Paper Citation Record · LEDGER

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding

As of 6 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2508.18676.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.18676 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:22:50.938199Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

verified exact5
verified fuzzy0
unresolved38
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2a25411-95a3-407a-a8a3-caefed1c0cdf · outbound

This paper cites H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:22:51.599284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2de5a514-6e0c-4836-840b-4f516ef72eeb · outbound

This paper cites Many-Shot In-Context Learning.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Many-Shot In-Context Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.712218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cef81262-de53-44ea-a679-d72df9925769 · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.718279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae042973-3715-4ae4-870e-0d95f9b0f27c · outbound

This paper cites Cafarella, Alon Halevy, Daisy Zhe Wang, Eugene Wu, and Yang Zhang.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Cafarella, Alon Halevy, Daisy Zhe Wang, Eugene Wu, and Yang Zhang

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.723980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48cd6a01-ca60-4fef-b9d4-304621a090d4 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.729618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e075d76-9a2d-4377-8190-c2857f497b2f · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:51.813154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2de2a41f-b63d-4071-9f11-6403757b0a4c · outbound

This paper cites Steering Large Language Models between Code Execution and Textual Reasoning.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Steering Large Language Models between Code Execution and Textual Reasoning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.740416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd0a8fa8-4406-4702-90a9-19262b8c333c · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:51.795092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d4a24f34-8d86-4d0d-9326-4dfdd7a8447a · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 9

Resolution: verified exact; doi, observed 2026-08-05T16:22:51.035652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 648e49c1-fada-4ae7-b55c-dfd5c2e1fd36 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:51.776763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c92d935f-5360-45bd-823c-6e906623d25f · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.762735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbf281ee-c15b-4e3a-b418-d7d51a551176 · outbound

This paper cites Reinforced Self-Training (ReST) for Language Modeling.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Reinforced Self-Training (ReST) for Language Modeling

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.767606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcbe7d90-ab51-423e-9af4-f49e6f60e82f · outbound

This paper cites DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.772814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a57fe15-3d1c-4ede-a7cb-ff6a50585a3f · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.778057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1bd013c-ea94-4279-abd2-78ada6aff804 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:51.758294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c0e2dce3-80da-48ee-9047-8326fa16bf56 · outbound

This paper cites Large Language Models Cannot Self-Correct Reasoning Yet.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Large Language Models Cannot Self-Correct Reasoning Yet

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.787817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8e6e0fd-56ac-41d9-bf16-9f112015f9c2 · outbound

This paper cites StructGPT: A General Framework for Large Language Model to Reason over Structured Data.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding StructGPT: A General Framework for Large Language Model to Reason over Structured Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.792809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5e9d5e3-38ab-4053-9719-c340687969dc · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.798682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2cbb05d-7954-4223-84d7-e5dde5e17109 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:51.737202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7338b5a2-b0fc-4bc3-902a-5d942da7f062 · outbound

This paper cites Table-GPT: Table-tuned GPT for Diverse Table Tasks.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Table-GPT: Table-tuned GPT for Diverse Table Tasks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.809542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daadfe73-90ef-4bad-a2e0-71ef875d478f · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:51.715600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cd0b98e2-6ef8-4d5a-aa30-e170bc2c8bb3 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:51.696811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ced3f6b9-097b-4db9-a574-351596588d7a · outbound

This paper cites Rethinking Tabular Data Understanding with Large Language Models.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Rethinking Tabular Data Understanding with Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.824293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 524a0937-2b4e-4ad7-a2cc-cce4db8b2995 · outbound

This paper cites CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.829373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38992f59-4837-4aa9-87e0-6e3933628973 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 25

Resolution: malformed identifier; no resolver link, observed 2026-08-05T16:22:50.834346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd05dfd8-238c-4ecb-ac15-f41a82e92159 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.839164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f2443df-323a-4fd6-aba3-f253945c9f43 · outbound

This paper cites CABINET: Content Relevance based Noise Reduction for Table Question Answering.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding CABINET: Content Relevance based Noise Reduction for Table Question Answering

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.845445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35f10aa1-8893-4452-b72e-9bb04b74ef60 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:51.676394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 74fadc04-3375-41ec-9c33-da8c6e8c4019 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.857212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71059b91-6525-46b3-98d2-7379c09ed54a · outbound

This paper cites Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.862355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92bd4919-b8ac-4109-8472-5089b7716ff5 · outbound

This paper cites an unresolved cited work.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:51.644272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a976de8e-c5ce-4d2d-9b79-b85b4fa3425d · outbound

This paper cites Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.873494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ac41209-30f1-41d8-afc6-7ed0cbb5abf1 · outbound

This paper cites ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:22:51.249566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e9a4bce6-1cc6-4acb-a236-43318a3ae764 · outbound

This paper cites UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.884020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3320137-faea-44dc-b14e-78d80162c814 · outbound

This paper cites Corrective Retrieval Augmented Generation.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Corrective Retrieval Augmented Generation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.890723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fc7af22-d5b5-4e62-b488-ecbf0d9befef · outbound

This paper cites UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.897110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51ef407c-a18d-48b2-82a3-ca162a7c8d5b · outbound

This paper cites Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:22:51.170253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 64223b63-9882-44f1-9d82-987093b72b33 · outbound

This paper cites TextGrad: Automatic "Differentiation" via Text.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding TextGrad: Automatic "Differentiation" via Text

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.907232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ab8e126-948c-4e24-8439-b0a1cde8716d · outbound

This paper cites TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.912101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d7f51e5-05cc-4809-83e5-8c46378e393d · outbound

This paper cites TableLlama: Towards Open Large Generalist Models for Tables.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding TableLlama: Towards Open Large Generalist Models for Tables

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.917417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43632370-f14a-4c6f-8c06-f900a0f4cb95 · outbound

This paper cites ReAcTable: Enhancing ReAct for Table Question Answering.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding ReAcTable: Enhancing ReAct for Table Question Answering

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.922518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5904bc5b-ba22-48b1-bfa8-37c88207ff9b · outbound

This paper cites ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:22:51.072117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ba15425c-a180-438b-9be1-704ca0d03141 · outbound

This paper cites online" 'onlinestring :=.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding online" 'onlinestring :=

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.932869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a15b98c3-a174-4dd8-9b49-3d97f3ca45a6 · outbound

This paper cites write newline.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding write newline

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:50.938199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.