Enhancing Table Reasoning with Deterministic Table-State Rewards

Chunhe Wang; Chun Ho Mak; Guang Cheng; Hengzhi He; Lei Ding; Liheng Ma; Peng Lu; Tung Sum Thomas Kwok; Xiaofeng Lin; Xinyu Wang

arxiv: 2601.22530 · v2 · pith:WWA2AAENnew · submitted 2026-01-30 · 💻 cs.AI

Enhancing Table Reasoning with Deterministic Table-State Rewards

Tung Sum Thomas Kwok , Xinyu Wang , Hengzhi He , Xiaofeng Lin , Peng Lu , Liheng Ma , Chunhe Wang , Chun Ho Mak

show 4 more authors

Yuyu Luo Ying Nian Wu Lei Ding Guang Cheng

This is my paper

Pith reviewed 2026-05-21 14:01 UTC · model grok-4.3

classification 💻 cs.AI

keywords table reasoningLLM reasoningdeterministic rewardlongest common subsequencetest-time scalingintermediate state rewardRE-TABTABROUGE

0 comments

The pith

TABROUGE adapts longest common subsequence to give training-free rewards for intermediate table states in LLM reasoning.

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

The paper establishes that LLMs can improve multi-step table reasoning by receiving explicit, deterministic feedback on intermediate table states instead of waiting for final answers or relying on learned verifiers. It introduces TABROUGE, which measures lexical coverage and structural integrity by adapting the longest common subsequence metric to compare generated tables against the original query. This approach turns reasoning into a controllable process with stepwise signals and trajectory-level test-time scaling, delivering large accuracy gains without any training or external executors. A sympathetic reader would care because it offers a scalable alternative to methods that break down when executors are unavailable or when paraphrase variations occur.

Core claim

RE-TAB reframes table reasoning as deterministic control over intermediate states, utilizing TABROUGE for stepwise feedback and trajectory-level test-time scaling signals. Across six backbones and three benchmarks, RE-TAB improves accuracy by an average of 26.7 percentage points over no-reward baselines while also reducing the number of TTS samples needed by up to 33 percent. Preliminary GRPO experiments indicate TABROUGE works as a scalable post-training reward, adding 8.34 points, though the authors note failure modes such as paraphrase under-rewarding and echo-column hacking.

What carries the argument

TABROUGE, an adaptation of the Longest Common Subsequence metric that scores lexical coverage and structural integrity of intermediate tables against the input query.

If this is right

Accuracy rises by 26.7 points on average across models and datasets when stepwise TABROUGE rewards replace no-reward baselines.
Test-time scaling requires up to 33 percent fewer samples to reach the same performance level.
TABROUGE can serve as a post-training reward in algorithms like GRPO, yielding an additional 8.34-point lift.
Structure-aware lexical rewards remain reliable only when paraphrasing and column reordering stay limited.

Where Pith is reading between the lines

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

The method may extend to other structured outputs such as code or database schemas if similar subsequence-based scoring is defined for those domains.
Combining TABROUGE with small learned verifiers could address its paraphrase weaknesses while keeping most of the training-free benefit.
Trajectory-level signals from TABROUGE might reduce the need for outcome-only supervision in broader multi-step reasoning tasks beyond tables.

Load-bearing premise

Lexical coverage and structural integrity measured by adapted LCS reliably indicate progress toward correct final answers even when queries involve paraphrasing or column reordering.

What would settle it

Run RE-TAB on a set of table queries where high TABROUGE scores on intermediate states still produce wrong final answers due to paraphrase mismatches, and check whether accuracy gains disappear.

Figures

Figures reproduced from arXiv: 2601.22530 by Chunhe Wang, Chun Ho Mak, Guang Cheng, Hengzhi He, Lei Ding, Liheng Ma, Peng Lu, Tung Sum Thomas Kwok, Xiaofeng Lin, Xinyu Wang, Ying Nian Wu, Yuyu Luo.

**Figure 1.** Figure 1: Illustration of the comparison between (a) Tabular agent without rewards (Chain-of-Tables (Wang et al., 2024)), (b) VLM agent with world model rewards (VAGEN (Wang et al., 2025a)), and (c) Our proposed RE-TAB. Given a complex table where the score is embedded in the form of box score text, (a) retrieves correctly but lacks an explicit reward to reason correctly; (b) provides correct reasoning but falls sho… view at source ↗

**Figure 2.** Figure 2 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Results on WikiTQ with GPT-3.5. We faithfully replicate both Chain-of-Tables and Tree-of-Tables and obtain similar results as reported in (Wang et al., 2024; Ji et al., 2025). After incorporating RE-TAB, both frameworks show substantial improvementsz with notable reduction in inference cost. 4.2. Setup Datasets. We conduct ablation studies on three public table-understanding benchmarks: WikiTQ (Pasupat &… view at source ↗

**Figure 4.** Figure 4: shows that convergence drops from 22–27 trajectories (standard) to 15–18 with reward feedback and to 12 with state-based TABROUGE, which also achieves higher accuracy and lower variance. This indicates that state evaluation captures correctness better than confidence. Removing State Transition rewards degrades state-based TABROUGE 0.5 0.6 0.7 0.8 0.9 1.0 Mean Accuracy Probability n=18 n=27 n=15 n=22 n=12… view at source ↗

**Figure 5.** Figure 5: Comparison of reward designs on three TableQA benchmarks. VLM-based rewards improve multi-step reasoning but degrade on large-table lookup, while embedding-based rewards are unstable due to truncation-induced structural loss. strong zero-shot capabilities, its robustness under extreme distribution shifts requires further validation across diverse industrial schemas. Semantic Noise Sensitivity: Current rew… view at source ↗

**Figure 6.** Figure 6: Unoptimized tabular LLM agent shows tendency to drift despite having already discovered the correct answer within the intermediate steps, and such drift would lead to the generation of an incorrect answer at final step. We measure LLM drift via the drifting on the TABROUGE, as we align all trajectories to the ending point to measure how each step performs before reaching conclusion. For x = 0 representing … view at source ↗

**Figure 7.** Figure 7: The first plot depicts LLM’s drifting before reaching final conclusion, while the second plot illustrates the LLM is capable of reaching most of the work in the first four steps, while the subsequent drifting does not contribute to the performance significantly. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗

**Figure 8.** Figure 8: Delimiter sensitivity measured via paired per-question differences relative to the baseline encoding. Changes to relation, separator, or row delimiters produce only small fluctuations, with confidence intervals overlapping zero, indicating limited impact on performance. D.4. Sensitivity Analysis on Tabular Size Large tables pose significant challenges for LLM-based reasoning, as models struggle to interpre… view at source ↗

**Figure 9.** Figure 9: Impact of question type on RE-TAB performance. We distinguish between which queries, where all options are given in the question, and what queries, which require table lookup. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗

read the original abstract

Large Language Models (LLMs) struggle with multi-step reasoning over structured tables. The primary reason is the lack of explicit supervision for intermediate reasoning states. Existing learned reward models or executor-based verifiers are either unscalable or rely on answer-checking environments unavailable for many tabular tasks. This leaves no signal that is scalable and grounded in the query. To address this, we introduce TABROUGE, a training-free and deterministic state reward. By adapting the Longest Common Subsequence (LCS) metric from text summarization to evaluate tabular states, TABROUGE assesses the lexical coverage and structural integrity of intermediate tables against the query without requiring learned models or external executors. Built upon this metric, we propose RE-TAB, a plug-and-play, training-free framework. RE-TAB reframes table reasoning as deterministic control over intermediate states, utilizing TABROUGE for stepwise feedback and trajectory-level test-time scaling (TTS) signals. Across six backbones and three benchmarks, RE-TAB improves accuracy by an average of 26.7 pp over no-reward baselines. It also reduces TTS samples by up to 33%. Preliminary GRPO experiments further indicate TABROUGE's viability as a scalable post-training reward, increasing gains by 8.34 pp. We further analyze failure modes of TABROUGE, including paraphrase under-rewarding and echo-column hacking, and identify when structure-aware lexical rewards remain reliable.

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

RE-TAB gives a simple LCS-based deterministic reward that lifts table reasoning accuracy but still depends on surface matches holding up in practice.

read the letter

The main point is that this work supplies a training-free reward for intermediate table states by adapting longest common subsequence to measure lexical coverage and structure against the query. They call it TABROUGE and build RE-TAB around it for both step-wise feedback and trajectory selection at test time. That combination is new enough to notice, and the reported results show clear average gains of 26.7 points over no-reward baselines across six models and three benchmarks, plus up to 33 percent fewer TTS samples. They also run a quick GRPO check that adds another 8 points, which suggests the signal can transfer to post-training as well. The paper earns credit for naming its own failure modes up front, including paraphrase under-rewarding and echo-column hacking, instead of pretending the metric is universal. That honesty makes the contribution easier to evaluate. The soft spot is the assumption that LCS alignment reliably tracks progress toward the right answer. The authors themselves say the reward works only when certain conditions hold, yet the abstract gives no count of how often paraphrasing or reordering appears in the benchmarks or how much those cases drag down the average. Without that breakdown or an ablation on lexically matched versus mismatched trajectories, it is hard to tell whether the gains come from genuine state guidance or from the subset of examples where surface form already lines up with correctness. The experimental details on baselines and splits are also thin in the summary, though the full paper may fill them in. Readers who build agents or post-training pipelines for structured data would get practical value from the plug-and-play framing and the failure analysis. The work is coherent on its own terms and shows honest engagement with the gap it targets, so it deserves a serious referee rather than a desk reject. I would send it for review with a request for more quantitative checks on mismatch frequency.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces TABROUGE, a training-free deterministic reward that adapts the Longest Common Subsequence (LCS) metric to evaluate lexical coverage and structural integrity of intermediate table states against the input query. It proposes the RE-TAB framework, which uses TABROUGE for stepwise supervision and trajectory-level test-time scaling (TTS) signals. Across six LLM backbones and three benchmarks, RE-TAB reports an average 26.7 pp accuracy gain over no-reward baselines, up to 33% reduction in required TTS samples, and an additional 8.34 pp gain in preliminary GRPO post-training experiments. The paper also identifies failure modes including paraphrase under-rewarding and echo-column hacking.

Significance. If the empirical results and underlying assumptions hold after addressing the noted gaps, the work provides a scalable, training-free alternative to learned reward models or executor-based verifiers for multi-step table reasoning. The deterministic formulation, explicit analysis of when structure-aware lexical rewards remain reliable, and reported efficiency gains represent a practical contribution to grounded supervision in structured reasoning tasks.

major comments (2)

Abstract: The central accuracy claim of a 26.7 pp average improvement lacks supporting details on statistical significance testing, exact baseline implementations, and data split handling; these omissions leave the reported gains only partially substantiated from the given text.
Abstract: The load-bearing assumption that adapted LCS reliably tracks progress toward correct final answers is undermined by the acknowledged failure mode of paraphrase under-rewarding; without a quantitative breakdown of mismatch frequency on the three benchmarks or an ablation isolating paraphrased versus lexically matched trajectories, the stepwise supervision signal's effectiveness remains insufficiently demonstrated.

minor comments (1)

The description of the exact LCS adaptation for tabular states (including handling of column reordering) would benefit from a dedicated methods subsection with pseudocode or a worked example to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback. We address each major comment point by point below, clarifying details from the full manuscript and outlining revisions to improve substantiation of our claims.

read point-by-point responses

Referee: Abstract: The central accuracy claim of a 26.7 pp average improvement lacks supporting details on statistical significance testing, exact baseline implementations, and data split handling; these omissions leave the reported gains only partially substantiated from the given text.

Authors: We agree that the abstract, as a high-level summary, omits some supporting details present in the body of the paper. Section 4 specifies that baselines follow the standard implementations and prompting strategies from prior table-reasoning literature, data splits use the official benchmark partitions, and statistical significance is assessed via paired tests across five independent runs with reported standard deviations. To address the concern directly in the abstract, we will add a concise clause noting the statistical significance and reference to the experimental protocol. revision: yes
Referee: Abstract: The load-bearing assumption that adapted LCS reliably tracks progress toward correct final answers is undermined by the acknowledged failure mode of paraphrase under-rewarding; without a quantitative breakdown of mismatch frequency on the three benchmarks or an ablation isolating paraphrased versus lexically matched trajectories, the stepwise supervision signal's effectiveness remains insufficiently demonstrated.

Authors: We appreciate this point and the emphasis on stronger evidence for the reliability of the reward signal. The manuscript already identifies paraphrase under-rewarding as a failure mode in Section 5.3 and provides qualitative examples along with conditions under which the structure-aware lexical reward remains reliable. However, we acknowledge that a quantitative breakdown of mismatch frequency and a dedicated ablation would further substantiate the claim. We will incorporate these analyses in the revised manuscript, including mismatch rates per benchmark and performance comparison on paraphrased versus lexically aligned trajectories. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected; derivation is self-contained.

full rationale

The paper introduces TABROUGE as a deterministic adaptation of the standard LCS metric to measure lexical coverage and structural integrity of intermediate table states against the query. This definition relies on an external, well-known sequence metric rather than any fitted parameters, self-referential predictions, or results from the current experiments. RE-TAB then applies this reward for stepwise feedback and TTS signals, with accuracy gains reported as empirical outcomes across backbones and benchmarks. No load-bearing self-citations, uniqueness theorems from prior author work, or ansatzes smuggled via citation appear in the provided text. The central claims rest on the independent definition of the reward and observed performance improvements, making the derivation chain self-contained without reduction to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that an LCS-derived lexical-and-structural score correlates with reasoning correctness for tabular queries. No free parameters or invented entities are mentioned; the method is presented as training-free and deterministic.

axioms (1)

domain assumption Lexical coverage and structural integrity measured by adapted LCS serve as a reliable proxy for progress toward the correct final table answer.
Invoked when TABROUGE is used to supply stepwise feedback and TTS signals without external verification.

pith-pipeline@v0.9.0 · 5820 in / 1309 out tokens · 61214 ms · 2026-05-21T14:01:26.724488+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We extend ROUGE-L to define TABROUGE... rTABROUGE(al, Tl+1, Q) = LCS(Q, Enc(Tl+1)) / |Enc(Tl+1)|
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

formalize the tool-based table reasoning as a Partially Observable Markov Decision Process

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

From Table to Cell: Attention for Better Reasoning with TABALIGN
cs.AI 2026-05 unverdicted novelty 7.0

TABALIGN pairs a diffusion language model planner emitting binary cell masks with a trained attention verifier, raising average accuracy 15.76 points over strong baselines on eight table benchmarks while speeding exec...

Reference graph

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[1] [1]

ISBN 9798331314385

Curran Associates Inc. ISBN 9798331314385. Basu, S., Grayson, M., Morrison, C., Nushi, B., Feizi, S., and Massiceti, D. Understanding information stor- age and transfer in multi-modal large language mod- els. InThe Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URL https: //openreview.net/forum?id=s63dtq0mwA. Borisov, V .,...

work page doi:10.18653/v1/2023.findings-eacl 2024

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Knowledge-Centric Hallucination Detection

URL https://aclanthology.org/2023. findings-eacl.83/. Cheng, S., Zhuang, Z., Xu, Y ., Yang, F., Zhang, C., Qin, X., Huang, X., Chen, L., Lin, Q., Zhang, D., Rajmohan, S., and Zhang, Q. Call me when neces- sary: LLMs can efficiently and faithfully reason over structured environments. In Ku, L.-W., Martins, A., and Srikumar, V . (eds.),Findings of the Assoc...

work page doi:10.18653/v1/2024 2023

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Hessel, J., Holtzman, A., Forbes, M., Le Bras, R., and Choi, Y

URL https://openreview.net/forum? id=XPZIaotutsD. Hessel, J., Holtzman, A., Forbes, M., Le Bras, R., and Choi, Y . CLIPScore: A reference-free evaluation metric for image captioning. In Moens, M.-F., Huang, X., Specia, L., and Yih, S. W.-t. (eds.),Proceedings of the 2021 Con- ference on Empirical Methods in Natural Language Pro- cessing, pp. 7514–7528, On...

work page doi:10.18653/v1/2021.emnlp-main 2021

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emnlp-main.595/

URL https://aclanthology.org/2021. emnlp-main.595/. Hollmann, N., M¨uller, S., Purucker, L., Krishnakumar, A., K¨orfer, M., Hoo, S. B., Schirrmeister, R. T., and Hutter, F. Accurate predictions on small data with a tabular foun- dation model.Nature, 637:319–326, 2025. doi: 10.1038/ s41586-024-08328-6. URL https://www.nature. com/articles/s41586-024-08328-...

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findings-emnlp.695/

Association for Computational Linguistics. ISBN 979-8-89176-256-5. doi: 10.18653/v1/2025.findings-acl

work page doi:10.18653/v1/2025.findings-acl 2025

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findings-acl.1016/

URL https://aclanthology.org/2025. findings-acl.1016/. Ji, D., Zhu, L., Gao, S., Xu, P., Lu, H., Ye, J., and Zhao, F. Tree-of-table: Unleashing the power of LLMs for enhanced large-scale table understanding,

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Jacovi, A., Caciularu, A., Goldman, O., and Goldberg, Y

URL https://openreview.net/forum? id=Yv8FrCY87H. Jiang, J., Zhou, K., Dong, Z., Ye, K., Zhao, X., and Wen, J.-R. StructGPT: A general framework for large language model to reason over structured data. In Bouamor, H., Pino, J., and Bali, K. (eds.),Pro- ceedings of the 2023 Conference on Empirical Meth- ods in Natural Language Processing, pp. 9237–9251, Sin...

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emnlp-main.574/

URL https://aclanthology.org/2023. emnlp-main.574/. Jiang, Z., Mao, Y ., He, P., Neubig, G., and Chen, W. Om- niTab: Pretraining with natural and synthetic data for few-shot table-based question answering. In Carpuat, M., de Marneffe, M.-C., and Meza Ruiz, I. V . (eds.),Pro- ceedings of the 2022 Conference of the North American Chapter of the Association ...

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naacl-main.68/

URL https://aclanthology.org/2022. naacl-main.68/. Lee, J., Lee, J.-S., Lee, J., Choi, Y ., and Lee, J.-H. DCG- SQL: Enhancing in-context learning for text-to-SQL with deep contextual schema link graph. In Che, W., Nabende, J., Shutova, E., and Pilehvar, M. T. (eds.),Proceed- ings of the 63rd Annual Meeting of the Association for Computational Linguistics...

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URL https: //aclanthology.org/2025.acl-long.748/

doi: 10.18653/v1/2025.acl-long.748. URL https: //aclanthology.org/2025.acl-long.748/. Lee, Y ., Kim, S., Rossi, R. A., Yu, T., and Chen, X. Learning to reduce: Towards improving performance of large language models on structured data, 2024. URL https://arxiv.org/abs/2407.02750. Li, B., Zhang, J., Fan, J., Xu, Y ., Chen, C., Tang, N., and Luo, Y . Alpha-SQ...

work page doi:10.18653/v1/2025.acl-long.748 2025

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Cosmos World Foundation Model Platform for Physical AI

URL https://aclanthology.org/2025. findings-emnlp.1131/. Nguyen, G., Brugere, I., Sharma, S., Kariyappa, S., Nguyen, A. T., and Lecue, F. Interpretable LLM-based table question answering.Transactions on Machine Learn- ing Research, 2025. ISSN 2835-8856. URL https: //openreview.net/forum?id=2eTsZBoU2W. NVIDIA, :, Agarwal, N., Ali, A., Bala, M., Balaji, Y ....

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ISBN 9781713871088

Curran Associates Inc. ISBN 9781713871088. 11 Enhancing TableQA through Verifiable Reasoning Trace Reward Pal, V ., Yates, A., Kanoulas, E., and de Rijke, M. Multi- TabQA: Generating tabular answers for multi-table ques- tion answering. In Rogers, A., Boyd-Graber, J., and Okazaki, N. (eds.),Proceedings of the 61st Annual Meet- ing of the Association for C...

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ISBN 979-8-89176-251-0

Association for Computational Linguistics. ISBN 979-8-89176-251-0. doi: 10.18653/v1/2025.acl-long

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acl-long.390/

URL https://aclanthology.org/2025. acl-long.390/. Sarch, G. H., Saha, S., Khandelwal, N., Jain, A., Tarr, M. J., Kumar, A., and Fragkiadaki, K. Grounded reinforcement learning for visual reasoning. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems,

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URL https://openreview.net/forum? id=1amnhVRQ3l. Shang, Y ., Zhang, X., Tang, Y ., Jin, L., Gao, C., Wu, W., and Li, Y . Roboscape: Physics-informed embodied world model. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https: //openreview.net/forum?id=wbZCBBrq3W. Shrey, P. and etc. Medhallu: A comprehensive benchma...

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findings-acl.352/

URL https://aclanthology.org/2023. findings-acl.352/. Wang, K., Zhang, P., Wang, Z., Gao, Y ., Li, L., Wang, Q., Chen, H., Lu, Y ., Yang, Z., Wang, L., Krishna, R., Wu, J., Fei-Fei, L., Choi, Y ., and Li, M. V AGEN: Re- inforcing world model reasoning for multi-turn VLM agents. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems,...

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Gao, S., Zhou, P., Cheng, M.-M., and Yan, S

URL https://openreview.net/forum? id=4L0xnS4GQM. Wu, J., Xu, Y ., Gao, Y ., Lou, J.-G., Karlsson, B. F., and Oku- mura, M. TACR: A table alignment-based cell selection method for HybridQA. In Rogers, A., Boyd-Graber, J., and Okazaki, N. (eds.),Findings of the Association for Computational Linguistics: ACL 2023, pp. 6535–6549, Toronto, Canada, July 2023. A...

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emnlp-main.1173/

URL https://aclanthology.org/2023. findings-acl.409/. Wu, J., Yang, L., Li, D., Ji, Y ., Okumura, M., and Zhang, Y . MMQA: Evaluating LLMs with multi-table multi-hop complex questions. InThe Thirteenth In- ternational Conference on Learning Representations, 2025a. URL https://openreview.net/forum? id=GGlpykXDCa. Wu, J., Yin, S., Feng, N., and Long, M. RLV...

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findings-emnlp.131/

URL https://aclanthology.org/2024. findings-emnlp.131/. Zhang*, T., Kishore*, V ., Wu*, F., Weinberger, K. Q., and Artzi, Y . Bertscore: Evaluating text generation with bert. InInternational Conference on Learning Representations,

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Zhang, Y ., Henkel, J., Floratou, A., Cahoon, J., Deep, S., and Patel, J

URL https://openreview.net/forum? id=SkeHuCVFDr. Zhang, Y ., Henkel, J., Floratou, A., Cahoon, J., Deep, S., and Patel, J. M. Reactable: Enhancing react for ta- ble question answering.Proc. VLDB Endow., 17(8): 1981–1994, April 2024b. ISSN 2150-8097. doi: 10. 14778/3659437.3659452. URL https://doi.org/ 10.14778/3659437.3659452. Zhang, Y ., Fan, M., Fan, J....

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findings-emnlp.695/

URL https://aclanthology.org/2025. findings-emnlp.695/. ˚Astr¨om, K. Optimal control of markov processes with incomplete state information.Journal of Mathematical Analysis and Applications, 10 (1):174–205, 1965. ISSN 0022-247X. doi: https://doi.org/10.1016/0022-247X(65)90154-X. URL https://www.sciencedirect.com/ science/article/pii/0022247X6590154X. 14 En...

work page doi:10.1016/0022-247x(65)90154-x 2025

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Global Table State

Global State Distillation and Information CompressionIn tool-based TableQA, the observation ot is spatially limited to a small window of rows and columns, creating a significant information gap where the agent lacks visibility into the “Global Table State”Tt. Formally, the state entropy remains high given only the observation, H(T t |o t)≫0 . The scalar r...

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imagination gap

Mitigation of Stochastic Drift (ϵ-Accumulation)The tool-based transition is modeled as Tt+1 =P(T t, at, θat) +ϵ t, where ϵt represents the “imagination gap” between the LLM’s predicted outcome and the actual tabular transformation. In code-based generation or unsupervised tool-calling, these discrepancies are unobserved, leading to a compounding errorPS l...

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gradient

Convergence via Density of Search SignalsCode-based generation often suffers from sparse terminal rewards, whereas our framework utilizes rt to create adense reward environment. The trajectory reward R(τ) = P γlrl transforms the search into a directed optimization problem. The scalar rt provides a “gradient” for the agent; if an action at fails to maximiz...

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nX i=1 Zi −E nX i=1 Zi ! ≤ −t # ≤exp − 2t2 Pn i=1(1)2 By choosingt=ϵn, Pr

Grounding of Latent State UtilityAs noted in (Wang et al., 2025b), LLMs fail to attend to all tokens, and many atomic operations (e.g., casting types or re-indexing) result in latent changes to Tt+1 that are invisible in the snapshot ot+1. In an unsupervised setting, the agent suffers fromperceptual aliasing, where it cannot distinguish between a producti...

work page arXiv 2023

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Assume an answer always exists

If a name/entity mentioned in the question is not found in df, make sensible inferences to map the entity to the table. Assume an answer always exists

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Tips (use if necessary)

If the DataFrame contains unit symbols (e.g., $) or commas as thousand separators, use StringOperationTool()to clean the data and parse values into numbers before further computation. Tips (use if necessary). • Some columns may contain only a single group/category. • The toolset computes time differences indays. • The question may contain typos; interpret...

work page