2)

FinLongDocQA
purpose: Dataset for single-table and cross-table financial numerical reasoning in long-context annual reports.
Named contribution; construction details, size, and labels not available in the abstract.
FinLongDocAgent
purpose: Multi-Agent Multi-Round RAG system that iteratively retrieves evidence, computes intermediates, and verifies answers.
Named system contribution; architecture and gains not inspectable from abstract-only materials.

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Cite this review

Pith. "Pith review of Document-Level Numerical Reasoning across Single and Multiple Tables in Financial Reports." pith.science (2026). https://pith.science/paper/2604.03664

@misc{pith2026260403664,
  author       = {Pith},
  title        = {Pith review of: Document-Level Numerical Reasoning across Single and Multiple Tables in Financial Reports},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.03664}},
  note         = {Machine review of arXiv:2604.03664}
}

read the original abstract

Despite the strong language understanding abilities of large language models (LLMs), they still struggle with reliable question answering (QA) over long, structured documents, particularly for numerical reasoning. Financial annual reports exemplify this difficulty: financial statement analysis often hinges on accurate arithmetic, and analysts derive key indicators by integrating evidence scattered across multiple tables and narrative text. However, existing benchmarks focus largely on single-table settings, leaving cross-table document-level numerical reasoning underexplored. To address this gap, we introduce FinLongDocQA, a dataset for both single-table and cross-table financial numerical reasoning in long-context reports. Evaluating both closed-source and open-source LLMs on FinLongDocQA reveals two bottlenecks: (1) annual reports often exceed 129k tokens, exacerbating the context rot problem for locating relevant tables; and (2) even when relevant evidence is located, LLMs remain prone to errors in multi-step numerical reasoning. We propose FinLongDocAgent, a Multi-Agent Multi-Round Retrieval-Augmented Generation (RAG) approach that iteratively retrieves evidence, performs intermediate calculations, and verifies results across rounds. Experiments highlight the importance of iterative retrieval and verification for reliable numerical QA in long financial documents.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed July 13, 2026 · model on record in the stance chip above.

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O. Regev, “On lattices, learning with errors, random linear codes, and cryptography,”Journal of the ACM, vol. 56, no. 6, Art. 34, 2009. (Preliminary version inProc. 37th ACM STOC, pp. 84–93, 2005.) doi: 10.1145/1568318.1568324

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C.-P. Schnorr and M. Euchner, “Lattice basis reduction: Improved practical algorithms and solving sub- set sum problems,”Mathematical Programming, vol. 66, pp. 181–199, 1994.doi: 10.1007/BF01581144

doi:10.1007/bf01581144 1994