pith. machine review for the scientific record. sign in

arxiv: 2604.28076 · v1 · submitted 2026-04-30 · 💻 cs.CL · cs.AI· cs.LG

Recognition: unknown

TopBench: A Benchmark for Implicit Prediction and Reasoning over Tabular Question Answering

An-Yang Ji , Jun-Peng Jiang , De-Chuan Zhan , Han-Jia Ye
Authors on Pith no claims yet

Pith reviewed 2026-05-07 05:42 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords tabular question answeringimplicit predictionlarge language modelsbenchmarkintent recognitionpredictive reasoningtable inference
0
0 comments X

The pith

LLMs default to lookups on tables instead of predicting unobserved values from patterns.

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

The paper introduces TopBench to test large language models on implicitly predictive queries over tabular data, where answers must be inferred from historical patterns rather than retrieved directly. Experiments on 779 samples across four sub-tasks show that models struggle first with recognizing the latent intent of such queries, which blocks any subsequent predictive reasoning. A sympathetic reader would care because many practical table-based questions in business, science, and policy involve forecasting or decision-making that current systems cannot yet handle reliably.

Core claim

TopBench demonstrates that accurate intent disambiguation serves as the prerequisite for LLMs to produce predictive behaviors on tabular data; without it, models default to lookups, and raising the upper bound on prediction precision requires more sophisticated modeling or reasoning capabilities.

What carries the argument

TopBench benchmark consisting of four sub-tasks (single-point prediction, decision making, treatment effect analysis, complex filtering) that require models to output both reasoning text and structured tables.

If this is right

  • Intent recognition must be solved before reliable predictive reasoning can emerge in tabular QA systems.
  • Text-based and agentic workflows both require upgrades to handle latent intent rather than surface retrieval.
  • Improving prediction precision will depend on integrating more advanced modeling techniques beyond current LLM defaults.
  • The benchmark can be used to measure progress on the shift from retrieval to inference in table-based tasks.

Where Pith is reading between the lines

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

  • The same intent-recognition bottleneck may limit LLMs on predictive tasks with other structured data such as time series or knowledge graphs.
  • Training objectives that explicitly reward intent disambiguation could raise performance ceilings on forecasting-style table questions.
  • Business and scientific analytics pipelines that rely on tables for forward-looking decisions would gain immediate value from models that clear TopBench thresholds.

Load-bearing premise

The 779 samples and four sub-tasks faithfully represent the distribution and difficulty of real-world implicit predictive queries over tabular data, and model outputs can be reliably scored for predictive accuracy versus retrieval.

What would settle it

A model that scores well on TopBench while still treating queries as lookups without correctly identifying predictive intent, or a follow-up study on a larger set of real-world implicit queries that shows substantially different failure rates.

Figures

Figures reproduced from arXiv: 2604.28076 by An-Yang Ji, De-Chuan Zhan, Han-Jia Ye, Jun-Peng Jiang.

Figure 1
Figure 1. Figure 1: Comparison between Traditional TQA and Implicit Predictive TQA. Rather than retrieving or aggregating explicit facts based on clear instructions, implicit predictive TableQA re￾quires the model to infer unobserved values. Chen et al., 2022) and medical interpretation (Lee et al., 2022; Shi et al., 2024) to daily record management (Dong et al., 2025). Driven by the growing demand to automat￾ically analyze t… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of TOPBENCH. TOPBENCH requires inferring unobserved outcomes from historical data across four tasks: Single-Point Prediction, Decision Making, Treatment Effect Analysis, and Ranking and Filtering. abstracting the textual description into a structured feature set. Subsequently, it needs to perform predictive reasoning over massive historical rows to infer the missing value. This process imposes a d… view at source ↗
Figure 3
Figure 3. Figure 3: Dataset Distributions. (a) Domain distribution (Inner Ring) and the corresponding historical table lengths (Outer Ring) defined as Short (< 1k), Medium (1k-10k), and Long (> 10k). (b) Distribution of Ranking tasks categorized by filtering constraints (Inner Ring) and the length of candidate lists to be processed (Outer Ring) as Short (< 100) and Long (> 100). and Healthcare. To test model robustness agains… view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of Predictive Tool Usage. The chart illus￾trates the frequency with which different LLMs invoke machine learning libraries versus simple data manipulation methods. It also highlights the most frequently selected algorithm for each model. DeepSeek Instruct DeepSeek Thinking Qwen3 Instruct Qwen3 Thinking Claude Sonnet4.5 Gemini3 Flash GPT-5.2 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 Average Perfo… view at source ↗
Figure 5
Figure 5. Figure 5: Performance Impact of Predictive Modeling. We compare average scores across Single Point (Accuracy), Decision (Decision Score), and Treatment Effect (Trend Score) tasks. “With Modeling” means LLMs use the predictive model. “Without Mod￾eling” means they default to data retrieval or simple aggregation. More specific results can be found in Appendix D. 6.1. Analysis of Agentic Predictive Modeling Behaviors T… view at source ↗
Figure 6
Figure 6. Figure 6: The Multi-Stage Data Synthesis Pipeline. The process begins with Foundation Curation, where logic-driven sampling selects challenging data points (e.g., hard negatives with similar feature values). In Task Construction, we employ a dual-perspective prompting strategy—simulating both non-technical users and professional data holders—to generate intent-rich queries across four sub-tasks. Finally, the Hybrid … view at source ↗
Figure 7
Figure 7. Figure 7: The Hallucination Verification Pipeline. To ensure data integrity, the extraction process employs a cascade of verification modules. Phase 1 performs aggressive text normalization followed by surface-level string matching. If direct matching fails, Phase 2 activates deep logic verifiers, including a numerical parser for unit conversion and an NLI agent to confirm semantic entailment. "prediction_payload": … view at source ↗
Figure 8
Figure 8. Figure 8: Standardized JSON Schemas for Predictive Reasoning Tasks. The Judge extracts structured payloads containing point estimates, intervals, and verbatim proof quotes. For B2 and B3, predictions are extracted per scenario to verify comparative reasoning. where max(Y ) and min(Y ) represent the maximum and minimum observed values of the target column in the historical dataset. If the range is negligible, we defa… view at source ↗
Figure 9
Figure 9. Figure 9: Tool Usage Distribution for Single Point Prediction (Standard). DeepSeek actively employs predictive models, while Qwen3 relies heavily on non-modeling approaches. Integrity of the Modeling Pipeline. Merely invoking a machine learning library does not guarantee a valid prediction; the raw tabular data must be rigorously prepared. We further investigate the completeness of the generated code by measuring th… view at source ↗
Figure 10
Figure 10. Figure 10: Tool Usage Distribution for Single Point Prediction (With Semantic Info). The addition of semantic metadata prompts a significant increase in modeling frequency for Qwen3. DeepSeek-V3.2 exhibits the most inflated response lengths. Lacking the internal capacity for precise arithmetic, these models frequently resort to generating extensive, convoluted reasoning chains to justify heuristic estimates. This “h… view at source ↗
Figure 11
Figure 11. Figure 11: Tool Usage Distribution for Decision Making (Standard). Models show a mixed strategy, balancing between comparison logic and predictive modeling. instances. However, this often leads to “superficial feature matching,”where the model latches onto salient but non-causal features to justify its choice. Conversely, the Agentic approach shown in view at source ↗
Figure 12
Figure 12. Figure 12: Tool Usage Distribution for Decision Making (With Semantic Info). Explicit task definition encourages models to adopt more formal comparative analysis techniques. 42% 30% 20% DeepSeekV3.2 Instruct 44% 21% 14% 13% 7% DeepSeekV3.2 Thinking 80% 11% 6% Qwen3 Instruct 87% 8% Qwen3 Thinking 43% 40% 14% Claude Sonnet-4.5 83% 11% 6% Gemini3 Flash 50% 23% 11% 7% 6% GPT-5.2 Predictive Models Linear Regression Rando… view at source ↗
Figure 13
Figure 13. Figure 13: Tool Usage Distribution for Treatment Effect Analysis (Standard). Causal reasoning scenarios drive a higher baseline usage of regression models across all agents. 28 view at source ↗
Figure 14
Figure 14. Figure 14: Tool Usage Distribution for Treatment Effect Analysis (With Semantic Info). Enhanced context further solidifies the preference for causal inference methods over heuristic estimation. 61% 27% 7% DeepSeekV3.2 Instruct 54% 29% 11% DeepSeekV3.2 Thinking 66% 28% Qwen3 Instruct 48% 39% 10% Qwen3 Thinking 72% 23% Claude Sonnet-4.5 70% 29% Gemini3 Flash 37% 30% 30% GPT-5.2 Predictive Models Random Forest Logistic… view at source ↗
Figure 15
Figure 15. Figure 15: Tool Usage Distribution for Ranking and Filtering (Standard). The complexity of batch processing naturally leads to a higher adoption of robust algorithms like Random Forest. 29 view at source ↗
Figure 16
Figure 16. Figure 16: Tool Usage Distribution for Ranking and Filtering (With Semantic Info). Semantic clarity assists models in selecting more appropriate feature sets for batch ranking algorithms. 0 10 20 30 40 50 60 70 Rate (%) 53.9% 60.3% 19.5% 21.6% 55.4% 26.3% 49.3% Model Usage and Data Preprocessing Capabilities Model Usage (Total) Data Preprocessing (Ratio in Usage) DeepSeekV3.2 Instruct DeepSeekV3.2 Thinking Qwen3 Ins… view at source ↗
Figure 17
Figure 17. Figure 17: Ratio of Data Preprocessing in Predictive Workflows. The chart quantifies the conditional probability that a model implements necessary feature engineering steps (e.g., encoding, imputation) when employing machine learning algorithms. Higher ratios indicate more robust and executable code generation. 30 view at source ↗
Figure 18
Figure 18. Figure 18: Comparison of Average Response Lengths. We contrast the token usage between Text-Based Reasoning (No Tool), where models rely solely on internal parameters, and the Agentic Workflow (With Tool), where models utilize Python execution. The lengths reported include the generated reasoning text and code blocks. Query: Just had my annual checkup, and the doctor reviewed everything with me. So, being a 51-year-… view at source ↗
Figure 19
Figure 19. Figure 19: Single-Point Prediction (Text-Based). The model resorts to retrieving similar historical rows and estimating a vague range based on neighbor values. While semantically plausible, this approach fails to capture the precise, non-linear mapping required for the ground truth target. 31 view at source ↗
Figure 20
Figure 20. Figure 20: Single-Point Prediction (Agentic Workflow). Leveraging the sandbox, the model trains a Linear Regression model to generate a numerical estimate. Note that while the approach is correct (modeling vs. retrieval), the result ($13.5k) still deviates from the ground truth ($9.3k), highlighting the limitation of simple default algorithms in zero-shot code generation. 32 view at source ↗
Figure 21
Figure 21. Figure 21: Decision Making (Text-Based). The model relies on qualitative comparisons with retrieved historical samples. The reasoning is fragile, often basing the decision on superficial feature similarities (e.g., region matches) rather than calculated risk factors. 33 view at source ↗
Figure 22
Figure 22. Figure 22: Decision Making (Agentic Workflow). The model quantifies the decision by predicting exact scores for both candidates. This quantitative comparison enables a more rigorous trade-off analysis compared to the fuzzy logic of the text-based baseline. 34 view at source ↗
Figure 23
Figure 23. Figure 23: The Exhaustive Retrieval Loop (Qwen3-Thinking). Misinterpreting the prediction task as a database lookup, the model enters an infinite loop of row-by-row verification. It attempts to find an exact match for a hypothetical profile, eventually exhausting the context window without producing a valid answer. 35 view at source ↗
read the original abstract

Large Language Models (LLMs) have advanced Table Question Answering, where most queries can be answered by extracting information or simple aggregation. However, a common class of real-world queries is implicitly predictive, requiring the inference of unobserved answers from historical patterns rather than mere retrieval. These queries introduce two challenges: recognizing latent intent and reliable predictive reasoning over massive tables. To assess LLMs in such Tabular questiOn answering with implicit Prediction tasks, we introduce TopBench, a benchmark consisting of 779 samples across four sub-tasks, ranging from single-point prediction to decision making, treatment effect analysis, and complex filtering, requiring models to generate outputs spanning reasoning text and structured tables. We evaluate diverse models under both text-based and agentic workflows. Experiments reveal that current models often struggle with intent recognition, defaulting to just lookups. Deeper analysis identifies that accurate intent disambiguation serves as the prerequisite for leading these predictive behaviors. Furthermore, elevating the upper bound of prediction precision requires the integration of more sophisticated modeling or reasoning capabilities.

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.

Referee Report

2 major / 2 minor

Summary. The paper introduces TopBench, a benchmark of 779 samples across four sub-tasks (single-point prediction, decision making, treatment effect analysis, complex filtering) to evaluate LLMs on tabular QA requiring implicit prediction of unobserved values from historical patterns rather than retrieval. Evaluations of diverse models in text-based and agentic workflows show that models frequently fail to recognize latent intent and default to lookups; the authors conclude that accurate intent disambiguation is a prerequisite for effective predictive reasoning and that more sophisticated modeling is needed to raise prediction precision.

Significance. If the benchmark construction and scoring protocols are sound and representative, TopBench would fill a genuine gap in table QA evaluation by targeting predictive inference over retrieval. The empirical finding that intent recognition is the primary bottleneck could usefully steer future work on agentic and reasoning-enhanced table models. The provision of a public benchmark with structured outputs (reasoning text plus tables) is a concrete community resource.

major comments (2)
  1. [§3] §3 (Benchmark Construction): The manuscript must supply explicit details on query provenance (real logs vs. synthetic generation), table domains and sizes, the protocol used to establish unobserved ground-truth values for prediction/treatment-effect tasks, and inter-annotator agreement statistics for labeling samples as requiring predictive reasoning versus retrieval. Without these, it is impossible to confirm that the 779 samples genuinely test implicit prediction rather than artifacts of benchmark design.
  2. [§4] §4 (Experiments and Analysis): The claim that models 'default to just lookups' and that 'accurate intent disambiguation serves as the prerequisite' rests on the reliability of output scoring that distinguishes predictive reasoning from retrieval. The paper should report the exact rubric or classifier used for this distinction, quantitative inter-rater reliability for those labels, and at least a sample of annotated model outputs. Absent this, the observed failure modes cannot be confidently attributed to model limitations rather than evaluation artifacts.
minor comments (2)
  1. [§3.2] Table 1 or §3.2: Provide summary statistics (average rows/columns per table, domain distribution) to allow readers to judge scale and diversity.
  2. [§4.1] §4.1: Clarify the precise prompting templates and agentic workflow implementations so that the text-based vs. agentic comparison is fully reproducible.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important areas for improving transparency and rigor in the manuscript. We address each major comment below and will incorporate revisions to strengthen the paper.

read point-by-point responses

  1. Referee: [§3] §3 (Benchmark Construction): The manuscript must supply explicit details on query provenance (real logs vs. synthetic generation), table domains and sizes, the protocol used to establish unobserved ground-truth values for prediction/treatment-effect tasks, and inter-annotator agreement statistics for labeling samples as requiring predictive reasoning versus retrieval. Without these, it is impossible to confirm that the 779 samples genuinely test implicit prediction rather than artifacts of benchmark design.

    Authors: We agree that these details are necessary to allow readers to evaluate the benchmark's construction and confirm its focus on implicit prediction. While §3 outlines the overall process, we acknowledge that more granular information on provenance, domains, ground-truth protocols, and agreement statistics was not provided. In the revised manuscript we will expand §3 with a dedicated subsection supplying this information, including the sources of queries, table characteristics, how unobserved values were determined, and agreement metrics. These additions will directly address the concern and substantiate that the 779 samples target predictive reasoning. revision: yes

  2. Referee: [§4] §4 (Experiments and Analysis): The claim that models 'default to just lookups' and that 'accurate intent disambiguation serves as the prerequisite' rests on the reliability of output scoring that distinguishes predictive reasoning from retrieval. The paper should report the exact rubric or classifier used for this distinction, quantitative inter-rater reliability for those labels, and at least a sample of annotated model outputs. Absent this, the observed failure modes cannot be confidently attributed to model limitations rather than evaluation artifacts.

    Authors: We concur that transparent documentation of the scoring process is required to support the claims about model behavior and the role of intent disambiguation. The current §4 describes the high-level evaluation approach, but we will revise it to include the precise rubric for distinguishing predictive reasoning from retrieval, quantitative inter-rater reliability statistics, and a set of annotated sample outputs (to be placed in the appendix). These changes will strengthen the attribution of observed failure modes to model limitations rather than evaluation artifacts. revision: yes

Circularity Check

0 steps flagged

No derivation chain or self-referential structure present in this empirical benchmark paper

full rationale

This is a benchmark construction and evaluation paper with no mathematical derivations, equations, parameter fittings, or predictive models that could reduce to their inputs by construction. The core contribution is the creation of TopBench (779 samples across four sub-tasks) followed by direct empirical testing of LLMs under text-based and agentic workflows. Claims about models defaulting to lookups or the prerequisite role of intent disambiguation are observational results from held-out evaluation, not derived quantities. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling appear in the abstract or described content. The paper is self-contained against external benchmarks in the sense that its results are falsifiable via re-running the evaluations on the released data; therefore no circularity is present.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that the constructed tasks capture genuine implicit prediction challenges and that intent recognition is a separable prerequisite; no free parameters or invented physical entities are involved.

axioms (1)
  • domain assumption The four sub-tasks (single-point prediction, decision making, treatment effect analysis, complex filtering) adequately cover the space of implicit predictive tabular queries.
    Invoked when defining the benchmark scope in the abstract.
invented entities (1)
  • TopBench benchmark dataset and tasks no independent evidence
    purpose: To evaluate LLMs on implicit prediction and reasoning over tables
    Newly introduced collection of 779 samples; no independent evidence outside the paper is provided in the abstract.

pith-pipeline@v0.9.0 · 5489 in / 1268 out tokens · 40796 ms · 2026-05-07T05:42:48.919429+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

25 extracted references · 3 canonical work pages · 1 internal anchor

  1. [1]

    Ovis2.5 Technical Report

    Springer Nature Singapore, 2022. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y . Lightgbm: A highly efficient gradient boosting decision tree. InNeurIPS, 2017. Lee, G., Hwang, H., Bae, S., Kwon, Y ., Shin, W., Yang, S., Seo, M., Kim, J.-Y ., and Choi, E. EHRSQL: A practical text-to-SQL benchmark for electronic health reco...

    work page internal anchor Pith review arXiv 2022

  2. [2]

    The value... is high

    Text Standardization and Hybrid Surface Matching:Before comparison, raw responses undergo aggressive normal- ization: stripping LaTeX formatting (e.g., converting \text{1.5k} to 1.5k), unifying Unicode symbols, and mapping natural language numerals (e.g., “three”) to digits via a lookup table. The system then attempts to verify the extracted proof quote u...

  3. [3]

    1.5k”→1500, “20%

    Numeric Parsing and Interval Logic:For regression tasks, we deploy a specialized NumberParser to verify mathematical equivalence between the extracted structure and the text. This module: •Unit Conversion: Maps domain-specific suffixes (e.g., “1.5k”→1500, “20%”→0.2, “5bn”→5×10 9). • Interval Derivation: Validates implied intervals. For instance, if the te...

  4. [4]

    skyrocketed

    Semantic Entailment (NLI Fallback):If structural and numeric matching fail—often due to high-level summariza- tion—we activate a secondary LLM agent to perform Natural Language Inference (NLI). This agent determines if the 16 TOPBENCH: A Benchmark for Implicit Prediction and Reasoning over Tabular Question Answering Table 10.Model-by-Row-Scale Cross-Tabul...

  5. [5]

    ’history.csv’ - Columns: $history_columns

  6. [6]

    The data provided above are ONLY column names

    ’current.csv’ - Columns: $current_columns Note: These files are mounted, use their filenames directly. The data provided above are ONLY column names. DO NOT hallucinate data rows. You MUST use the CodeRunner tool to read the files (e.g., pd.read_csv) to inspect the actual data content. CRITICAL REQUIREMENT:

  7. [7]

    You MUST process the data and save the final results into a file named ’result.csv’

  8. [8]

    The ’result.csv’ MUST contain the exact columns matching history.csv format

  9. [9]

    Which is X?

    Do not just print the result, you must save it to ’result.csv’ using pandas to_csv().$prompt_extras You need to give the answer within $max_iterations rounds. Judge Regression Extraction Prompt You are an expert evaluator for a tabular data prediction task (REGRESSION). Input Data: [Query]: {query} [Model Response]: {response} [Ground Truth]: {gt_str} [Da...

  10. [10]

    2 million+

    Prediction Extraction (STRICT): - Extract the final numerical prediction or interval. - CRITICAL RULE FOR VAGUE NUMBERS: - If the text says "2 million+", "over 500k", or "approx 10%", you must extract the visible number (e.g., 2000000, 500000, 10). - DO NOT make up a precise number to represent the "+". - Unless the precise number is explicitly stated in ...

  11. [11]

    predicted_category

    Proof Extraction (CRITICAL): - You MUST copy the proof_quote VERBATIM from the [Model Response]. - The numbers in predicted_interval MUST be visibly identical to the numbers in this quote. - For intervals, your quote MUST contain the text for BOTH the lower and upper bounds. - DO NOT add property names, keys, or prefixes. {ANTI_HALLUCINATION_EXAMPLES} {lo...

    work page arXiv 1967

  12. [12]

    **Get Official Quotes:** Contact insurance providers directly or use the Health Insurance Marketplace for personalized quotes

  13. [13]

    **Consider Plan Details:** Look beyond the premium at deductibles, out-of-pocket maximums, and covered services

  14. [15]

    **Age (51 years)**: Adds about $255 per year of age

  15. [16]

    **High BMI (39.7)**: Your BMI is in the 92nd percentile, adding approximately $330 per BMI unit

  16. [17]

    **Non-smoker status**: This is actually saving you about **$23,400 per year** compared to smokers!

  17. [18]

    **One dependent child**: Adds about $519 to your premium

  18. [20]

    **Person A**: BMI = 33.77

  19. [21]

    **Person B**: BMI = 30.03

  20. [22]

    ### **Matching records from the dataset**

    **Person C**: BMI = 35.2 From the dataset, I can find records that closely match these profiles to estimate their insurance charges. ### **Matching records from the dataset** ...... ### **General trend from data** Looking at 18-year-old males in Southeast, non-smoker, 0 children (since 1-child records are rare for this age in dataset): ...... ### **Estima...

  21. [23]

    **Person B (BMI 30.03)** — lowest BMI → lowest base charge

  22. [24]

    **Person A (BMI 33.77)** — middle BMI → middle charge

  23. [26]

    **Second person (BMI 30.03)**: $2,211.46

  24. [27]

    **First person (BMI 33.77)**: $2,286.33

  25. [28]

    charges":

    **Third person (BMI 35.20)**: $2,314.95 ...... ### **Final Recommendation:** Your cousin should expect that **lower BMI generally correlates with lower insurance costs**. Among the three scenarios presented, the profile with **BMI 30.03** would likely have the most affordable insurance premiums. The good news is that all three scenarios are relatively clo...

    work page arXiv