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ARXIV:2603.06503 · SPREADSHEET AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.06503SPREADSHEET AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
BRTR is an agentic framework that enables LLMs to understand and edit complex spreadsheets through iterative tool-calling, achieving state-of-the-art performance on multiple benchmarks.
Opportunity summary
Pain BRTR is an agentic framework that enables LLMs to understand and edit complex spreadsheets through iterative tool-calling, achieving state-of-the-art performance on multiple benchmarks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
BRTR is an agentic framework that enables LLMs to understand and edit complex spreadsheets through iterative tool-calling, achieving state-of-the-art performance on multiple benchmarks. However, state-of-the-art approaches exclude critical context through single-pass retrieval, lose data…
Recent advances in multimodal Retrieval-Augmented Generation (RAG) enable Large Language Models (LLMs) to analyze enterprise spreadsheet workbooks containing millions of cells, cross-sheet dependencies, and embedded visual artifacts. However, state-of-the-art approaches exclude critical context through…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Recent advances in multimodal Retrieval-Augmented Generation (RAG) enable Large Language Models (LLMs) to analyze enterprise spreadsheet workbooks containing millions of cells, cross-sheet dependencies, and…
Spreadsheet AI moved forward this cycle; last verified April 2026. Public score 8.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
BRTR is an agentic framework that enables LLMs to understand and edit complex spreadsheets through iterative tool-calling, achieving state-of-the-art performance on multiple benchmarks.
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10.48550/arXiv.2603.06503BRTR is an agentic framework that enables LLMs to understand and edit complex spreadsheets through iterative tool-calling, achieving state-of-the-art performance on multiple benchmarks.
Abstract
Recent advances in multimodal Retrieval-Augmented Generation (RAG) enable Large Language Models (LLMs) to analyze enterprise spreadsheet workbooks containing millions of cells, cross-sheet dependencies, and embedded visual artifacts. However, state-of-the-art approaches exclude critical context through single-pass retrieval, lose data resolution through compression, and exceed LLM context windows through naive full-context injection, preventing reliable multi-step reasoning over complex enterprise workbooks. We introduce Beyond Rows to Reasoning (BRTR), a multimodal agentic framework for spreadsheet understanding that replaces single-pass retrieval with an iterative tool-calling loop, supporting end-to-end Excel workflows from complex analysis to structured editing. Supported by over 200 hours of expert human evaluation, BRTR achieves state-of-the-art performance across three frontier spreadsheet understanding benchmarks, surpassing prior methods by 25 percentage points on FRTR-Bench, 7 points on SpreadsheetLLM, and 32 points on FINCH. We evaluate five multimodal embedding models, identifying NVIDIA NeMo Retriever 1B as the top performer for mixed tabular and visual data, and vary nine LLMs. Ablation experiments confirm that the planner, retrieval, and iterative reasoning each contribute substantially, and cost analysis shows GPT-5.2 achieves the best efficiency-accuracy trade-off. Throughout all evaluations, BRTR maintains full auditability through explicit tool-call traces.
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Proof status
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What was readable
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Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
BRTR is an agentic framework that enables LLMs to understand and edit complex spreadsheets through iterative tool-calling, achieving state-of-the-art performance on multiple benchmarks. However, state-of-the-art approaches exclude critical context through single-pass retrieval,...
METHOD
Recent advances in multimodal Retrieval-Augmented Generation (RAG) enable Large Language Models (LLMs) to analyze enterprise spreadsheet workbooks containing millions of cells, cross-sheet dependencies, and embedded visual artifacts. However, state-of-the-art approaches exclude...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Recent advances in multimodal Retrieval-Augmented Generation (RAG) enable Large Language Models (LLMs) to analyze enterprise spreadsheet workbooks containing millions of cells, cross-sheet dependencies, a...
WHY NOW
Spreadsheet AI moved forward this cycle; last verified April 2026. Public score 8.0/10.
BRTR achieves state-of-the-art performance across three frontier spreadsheet understanding benchmarks, surpassing prior methods by 25 percentage points on FRTR-Bench
Directly stated in abstract with clear numeric comparison
partial
surpassing prior methods by 25 percentage points on FRTR-Bench, 7 points on SpreadsheetLLM
Directly stated in abstract with clear numeric comparison
partial
and 32 points on FINCH
Directly stated in abstract with clear numeric comparison
partial
We evaluate five multimodal embedding models, identifying NVIDIA NeMo Retriever 1B as the top performer for mixed tabular and visual data
Directly stated in abstract with clear identification of best performer
partial
cost analysis shows GPT-5.2 achieves the best efficiency-accuracy trade-off
Directly stated in abstract with clear comparison result
partial
Throughout all evaluations, BRTR maintains full auditability through explicit tool-call traces
Directly stated in abstract as a key feature
partial
state-of-the-art approaches exclude critical context through single-pass retrieval, lose data resolution through compression, and exceed LLM context windows through naive full-context injection
Directly stated as limitations of prior approaches in abstract
partial
We introduce Beyond Rows to Reasoning (BRTR), a multimodal agentic framework for spreadsheet understanding that replaces single-pass retrieval with an iterative tool-calling loop
Directly stated as core methodological innovation in abstract
partial
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Concepts
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BRTR is an agentic framework that enables LLMs to understand and edit complex spreadsheets through iterative tool-calling, achieving state-of-the-art performance on multiple benchmarks.
Segment
Spreadsheet AI
Adoption evidence
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Commercial read
8.0/10 public viability
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reason
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passport absent
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stale
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Technical feasibility
partial
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Gaps
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missing
Current read
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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