arxiv: 2605.05811 · v1 · submitted 2026-05-07 · 💻 cs.AI

Recognition: unknown

Sheet as Token: A Graph-Enhanced Representation for Multi-Sheet Spreadsheet Understanding

Bo Guan, Chunhui Wang, Depei Zhu, Tianyu Shi, Yiming Lei, Yiqi Wang, Yujia Zhang, Zhuonan Hao

Authors on Pith no claims yet

Pith reviewed 2026-05-08 11:20 UTC · model grok-4.3

classification 💻 cs.AI

keywords multi-sheet spreadsheetsheet tokenizationgraph retrievercross-sheet reasoningworkbook understandingschema-aware encodinglistwise retrievalretrieval-augmented analysis

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The pith

Treating each worksheet as one compact token linked in a query-specific graph improves retrieval of relevant sheets from multi-sheet spreadsheets.

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

The paper proposes representing each entire worksheet as a single dense token instead of fragmenting it into smaller text pieces. This token is created by pulling out schema-aware details such as sheet names, column headers, sample values, and layout patterns. A graph retriever then builds connections between these sheet tokens using semantic similarity, schema matches, and shape compatibility to locate the right group of sheets for a natural-language query. Experiments on a multi-sheet corpus indicate that the tokenization produces consistent representations and that the graph stage lifts retrieval quality over simpler baselines while adding little computation. If the approach holds, AI systems could analyze full workbooks more reliably without losing the overall meaning of each sheet.

Core claim

Sheet as Token encodes every worksheet into a compact dense token drawn from its name, headers, representative values, and layout features; a Graph Retriever then assembles a query-specific candidate graph over these tokens through semantic, schema-consistency, and shape-compatibility channels and processes the graph with a multi-stage transformer to retrieve supporting sheet sets.

What carries the argument

The sheet token, a unified dense encoding of an entire worksheet that preserves global semantics and serves as the atomic unit for graph-based cross-sheet reasoning.

If this is right

Sheet-level tokenization produces stable representations of individual worksheets.
Graph-enhanced cross-sheet reasoning improves listwise retrieval over a shallow graph baseline.
The performance gain occurs with only limited additional computation on the graph side.
The unified sheet tokens maintain global semantics better than chunk-centric decompositions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same sheet-token abstraction could reduce context loss when language models process large workbooks that exceed typical input limits.
Multiple relation channels in the candidate graph may surface implicit dependencies between sheets that text-only retrieval overlooks.
The approach points toward treating other multi-part documents, such as reports divided into sections, as unified tokens for relational reasoning.

Load-bearing premise

The extracted schema-aware records from sheet names, column headers, representative values, and layout features are sufficient to encode the full semantic content of each worksheet for effective cross-sheet reasoning.

What would settle it

A retrieval test on spreadsheets where key semantic links reside in unextracted formulas or detailed cell patterns beyond the representative values and layout features, causing the method to miss relevant sheets that a full-content reader would select.

Figures

Figures reproduced from arXiv: 2605.05811 by Bo Guan, Chunhui Wang, Depei Zhu, Tianyu Shi, Yiming Lei, Yiqi Wang, Yujia Zhang, Zhuonan Hao.

**Figure 1.** Figure 1: Overview of the Sheet as Token framework. Raw workbook sheets are first converted into schema-aware records, encoded into dense Sheet Tokens, and then processed by a query-specific Graph Retriever to identify supporting sheet sets. This raises a natural question: if multi-sheet understanding ultimately requires identifying relevant worksheets, should the sheet itself be treated as the basic semantic unit f… view at source ↗

**Figure 2.** Figure 2: Transformation of a raw spreadsheet into a schema-aware semantic record. The feature extractor view at source ↗

**Figure 3.** Figure 3: Architecture of the two-stage retrieval framework. view at source ↗

**Figure 4.** Figure 4: Training dynamics of the full two-stage retrieval pipeline. The left plot shows accuracy, and the view at source ↗

**Figure 5.** Figure 5: Data preparation pipeline. Real-world and synthetically generated tables are processed into three view at source ↗

read the original abstract

Workbook-scale spreadsheet understanding is increasingly important for language-model-based data analysis agents, but remains challenging because relevant information is often distributed across multiple sheets with heterogeneous schemas, layouts, and implicit relationships. Existing retrieval-augmented approaches typically decompose spreadsheets into rows, columns, or blocks to improve scalability; however, such chunk-centric representations can fragment worksheets into isolated text spans and weaken global sheet-level semantics. We propose Sheet as Token, a graph-enhanced framework that treats each worksheet as a unified semantic unit for multi-sheet spreadsheet retrieval. Our method extracts schema-aware records from sheet names, column headers, representative values, and layout features, and encodes each worksheet into a compact dense token. Given a natural-language query, a Graph Retriever constructs a query-specific candidate graph over sheet tokens using semantic, query-conditioned, schema-consistency, and shape-compatibility relations, and composes these channels through a multi-stage graph transformer to retrieve supporting sheet sets. Experiments on a constructed multi-sheet spreadsheet corpus show that sheet-level tokenization learns stable representations, and that graph-enhanced cross-sheet reasoning improves listwise retrieval over a shallow graph baseline with limited additional graph-side computation. These results suggest that sheet-level tokenization is a promising abstraction for scalable multi-sheet spreadsheet understanding.

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

The sheet-as-token approach with a multi-relation graph retriever is a reasonable shift from chunking for multi-sheet spreadsheets, but the experiments are described too lightly to confirm the gains.

read the letter

The main takeaway is that this paper frames each worksheet as a single token rather than breaking it into rows or blocks, then routes queries through a graph that mixes semantic, schema, and shape edges. That move targets a real issue for language-model agents that lose context when spreadsheets get large and spread out across sheets. The extraction step pulls sheet names, headers, sample values, and layout features to build compact representations, and the retriever composes those channels with a graph transformer. This combination at sheet granularity is the clearest new element compared with prior chunk-based retrieval work. It does a clean job of naming the fragmentation problem and showing how explicit relations can help cross-sheet reasoning without much extra compute on the graph side. The idea is practical and could feed into downstream tools that need to pull relevant sheets for analysis tasks. The soft spots sit mostly in the evaluation. The abstract states that sheet-level tokens produce stable representations and that the graph improves listwise retrieval over a shallow baseline on a constructed corpus, yet it supplies no concrete baselines, metrics, numbers, or checks for statistical significance. Without those, it is hard to judge whether the reported edge comes from the graph or from how the corpus was built. The extraction of surface features also looks like the weakest link, since many spreadsheets carry important logic in formulas, named ranges, or conditional formatting that the listed records would miss. A constructed corpus raises the further risk that the test cases already favor the method. This paper is aimed at people building retrieval components for spreadsheet-aware AI systems or data-analysis agents. A reader who works on structured-data retrieval might borrow the multi-channel graph design, but anyone needing reliable performance numbers should look elsewhere until more details appear. The work shows clear thinking about the problem and honest engagement with existing retrieval ideas, so it deserves a serious referee to examine the full experiments, the corpus details, and whether the extraction step holds up on real workbooks. I would send it to peer review with requests for expanded evaluation and tests on public spreadsheet collections.

Referee Report

2 major / 1 minor

Summary. The paper proposes 'Sheet as Token', a graph-enhanced framework for multi-sheet spreadsheet understanding that treats each worksheet as a unified semantic token. It extracts schema-aware records from sheet names, column headers, representative values, and layout features to encode worksheets into dense tokens, then uses a query-conditioned Graph Retriever to build a candidate graph with semantic, schema-consistency, and shape-compatibility edges, composed via a multi-stage graph transformer for listwise retrieval. Experiments on a constructed multi-sheet spreadsheet corpus claim that sheet-level tokenization yields stable representations and that the graph-enhanced approach improves retrieval over a shallow graph baseline with limited extra computation.

Significance. If the extraction method robustly captures semantics and the experimental claims are supported by rigorous controls, this could advance scalable retrieval for workbook-scale spreadsheet agents by avoiding fragmentation from row/column chunking and enabling better cross-sheet reasoning. The graph transformer composition of multiple relation channels is a potentially reusable idea for structured data retrieval.

major comments (2)

[Abstract] Abstract and Experiments section: The abstract states that experiments show 'stable representations' and 'improved listwise retrieval' over a shallow graph baseline, but provides no details on the baselines, metrics (e.g., nDCG, recall@K), error bars, statistical significance tests, or biases in corpus construction. These omissions are load-bearing because the central claim of practical improvement cannot be assessed without them.
[Method] Method section (record extraction description): The framework relies on the claim that schema-aware records extracted from sheet names, column headers, representative values, and layout features are sufficient to encode full worksheet semantics for stable tokens and effective graph reasoning. This is unvalidated in the provided text and is load-bearing, as spreadsheets frequently embed critical semantics in formulas, named ranges, conditional logic, and inter-cell dependencies that are not recoverable from the listed surface features; omission risks retrieving syntactically compatible but semantically incomplete sheet sets.

minor comments (1)

[Abstract] Abstract: The phrase 'with limited additional graph-side computation' is vague; a brief quantification or comparison (e.g., FLOPs or runtime) would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the presentation of our contributions. We address each major point below and indicate the revisions we will make to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract] Abstract and Experiments section: The abstract states that experiments show 'stable representations' and 'improved listwise retrieval' over a shallow graph baseline, but provides no details on the baselines, metrics (e.g., nDCG, recall@K), error bars, statistical significance tests, or biases in corpus construction. These omissions are load-bearing because the central claim of practical improvement cannot be assessed without them.

Authors: We agree that the abstract and experiments section require more explicit details to substantiate the claims. In the revised manuscript we will expand the abstract to name the primary metrics (nDCG@K and Recall@K), note the inclusion of error bars, and reference statistical significance testing. The experiments section will be updated to describe the shallow graph baseline in greater detail, report the exact evaluation protocol, and include a short analysis of potential corpus-construction biases (e.g., sheet-size distribution and domain coverage). These additions will make the reported improvements directly verifiable. revision: yes
Referee: [Method] Method section (record extraction description): The framework relies on the claim that schema-aware records extracted from sheet names, column headers, representative values, and layout features are sufficient to encode full worksheet semantics for stable tokens and effective graph reasoning. This is unvalidated in the provided text and is load-bearing, as spreadsheets frequently embed critical semantics in formulas, named ranges, conditional logic, and inter-cell dependencies that are not recoverable from the listed surface features; omission risks retrieving syntactically compatible but semantically incomplete sheet sets.

Authors: We acknowledge that formulas, named ranges, and inter-cell dependencies often carry essential semantics not captured by the surface features we currently extract. Our design choice prioritizes scalable, parse-light record extraction to enable workbook-scale retrieval without requiring full formula evaluation. In the revised manuscript we will add an explicit limitations paragraph in the Method section that states the scope of the extracted records, notes the potential omission of formula-driven semantics, and outlines planned future work on lightweight formula embedding. We will also clarify that the multi-channel graph is intended to compensate for missing signals by leveraging schema-consistency and shape-compatibility edges among the available tokens. revision: partial

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper's method is defined through explicit, rule-based extraction of schema-aware records (sheet names, column headers, representative values, layout features) and construction of a query-conditioned graph using semantic, schema-consistency, and shape-compatibility relations, followed by a multi-stage graph transformer. These components are specified independently of the reported retrieval metrics. Experiments on a constructed corpus are presented as validation rather than as fitted inputs renamed as predictions. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes, and no equations or steps reduce the central claims to their own inputs by construction. The derivation chain remains self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the framework relies on standard dense encoding and graph neural network components whose details are not specified here.

pith-pipeline@v0.9.0 · 5538 in / 1049 out tokens · 21581 ms · 2026-05-08T11:20:47.823613+00:00 · methodology

discussion (0)

Reference graph

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