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Deep Tabular Research via Continual Experience-Driven Execution
Junnan Dong, Chuang Zhou, Zheng Yuan, Yifei Yu, Qiufeng Wang, Yinghui Li, Siyu An, Di Yin, Xing Sun, Feiyue Huang
TL;DR
LLMs struggle with long-horizon reasoning over irregular tables, whose latent structure and complex headers challenge conventional table QA. DTR responds with a closed-loop framework that combines query-aware planning, expectation-guided path selection, and experience-based memory refinement. Experiments on challenging unstructured benchmarks show consistent improvements over strong baselines and support separating strategic planning from low-level execution.
Problem
Existing table-reasoning methods assume clean schemas and leave latent table structure and long-horizon analytical reasoning insufficiently addressed.
Method
DTR constructs query-aware operations, selects execution paths with expectation-aware planning, and refines them using structured execution experience.
Results
DTR consistently improves over SOTA baselines on challenging unstructured tabular benchmarks.
Takeaways & Limitations
The results highlight separating high-level planning from low-level execution as necessary for long-horizon tabular reasoning.
Takeaways & Limitations
Deployment in high-stakes settings involving sensitive or biased data requires human oversight, data governance, and human-in-the-loop validation.
Abstract
from arXiv · showhide
Large language models often struggle with complex long-horizon analytical tasks over unstructured tables, which typically feature hierarchical and bidirectional headers and non-canonical layouts. We formalize this challenge as Deep Tabular Research (DTR), requiring multi-step reasoning over interdependent table regions. To address DTR, we propose a novel agentic framework that treats tabular reasoning as a closed-loop decision-making process. We carefully design a coupled query and table comprehension for path decision making and operational execution. Specifically, (i) DTR first constructs a hierarchical meta graph to capture bidirectional semantics, mapping natural language queries into an operation-level search space; (ii) To navigate this space, we introduce an expectation-aware selection policy that prioritizes high-utility execution paths; (iii) Crucially, historical execution outcomes are synthesized into a siamese structured memory, i.e., parameterized updates and abstracted texts, enabling continual refinement. Extensive experiments on challenging unstructured tabular benchmarks verify the effectiveness and highlight the necessity of separating strategic planning from low-level execution for long-horizon tabular reasoning.
1. Introduction
Deep Tabular Research addresses the mismatch between LLM table-QA methods built for clean schemas and real-world tables requiring long-horizon, multi-hop analysis. It proposes a closed-loop agentic framework that separates planning from execution and learns from execution feedback.
- Existing table-QA approaches rely on clean schemas, flat headers, and single-pass reasoning, limiting their applicability to irregular and semantically implicit tables.
- Real-world spreadsheets contain hierarchical and bidirectional headers, merged cells, missing values, and contextually defined values that complicate navigation.
- Deep Tabular Research formalizes long-horizon tabular reasoning as coordinated data acquisition, computation, and analytical synthesis over unstructured tables.
- The proposed framework decouples high-level strategic planning from low-level execution and treats reasoning as a continual decision process driven by execution experience.
- An expectation-aware policy selects promising execution trajectories, while siamese structured memory records outcomes and failures to refine planning.
- Extensive evaluations on challenging unstructured tabular benchmarks demonstrate DTR’s effectiveness and efficiency in complex, real-world data layouts.
2. Task Definition
DTR defines tabular reasoning over non-canonical tables as interactive exploration in an execution environment rather than direct linguistic mapping. The task requires trajectories of atomic operations whose intermediate results support a correct final answer.
- A DTR task is defined by the tuple (T, Q, E, Y), representing the tabular domain, query space, execution environment, and output space.
- DTR tables may have hierarchical headers, bidirectional row-column spans, and implicit semantic relations, while queries require long-horizon reasoning such as trend analysis or cross-region comparison.
- Unlike traditional TableQA, DTR models reasoning as interactive exploration in which an agent incrementally executes operations and adapts its strategies.
- The table’s true semantic structure is latent and must be inferred through interaction because raw data rarely exposes explicit hierarchical dependencies.
- Resolving a query requires an analytical trajectory of atomic actions, each mapping an input data state to an intermediate result.
- At step t, the reasoning state contains the query, table, and prior action-result pairs, with the objective of finding a trajectory whose final outcome supports the correct answer.
3. Deep Tabular Research
DTR turns complex tabular reasoning into a closed-loop process over structured table semantics and executable operation paths. It combines expectation-aware path selection with execution feedback to refine long-horizon analytical decisions.
- Table Comprehension: DTR constructs a structured representation of explicit and implicit table semantics, including bidirectional hierarchical headers and their containment relationships.Metadata and header regions are organized into a graph so cells can be interpreted through row-wise and column-wise context.
- Operation Planning: Natural-language queries are decomposed and aligned with candidate operations over the table graph, then organized into dependency-respecting sequential paths.The operation bank includes filtering, group aggregation, and numerical sorting, while the operation map encodes admissible orderings and prerequisites.
- Path Selection: DTR evaluates feasible paths with expectation-aware scores that combine historical expected returns, execution counts, and structural priors.The policy balances exploitation of reliable paths with exploration of plausible paths that have been executed fewer times.
- Path Selection: The expectation mechanism remains bounded and shifts sufficiently explored paths from exploration-driven selection toward exploitation based on empirical performance.The framework assumes bounded execution rewards and structural priors, making the exploration contribution decrease as path execution counts increase.
- Closed-Loop Execution: Selected paths are instantiated as executable programs, evaluated at the path level, and iteratively revised using intermediate execution interactions and feedback.Path-level evaluation reflects the non-decomposable nature of long-horizon rewards, while [THINK]/[CODE] signals characterize execution stages.
- Experience-Guided Reflection: Parameterized feedback and abstracted experience update expectations for executed and structurally related candidate paths, allowing future operation selection to adapt to prior outcomes.The learning rate controls the influence of new outcomes, while execution factors such as runtime and output-format consistency contribute to path rewards.
4. Experimental Analysis
Experiments show that DTR consistently improves analytical quality across unstructured-tabular benchmarks while balancing reasoning depth, execution reliability, and computational cost. Ablations and budget analyses attribute these gains to structured planning, experience-aware path selection, and efficient prompting.
- Main Results: DTR achieves the strongest overall performance on DTR-Bench across accuracy, analysis depth, feasibility, and aesthetics.The improvements remain consistent under both strict win-rate and tolerant score-rate evaluation.
- Main Results: DTR produces more complete and visually coherent reports than pure LLMs while remaining more efficient than tree-based and loop-based agent frameworks.Its macro-level planning and learned expectations avoid exhaustive search while preserving long-range reasoning coherence.
- Component Contributions: DTR attains consistent ablation gains, raising total accuracy from 33.5% to 37.5%, a 4.0-point improvement over the baseline.Tabular metainformation and query decomposition contribute +1.3 and +1.4 points, while historical path feedback adds +0.9 points and abstracted experience adds +0.4 points.
- Prompting Strategy Analysis: The structured [THINK]+[CODE] strategy reaches 37.5% accuracy and 30.2 analysis depth while reducing code errors from 42.3% to 28.4%.It also uses 4.78 average LLM calls versus 5.8 for direct generation; multi-stage reflection lowers errors to 26.1% but raises runtime from 42.1s to 48.6s.
- Efficiency and Scalability: DTR’s 4.78-call average lies in the transition regime, where gains slow from approximately +1.45% per call at 1–3 calls to +0.45% at 3–6 calls.Beyond six calls, each additional call yields less than 0.15% performance gain; CodeLoop uses 8.8 calls but reaches only 27.5% accuracy.
- Planning Dynamics: Across 10 batches, macro-path selection shifts from broad exploration toward exploitation without collapsing into a single deterministic strategy.Path 0 rises from 3% to 28%, the primary path stabilizes near 31%, Path 5 remains at 11%, and about 10–15% exploration is retained.
5. Conclusions
The paper defines Deep Tabular Research as long-horizon analytical reasoning over unstructured tables and introduces DTR as a closed-loop, execution-driven framework. Its experiments support experience-aware planning and separating strategic planning from low-level execution.
- Conclusions: Deep Tabular Research formalizes long-horizon analytical reasoning over unstructured tables as a distinct research paradigm.DTR treats tabular reasoning as a closed-loop decision process grounded in executable operations.
- Conclusions: DTR combines query-aware operator abstraction, expectation-driven path selection, and experience-based memory refinement to handle structural ambiguity and execution uncertainty.The framework jointly addresses strategic planning and operational execution.
- Conclusions: Experiments demonstrate consistent improvements over state-of-the-art baselines in reasoning accuracy, execution stability, and efficiency.The conclusion identifies separation of high-level planning from low-level execution as necessary for this setting.
Broader Impact
The work aims to improve automated reasoning over complex tables for scientific, business, and public-data workflows. It reports potential reductions in manual effort and analytical errors, while emphasizing that deployment risks remain those associated with LLMs and automated data analysis.
- Broader Impact: The framework targets more robust, interpretable, and effective automated reasoning over complex tabular data.The stated applications include scientific analysis, business intelligence, and public data reporting.
- Broader Impact: Improved understanding of complex table structures and multi-step analysis may reduce manual effort and errors in data-analysis workflows.The passage presents this as a potential positive societal impact rather than a demonstrated outcome.
- Broader Impact: The authors do not anticipate significant new ethical risks beyond those commonly associated with LLMs and automated data-analysis systems.They identify misuse without human oversight in high-stakes settings involving sensitive or biased data as a relevant concern.
- Broader Impact: Responsible deployment, data governance, and human-in-the-loop validation are presented as appropriate safeguards for these risks.The paper characterizes its contribution as incremental and not requiring mitigation beyond existing standards.
B. DTR-Bench Dataset Curation
DTR-Bench is a specialized benchmark of 500 scenario-driven analytical questions over curated Excel spreadsheets, designed to evaluate deep reasoning rather than simple retrieval. Its curation combines personas, analytical templates, compatibility checks, deduplication, and machine-checkable answer criteria.
- Dataset Overview: DTR-Bench contains 500 scenario-driven question-answer pairs spanning Economy, Business, Education, and other domains.The questions require correlation analysis, inequality measurement, anomaly detection, and statistical hypothesis testing over curated RealHitBench Excel spreadsheets.
- Question Generation: Scenario-driven generation grounds each question in a realistic user persona with domain-specific analytical needs.This design aims to reflect authentic data science tasks rather than artificial academic exercises.
- Question Generation: The benchmark defines 8 user personas representing real-world roles that regularly perform deep tabular analysis.
- Question Generation: Each persona receives 4 specialized templates specifying columns, analytical methods, and structured outputs such as rankings or statistical measures.Examples of requested methods include correlation, ANOVA, the Gini coefficient, and HHI.
- Quality Control: Validation filters unsuitable columns and table-template combinations, skips unsupported time-series questions, and removes duplicate questions.
- Evaluation Preparation: AnswerKeyPoints provide explicit, machine-checkable grading criteria, enabling fine-grained evaluation beyond string matching.The criteria can check operations such as aggregation, inequality calculation, and concentration reporting.
C.1. Benchmark Datasets
The benchmark evaluation combines DTR-Bench and RealHitBench to test long-horizon analytical reasoning across heterogeneous tables and multiple task types. DTR is compared with table-specialized models, general-purpose LLMs, and agentic frameworks, while visualization examples illustrate executable table analysis.
- DTR-Bench: DTR-Bench contains 500 long-form analytical queries generated from RealHitBench tables and designed to require multi-step reasoning and execution.The queries span categories including analysis and visualization.
- DTR-Bench: Each query typically requires planning across multiple operations and intermediate execution states, making shallow or single-pass reasoning unsuitable.
- RealHitBench: RealHitBench contains real-world tables with merged cells, bidirectional and hierarchical headers, missing values, and implicit semantic regions.
- Evaluation Tasks: Evaluation covers fact checking, numerical reasoning, structure comprehension, data analysis, and visualization.Metrics include EM and F1, LLM-based evaluation and ROUGE, and visualization measures such as ECR and Pass@1.
- Baselines: DTR is compared against table-specialized models, general-purpose LLMs, and agent-based reasoning frameworks.
- Framework Positioning: The framework integrates operator-level execution with continual experience-driven decision making for complex, unstructured tables requiring iterative analysis and revision.
E.1.4. QUALITY ANALYSIS Strengths:
The quality-analysis examples emphasize executable, validated visualizations with clear labeling and presentation choices. They include a 1984 employment pie chart and a 1983–1990 employment-trend example whose reported distribution is strongly non-agricultural.
- Pie Chart: The visualization code includes assertions and numeric-type checks before plotting, providing basic data validation.
- Pie Chart: The reported 1984 employment distribution is 95.5% non-agriculture versus 4.5% agriculture.
- Trend Chart: The line-chart example compares employed and unemployed populations across 1983–1990 using separate marked series.The chart specifies year and population-in-thousands axes, a legend, and a grid.
E.2.4. ANALYSIS
The analysis examples report accurate extraction, validation, and readable chart construction, alongside a substantial rise in employment and a declining unemployment rate. A separate scatter plot is specified to examine unemployment against civilian labor-force size over 1983–1998.
- Trend Analysis: The trend-chart workflow accurately extracts an 8-year time series and validates lengths and numeric types with assertions.
- Trend Analysis: The line chart uses distinct marked series, gridlines, a legend, and axis labels with thousands as the unit.
- Trend Analysis: 56.8M →65.1M, +14.6%: the reported employed population increased steadily from 1983 to 1990.
- Trend Analysis: The reported unemployment rate dropped from 10% to 5.6% over the same period.
- Scatter-Plot Analysis: The scatter-plot task examines unemployment rate against total civilian labor-force size for all years from 1983 to 1998.
- Scatter-Plot Analysis: The scatter plot places civilian labor force on the x-axis and unemployment rate on the y-axis.
E.3.4. ADVANCED ANALYSIS Multi-variable Extraction:
The analysis extracts and aligns three related labor-market series across 16 years, revealing an inverse relationship between labor-force size and unemployment. Unemployment declined through 1990, spiked during 1990–1992, and then improved as the labor force expanded.
- Three related data series were successfully extracted for the multi-variable analysis.
- The dataset contains 16 years of continuous data from 1983 through 1998.
- As the labor force grows, unemployment tends to decrease, indicating an inverse relationship.
- 1983–1990: Unemployment fell from 9.9% to 5.2% despite 9.4% labor-force growth, indicating strong job creation.
- 1990–1992: Unemployment spiked to 7.9% during a recession period.
- 1993–1998: Unemployment steadily improved to 4.4% while the labor force continued expanding.
F.1.3. MODEL RESPONSE (DETAILED ANALYSIS)
The analysis computes descriptive statistics for annual U.S. unemployment rates across the 1953–2023 series. It reports a 5.80% mean and 1.62% standard deviation, with higher values associated with recession periods and a COVID-19 spike.
- The analysis covers annual U.S. unemployment rates from 1953 through 2023.
- 5.80% is the mean unemployment rate, calculated from 71 valid data points.
- 1.62% is the standard deviation, indicating moderate variability in the annual rates.
- Most years fall between 4.2% and 7.4%, corresponding to the reported ±1σ range.
- Higher unemployment values occurred during the 1982, 2010, and 2020 recession-related periods.
- The reported results include numerical answers, calculation methodology, historical context, and identification of outliers.