RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals

Dingzirui Wang; Keyan Xu; Qingfu Zhu; Wanxiang Che; Xuanliang Zhang

REVIEW 3 major objections 4 minor 47 references

RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that a training-free prompting method, Row-of-Thought (RoT), which makes an LLM traverse a table row by row and reflect between traversals, beats long chain-of-thought reasoning models on table question answering by an…

desk verdict Useful, simple prompting method for table reasoning with mostly honest experiments; the RLLM baseline choice overstates the headline gap but the core result is solid enough to review. read the letter →

arxiv 2505.15110 v1 pith:POCNTTFW submitted 2025-05-21 cs.CL

Xuanliang Zhang , Dingzirui Wang , Keyan Xu , Qingfu Zhu , Wanxiang Che This is my paper · ORCID

classification cs.CL

keywords tablereasoningrow-wisetraversalchain-of-thoughtlargelanguagemodelshallucinationmitigationtraining-freepromptingWikiQuestionsBench

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that the main cost of high performance on table question answering is not reasoning-specialised training but where the model looks. Its method, Row-of-Thought (RoT), prompts any LLM to read the table row by row, update an intermediate answer at each row, and when one pass is not enough, reflect and start another traversal. On three table benchmarks, RoT run on ordinary instruction-tuned models beats long chain-of-thought reasoning models by an average of 4.3 percent, and using the same reasoning model it adds about 2.4 percent while cutting hallucinations. The authors see this as evidence that sequential row-wise attention is a cheap, training-free substitute for long reasoning traces in table tasks.

What carries the argument

The load-bearing mechanism is the row-wise traversal loop. At each traversal the model processes rows in order, emits a short reasoning step and an intermediate result per row, then decides whether to stop or to reflect and start another traversal; the model is never allowed to answer without completing a traversal, and iteration lets multi-hop questions be answered across several passes. That forcing step is what carries the argument: it scales reasoning length by table structure rather than by free-form deliberation, and the ablation data show that removing either the row-wise traversal or the iteration degrades accuracy on every dataset.

What would settle it

Run the same prompts with a frontier reasoning model (for example, full DeepSeek-R1 or an OpenAI o-series model) on WikiTableQuestions and TableBench, using Long CoT with zero-shot prompting. If that model's accuracy reaches or exceeds RoT's 78.7 and 44.8, the claim that RoT outperforms RLLMs on tables would be falsified for stronger reasoning models. A smaller check: swap R1-Qwen-7B for a general-purpose 7B reasoning model and see whether the reported average gain survives.

Watch

Extended reading notes

Core claim

The central discovery is that forcing a model to traverse a table row by row, and to treat each row as a small reasoning step, gives table question answering a reliability boost that long chain-of-thought models do not provide on their own. When RoT is used with non-reasoning LLMs, it outperforms reasoning-LLM Long CoT by an average of 4.3% across WikiTableQuestions, HiTab, and TableBench, and it reaches state-of-the-art numbers among comparable-scale models on WikiTableQuestions (78.7) and TableBench (44.8). The authors attribute the gains to reduced table-content hallucination: the model cannot skip or misremember rows it must address one at a time, and the iteration step lets it correct a mistaken traversal.

Load-bearing premise

The load-bearing premise is that the four reasoning models used as baselines fairly represent what Long CoT can do on tables; one of them is a math-optimized variant that does poorly for reasons unrelated to long reasoning, and a stronger reasoning model could erase the reported 4.3% edge.

Editorial extensions

If this is right

Non-reasoning LLMs can reach or exceed reasoning-specialised models on table QA without fine-tuning, so high table-reasoning performance no longer requires Long CoT training data.
Hallucinated table content, the main failure mode of Long CoT in this setting, is reduced by construction because the model must attend to every row.
RoT also improves RLLMs by about 2.4%, so the prompt can be stacked on top of reasoning models rather than replacing them.
Because non-RLLMs with RoT use fewer reasoning tokens than Long CoT, inference is cheaper as well as more accurate.
State-of-the-art results on WikiTableQuestions and TableBench among comparable models suggest the method transfers across flat tables; HiTab remains competitive but not SOTA.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: if the benefit is really forced attention, the same row-by-row prompt should transfer to other structured evidence, such as JSON arrays or database query results; a small experiment on a structured-data QA set would test that.
Editorial inference: the paper compares against distilled RLLMs at 7-70B scale, not frontier reasoning models; the 4.3% headline could shrink or reverse against a stronger Long CoT model, so the claim should be read as beats comparable distilled RLLMs.
Editorial inference: on very large tables, completing a full traversal may exceed context windows before the answer is produced; combining RoT with row filtering or chunked traversal is a natural extension the paper leaves untested.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Xuanliang, here's my read of RoT.

The core idea is straightforward: instead of letting an LLM free-run a long chain of thought over a table, force it to walk row by row, accumulate intermediate results, and optionally reflect and re-traverse. It's a prompt, not a method, but it's a good prompt. The experiments are unusually clean for this genre: consistent gains across three datasets and four model pairs, ablations showing both the row-wise traversal and the iteration contribute, and an error analysis that mostly agrees with the mechanism (hallucination drops). The token-efficiency plot is a nice touch; RoT gets better accuracy than Long CoT with fewer tokens on correct answers.

The main soft spot is the headline comparison. The '4.3% over RLLMs' rests on DeepSeek-R1-Distill models only, and one of them, R1-Qwen-7B, is built on Qwen2.5-Math-7B, which the authors themselves exclude as a non-RLLM baseline because it's math-specialized. That's a fair catch by the stress-test note. Recomputing without R1-Qwen-7B drops the average to about 3.8%, and a stronger full RLLM could shrink it further. But I don't think the paper collapses. The gains over Short CoT on the same non-RLLM are large and consistent, and that's the more defensible claim. The RLLM comparison is an attention-grabbing headline, not the load-bearing result.

Other soft spots: no error bars or significance tests, and the one-shot setting and row-wise traversal are chosen after looking at test-set curves. That's mild overfitting to the benchmark; it doesn't invalidate the method, but the authors should say so and ideally add a held-out config or more seeds.

The paper knows its limits: HiTab is not SOTA, and the limitation section is honest about scope. Citations look appropriate, and the comparison with prior methods in the appendix is fair.

Bottom line: this is a competent empirical paper that deserves a serious referee. The method is simple enough to be reproduced and tested by others, and the failure modes are openly discussed. I'd give it a round of review with the expectation that the authors tighten the baseline story and add variance information.

Referee Report

3 major / 4 minor

Summary. This paper proposes Row-of-Thought (RoT), a training-free prompting strategy for table reasoning in which the model is instructed to traverse the input table row by row, accumulate intermediate results, and optionally reflect and re-traverse. The method is evaluated on WikiTableQuestions, HiTab, and TableBench with four non-RLLMs (Llama3.1-8B, Llama3.3-70B, Qwen2.5-7B, Qwen2.5-32B) and four RLLMs (DeepSeek-R1-Distill variants). The main empirical claims are: (1) non-RLLMs prompted with RoT outperform RLLMs with Long CoT by an average of 4.3%; (2) RoT additionally boosts RLLMs by an average of 2.4%; and (3) RoT matches or exceeds prior SOTA table-reasoning methods at comparable model scales. The paper also reports ablations of iteration and traversal, analyses of error types, traversal counts, reasoning length, table size, traversal unit, and demonstration count.

Significance. If the headline comparison were robust, the contribution would be practically valuable: it offers a training-free, inference-time prompt that increases accuracy while using fewer reasoning tokens than Long CoT, and the ablation and case studies support the proposed mechanisms (attention to rows, iterative reflection) as the source of gains. The paper ships explicit prompts, detailed case studies, and a substantial set of analyses (error categories, traversal counts, table-size scaling, demonstration count), which make the method easy to reproduce and compare. The central risk is baseline selection: Section 3.1 uses only DeepSeek-R1-Distill models as 'RLLMs', including R1-Qwen-7B, which is built on a math-specialized base that the authors themselves exclude as a non-RLLM baseline, so the magnitude of the headline advantage over RLLMs is not yet established.

major comments (3)

[§3.1, Table 1] The headline average advantage of 4.3% over RLLMs depends heavily on the choice of RLLM baselines. All four RLLMs are DeepSeek-R1-Distill models, and one of them, R1-Qwen-7B, is derived from Qwen2.5-Math-7B, a math-specialized base that the paper itself excludes as a non-RLLM baseline because it is suboptimal on table reasoning. The three per-dataset gaps contributed by the R1-Qwen-7B row (8.4 on WikiTQ, 8.7 on HiTab, 0.7 on TableBench) are the largest in Table 1; removing them reduces the reported average from 4.3% to roughly 3.9%. Because the abstract and introduction claim that non-RLLMs 'outperform' RLLMs without qualification, this baseline composition is load-bearing. Please add a stronger, general-purpose RLLM baseline (e.g., full DeepSeek-R1 or a non-math-based distill) and re-compute the average, or explicitly report and qualify the version without the R1-Qwen-7B row.
[§3.2, Table 1] All main results are single runs with no variance estimates, confidence intervals, or significance tests. Several per-dataset gains are small (e.g., +0.9 on WikiTQ and +0.8 on TableBench for the 8B Llama pair, +1.0 and +0.5 for R1-Llama-8B with RoT), so it is unclear whether the differences are above run-to-run noise. Please provide results over at least three seeds or bootstrap resampling and paired tests (e.g., McNemar's or a permutation test) for the RoT-vs-Long-CoT and RoT-vs-Short-CoT comparisons.
[§3.4.5, §3.4.6, Figures 8 and 9] The traversal unit (row) and the number of demonstrations (one) are selected by comparing test-set performance on the same benchmarks used for the headline numbers. This is a form of selection on the evaluation data and should be disclosed in the experimental setup. Please either pre-register these choices, select them on a development split, or explicitly state that the configurations were chosen after observing test-set results; otherwise the abstract's claim of 'proving its effectiveness' is overstated.

minor comments (4)

[§4.1] The phrase 'Long Long CoT' is a typo; it should read 'Long CoT'.
[References] The entries listed as Jiang et al. 2024b and Jiang et al. 2024c are the same paper ('Seek and solve reasoning for table question answering'); one should be removed and the in-text citations adjusted.
[Figure 6 caption] The phrase 'across three datasets on WikiTQ' is confusing because the figure reports WikiTQ only; the caption should say 'on WikiTQ'.
[Table 3] The caption contains a typo ('campared' should be 'compared').

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: RoT is an empirical prompting method whose gains are measured against external baselines and supported by independent ablations.

full rationale

This paper is an empirical prompt-engineering study rather than a formal derivation, so there is no equation-level circularity in the sense of a predicted quantity being equal to an input by construction. The central claim is that RoT, a row-wise traversal prompt, improves table reasoning accuracy over Short CoT and Long CoT baselines. These baselines are external models (Llama, Qwen, DeepSeek-R1-Distill) whose outputs are not defined in terms of RoT's parameters, so the comparison is not self-referential. The ablations in Table 3 (w/o Iteration and w/o Traversal) independently isolate the contributions of the two named components, and the traversal-unit experiments in Figure 8 provide further independent evidence; these are genuine ablations, not renamings of the input. The paper's design choices, such as the one-shot demonstration and the row traversal unit, are selected through experiments on WikiTQ, but this is standard hyperparameter or prompt selection rather than fitting a parameter and then calling a closely related quantity a prediction; moreover, the same demonstration is transferred to TableBench and the HiTab demonstration is drawn from a different dataset. The cited works for LLM self-reflection and DeepSeek-R1 are external and do not carry a load-bearing uniqueness claim. The most plausible concern is baseline representativeness: the RLLM set consists of distilled DeepSeek-R1 models, and R1-Qwen-7B inherits a math-optimized base. That is a correctness or external-validity risk, not circularity, because the reported numbers do not reduce to the paper's own assumptions by definition. No load-bearing self-citation chain or ansatz-smuggling via citation is present. Therefore, under the rubric that circularity requires exhibiting a specific reduction of a result to its inputs, this paper has no significant circularity.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The ledger captures the two explicit benchmark-driven design choices (shot count and traversal unit) plus the unstated assumptions about reflection reliability, attention and hallucination, and demonstration representativeness. There are no invented entities or fitted numerical constants beyond these choices.

free parameters (2)

Number of in-context demonstrations = 1
Chosen based on WikiTQ test performance in Figure 9; one-shot selected as the balance point.
Traversal unit = row
Row traversal chosen as default after comparing row, column, and cell traversal on all three test datasets in Figure 8.

assumptions (3)

domain assumption LLMs possess reliable self-reflection capabilities that can be invoked by prompting
Section 2.3: the model 'can choose to reflect on the previous reasoning' and the paper claims reflection capabilities are 'equipped in LLMs' (Section 1), without measuring reflection reliability in the tested models.
domain assumption Sequential row-wise processing increases attention to table cells and thereby reduces hallucination
Section 2.2: 'mitigates hallucination by forcing the model to attend to the entire table content'; the paper does not directly measure attention or hallucination rates, it infers this from error categories.
ad hoc to paper The one-shot demonstration selected from the training set is representative and not cherry-picked
Section 3.1 says 'We select the same demonstration from the WikiTQ training set' but no procedure for selection is given; the choice can affect all comparisons.

how reviews work

0 comments

Cite this review

Pith. "Pith review of RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals." pith.science (2026). https://pith.science/paper/POCNTTFW

@misc{pith2026250515110,
  author       = {Pith},
  title        = {Pith review of: RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/POCNTTFW}},
  note         = {Machine review of arXiv:2505.15110}
}

read the original abstract

The table reasoning task, crucial for efficient data acquisition, aims to answer questions based on the given table. Recently, reasoning large language models (RLLMs) with Long Chain-of-Thought (Long CoT) significantly enhance reasoning capabilities, leading to brilliant performance on table reasoning. However, Long CoT suffers from high cost for training and exhibits low reliability due to table content hallucinations. Therefore, we propose Row-of-Thought (RoT), which performs iteratively row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal. Scaling reasoning length by row-wise traversal and leveraging reflection capabilities of LLMs, RoT is training-free. The sequential traversal encourages greater attention to the table, thus reducing hallucinations. Experiments show that RoT, using non-reasoning models, outperforms RLLMs by an average of 4.3%, and achieves state-of-the-art results on WikiTableQuestions and TableBench with comparable models, proving its effectiveness. Also, RoT outperforms Long CoT with fewer reasoning tokens, indicating higher efficiency.

Figures

Figures reproduced from arXiv: 2505.15110 by the authors.

**Figure 1.** Compared with (a) Long CoT, (b) ROT necessitates no training, exhibits lower costs, and enhances reliability by mitigating hallucination via sequentially row-wise table traversal. capabilities (Li et al., 2025b; Qian et al., 2025). This improvement stems from Long CoT, which sequentially scales the length of CoT, engages in selfreflection, and explores diverse reasoning paths, in contrast to the shallow and direc… view at source ↗

**Figure 2.** The overview of ROT with the input and output of the example. The instruction is highlighted with blue and the iterative row-wise table traversal process is highlighted with green. a novel method that enhances table reasoning by guiding the model to perform iteratively row-wise traversal reasoning, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 4.** The distribution of reasons for iterative traver [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

Figures from the paper (10 more)

**Figure 5.** Figure 5: The distribution of table traversal counts and the corresponding performance of [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Comparison of average reasoning lengths for [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 8.** Figure 8: Comparison of ROT traversing the table with different units across three datasets. 0-50 50-100 100-200 200-400 400+ Table Size 0.40 0.45 0.50 0.55 0.60 0.65 0.70 Performance Short CoT w. non-RLLM RoT w. non-RLLM Long CoT w. RLLM RoT w. RLLM [PITH_FULL_IMAGE:figures/fu…

**Figure 9.** Figure 9: Performance of ROT on WikiTQ with varying numbers of demonstrations. et al., 2020; Liu et al., 2024a). Cell-wise traversal resulted in a significant performance decrease, due to its overly fine-grained reasoning granularity and the presence of numerous irrelevant cells…

**Figure 10.** Figure 10: Long CoT underperforms ROT with RLLMs due to the error types, with their distribution. sequential scaling through row-wise traversal, leading to improved table reasoning capabilities. B.3 Why ROT with RLLMs outperforms Long CoT? To analyze specifically why ROT with R…

**Figure 11.** Figure 11: ROT outperforms Long CoT due to the hallucination issue of Long CoT. The row-wise table traversal process is highlighted in green, and the hallucinated reasoning steps are highlighted in red. Question what city is at the top of the list? Table Team City Venue … Past S…

**Figure 12.** Figure 12: ROT outperforms Long CoT due to the misunderstanding issue of Long CoT. The row-wise table traversal process is highlighted in green, and the misunderstanding reasoning steps are highlighted in red [PITH_FULL_IMAGE:figures/full_fig_p014_12.png]

**Figure 13.** Figure 13: ROT outperforms Long CoT due to the locating error of Long CoT. The row-wise table traversal process is highlighted in green, and the incorrect linking reasoning steps are highlighted in red [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]

**Figure 14.** Figure 14: ROT performs iterative row-wise table traversals due to Multi-Hop Reasoning. The row-wise table traversal process is highlighted in green. Question how many episodes in series 10 did fran carroll write? Table Output To answer the question, I need to find out how many …

**Figure 15.** Figure 15: ROT performs iterative row-wise table traversals due to Reflection. The row-wise table traversal process is highlighted in green [PITH_FULL_IMAGE:figures/full_fig_p017_15.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

47 extracted references · 44 canonical work pages

[1]

Hmm, maybe it's not applicable or the data isn't provided

One: The guest judge(s) column is blank. Hmm, maybe it's not applicable or the data isn't provided. I'll skip this one for now

work page
[2]

Same as the first, I'll skip this

Two: The guest judge(s) column is blank. Same as the first, I'll skip this

work page
[3]

That's the one I'm looking for, but I need to find the next one

Three: The guest judge(s) is Kelly Brook. That's the one I'm looking for, but I need to find the next one

work page
[4]

In The Twelfth International Conference on Learning Representa- tions

CABINET: Content relevance-based noise re- duction for table question answering. In The Twelfth International Conference on Learning Representa- tions. Lingfei Qian, Weipeng Zhou, Yan Wang, Xueqing Peng, Han Yi, Jimin Huang, Qianqian Xie, and Jianyun Nie

work page
[5]

That’s another one, so three

The Immaculate Conception, Haslingden: Founded in 1854. That’s another one, so three

work page
[6]

In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Process- ing, pages 14786–14802, Singapore

Large language models are complex table parsers. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Process- ing, pages 14786–14802, Singapore. Association for Computational Linguistics. Tianshi Zheng, Yixiang Chen, Chengxi Li, Chunyang Li, Qing Zong, Haochen Shi, Baixuan Xu, Yangqiu Song, Ginny Y . Wong, and Simon See. 2025. T...

arXiv 2023
[7]

That’s in the 1800s, so four

St James the Less, Rawtenstall: Founded in 1828. That’s in the 1800s, so four. So the answer is four. Answer: 4 The demonstration of ROT used on HiTab admissions to adult custody, by sex and by indigenous identity and jurisdiction, 2018/2019 | jurisdiction | male | male | male | male | male | male | female | female | female | female | female | female | |:...

work page 2018
[10]

Guest judge(s)

Four: The guest judge(s) is Louis Walsh. That's the next one after Kelly Brook. … So the answer is Louis Walsh. Answer: Louis Walsh Output of Long CoT <think> Okay, so I need to figure out who the guest judge was after Kelly Brook on the show. … Looking at the "Guest judge(s)" column, I see that for Series One, the guest judge is listed as N/A. Series Two...

work page

Show all 47 references

[11]

Aboomoslem, Mashhad: Mashhad is the city
[12]

Bargh Shiraz, Shiraz: Shiraz is the city
[13]

Damash Gilan, Rasht: Rasht is the city
[14]

Past Season

Est. Ahvaz, Ahvaz: Ahvaz is the city. … The first city in the list is Mashhad, which is the city of the team Aboomoslem. Answer: Mashhad Output of Long CoT <think> Okay, so I need to figure out which city is at the top of the list based on table … First, I should understand wh...

1947
[15]

That’s in the 1800s, so that’s one

St Mary, Bacup: Founded in 1852. That’s in the 1800s, so that’s one
[16]

Also in the 1800s, so that’s two

Our Immaculate Mother & St Anselm, Whitworth: Founded in 1860. Also in the 1800s, so that’s two
[17]

Hmm, maybe it’s not applicable or the data isn’t provided

St Joseph, Stacksteads: The "Founded" column is blank. Hmm, maybe it’s not applicable or the data isn’t provided. I’ll skip this one for now
[18]

That’s in the early 1900s, so not in the 1800s

St Joseph & St Peter, Newchurch-In-Rossendale: Founded in 1915. That’s in the early 1900s, so not in the 1800s

1915
[20]

That’s in the 1900s, so not relevant

St Veronica (Chapel of Ease), Helmshore: Founded in 1959. That’s in the 1900s, so not relevant

1959
[22]

Newfoundland and Labrador: Non-indigenous males have a percent change of -9%
[23]

Prince Edward Island: Non-indigenous males have a percent change of 11%
[24]

It shows a -7% change for non-indigenous males

Nova Scotia: Non-indigenous males have a percent change of 3%... It shows a -7% change for non-indigenous males. That seems to be the overall percentage decrease across all provinces and territories combined. I think that’s the answer because the total row aggregates all the d...
[25]

You must traverse the table row by row once and only once
[26]

Here is an example:

Represent your answer with: Answer: <Your Answer>. Here is an example: ... Based on the above example, you need to traverse the table below and answer the question. {Table} Question: {Question} Solution: The prompt of ROT without Traversal Your task is to think step by step to...
[27]

You must reflect your reasoning iteratively until you get a correct answer
[28]

candy sale

Represent your answer with: Answer: <Your Answer>. Here is an example: ... Based on the above example, you need to answer the question below. {Table} Question: {Question} Solution: Table 5: The prompts used in the ablation experiments. Question which episode came next after "c...

1994
[29]

The Charity

Series 1, Season 1, Title: "The Charity", Original air date: October 15,
[31]

Candy Sale

Series 1, Season 1, Title: "Candy Sale", Notes: ..., Original air date: January 26, 1995. This is the episode I'm looking for

1995
[32]

The Big Bully

Series # 1, Season # 1, Title: "The Big Bully", Notes: ..., Original air date: February 2, 1995. This is the next episode after "Candy Sale". So the answer is "The Big Bully". Answer: The Big Bully Output of Long CoT <think> Okay, so I need to figure out which episode comes af...

1995
[34]

Michigan - 1 player (round 1)
[35]

Alabama - 2 players (rounds 3, 10)
[36]

Oregon - 1 player (round 5)
[37]

Tennessee - 1 player (round 6) … Now, let's calculate the pick efficiency for each college in the first 10 rounds:
[38]

Michigan - 1 player / 8 players = 1/8
[39]

Alabama - 2 players / 8 players (rounds 3 and 10) = 2/8 = 1/4
[40]

Oregon - 1 player / 8 players = 1/8
[41]

Tennessee - 1 player / 8 players = 1/8 … Alabama’s two players in the first 10 rounds give it the highest pick efficiency. Answer: alabama round pick overall name position college 1 10 10 forest evashevski rb michigan 3 10 25 fred davis ot alabama 5 10 40 jim stuart ot oregon ...
[42]

Not in series 10

#1: Aired on September 14, 1997. Not in series 10

1997
[43]

Not in series 10

#2: Aired on September 28, 1997. Not in series 10. …

1997
[44]

Not in series 10

#18: Aired on February 22, 1998. Not in series 10. Wait, I made a mistake! I need to look for the episodes written by Fran Carroll, not the episodes in series 10. … Let me go through each row one by one

1998
[45]

#1: Written By Simon J. Sharkey. Not Fran Carroll
[46]

#2: Written By Simon J. Sharkey. Not Fran Carroll. …
[47]

#18: Written By Simon J. Sharkey. Not Fran Carroll. So, Fran Carroll wrote only one episode. Answer: 1 # Directed By Written By Original Air Date 1 Frank W. Smith Simon J. Sharkey September 14, 1997 2 Frank W. Smith Simon J. Sharkey September 28, 1997 3 Gerry Poulson David Hum...

2023
[186]

Adarsh Kumar, Hwiyoon Kim, Jawahar Sai Nathani, and Neil Roy

Springer. Adarsh Kumar, Hwiyoon Kim, Jawahar Sai Nathani, and Neil Roy. 2025. Improving the reliability of llms: Combining cot, rag, self-consistency, and self- verification. Preprint, arXiv:2505.09031. Qianlong Li, Chen Huang, Shuai Li, Yuanxin Xiang, Deng Xiong, and Wenqiang...

2025 arXiv
[1994]

Candy Sale

This is not "Candy Sale". …
[1995]

pick efficiency

The next episode after that would be episode 13. So, the answer should be episode 13. </think> Answer: 13 Figure 13: ROT outperforms Long CoT due to the locating error of Long CoT. The row-wise table traversal process is highlighted in green, and the incorrect linking reasonin...
[2023]

Transactions of the Association for Computational Linguistics , 11:227–249

Transformers for tabular data representation: A survey of models and applications. Transactions of the Association for Computational Linguistics , 11:227–249. Sourav Banerjee, Ayushi Agarwal, and Saloni Singla
[2024]

Preprint, arXiv:2409.05746

Llms will always hallucinate, and we need to live with this. Preprint, arXiv:2409.05746. Lang Cao. 2025. Tablemaster: A recipe to advance table understanding with language models. Preprint, arXiv:2501.19378. Yihan Cao, Shuyi Chen, Ryan Liu, Zhiruo Wang, and Daniel Fried. 2023....

2025 arXiv
[2025]

Preprint, arXiv:2502.08127

Fino1: On the transferability of reasoning en- hanced llms to finance. Preprint, arXiv:2502.08127. Tian Qin, David Alvarez-Melis, Samy Jelassi, and Eran Malach. 2025. To backtrack or not to back- track: When sequential search limits model reason- ing. Preprint, arXiv:2504.0705...

2025 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Hmm, maybe it's not applicable or the data isn't provided

One: The guest judge(s) column is blank. Hmm, maybe it's not applicable or the data isn't provided. I'll skip this one for now

work page

[2] [2]

Same as the first, I'll skip this

Two: The guest judge(s) column is blank. Same as the first, I'll skip this

work page

[3] [3]

That's the one I'm looking for, but I need to find the next one

Three: The guest judge(s) is Kelly Brook. That's the one I'm looking for, but I need to find the next one

work page

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In The Twelfth International Conference on Learning Representa- tions

CABINET: Content relevance-based noise re- duction for table question answering. In The Twelfth International Conference on Learning Representa- tions. Lingfei Qian, Weipeng Zhou, Yan Wang, Xueqing Peng, Han Yi, Jimin Huang, Qianqian Xie, and Jianyun Nie

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That’s another one, so three

The Immaculate Conception, Haslingden: Founded in 1854. That’s another one, so three

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In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Process- ing, pages 14786–14802, Singapore

Large language models are complex table parsers. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Process- ing, pages 14786–14802, Singapore. Association for Computational Linguistics. Tianshi Zheng, Yixiang Chen, Chengxi Li, Chunyang Li, Qing Zong, Haochen Shi, Baixuan Xu, Yangqiu Song, Ginny Y . Wong, and Simon See. 2025. T...

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That’s in the 1800s, so four

St James the Less, Rawtenstall: Founded in 1828. That’s in the 1800s, so four. So the answer is four. Answer: 4 The demonstration of ROT used on HiTab admissions to adult custody, by sex and by indigenous identity and jurisdiction, 2018/2019 | jurisdiction | male | male | male | male | male | male | female | female | female | female | female | female | |:...

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Guest judge(s)

Four: The guest judge(s) is Louis Walsh. That's the next one after Kelly Brook. … So the answer is Louis Walsh. Answer: Louis Walsh Output of Long CoT <think> Okay, so I need to figure out who the guest judge was after Kelly Brook on the show. … Looking at the "Guest judge(s)" column, I see that for Series One, the guest judge is listed as N/A. Series Two...

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