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Paper Citation Record · LEDGER

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task

As of 8 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2506.11986.

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

pith.paper-citation-record.v1
2506.11986 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:05:26.883411Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

46 of 46 outbound references displayed

  • verified exact1
  • verified fuzzy21
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69ae14cb-dd54-4b67-996a-66c8acbb8f1d · outbound

This paper cites A survey on employing large language models for text-to-sql tasks.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task A survey on employing large language models for text-to-sql tasks

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 0fcd2ad1-4d9d-46d3-bd08-8d73bfe6be4c · outbound

This paper cites Sql-r1: Training natural language to sql reasoning model by reinforcement learning.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Sql-r1: Training natural language to sql reasoning model by reinforcement learning

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.693235Z digest=sha256:069c3199266ac6debfd1078e7f9fb59cf89c0537964899fced4ef226eaadec6a

Observation 9ef37166-edfa-41d9-b681-abe30f15a9b6 · outbound

This paper cites Re-examining the role of schema linking in text-to-SQL.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Re-examining the role of schema linking in text-to-SQL

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 02a869db-2464-4d74-b848-f34934b3126a · outbound

This paper cites Text-to-sql empowered by large language models: A benchmark evaluation.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Text-to-sql empowered by large language models: A benchmark evaluation

Reference 4

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raw_fallback, observed 2026-08-07T01:05:27.931934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a6ae8a4c-f3e7-4540-8804-7c26a0b1ac3f · outbound

This paper cites Demonstration of db-gpt: Next generation data interaction system empowered by large language models.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Demonstration of db-gpt: Next generation data interaction system empowered by large language models

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-07T01:05:27.917371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c76a87aa-7006-4d29-8924-a53f8b9bfd34 · outbound

This paper cites Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models

Reference 6

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no resolver link, observed 2026-08-07T01:05:26.711952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.711952Z digest=sha256:6ee05e0cbe9750000f1d5cfa771459de5da682bcbed1ac1e5a502bf2676c1450

Observation da8aaabe-a169-4d70-b3f8-a022a00dd5e9 · outbound

This paper cites Sean Wang.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Sean Wang

Reference 7

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raw_fallback, observed 2026-08-07T01:05:27.903282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation bd159694-5368-4d13-8d8c-8a59eb804af8 · outbound

This paper cites Din-sql: decomposed in-context learning of text-to-sql with self-correction.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Din-sql: decomposed in-context learning of text-to-sql with self-correction

Reference 8

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raw_fallback, observed 2026-08-07T01:05:27.889433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.722126Z digest=sha256:309f343ddeadf32a19ae535bb9608b3299a892789f62b087a0941a1bdd375a09

Observation eadb08ca-dfb4-428f-b72f-fe157be53453 · outbound

This paper cites MCS-SQL: Leveraging multiple prompts and multiple-choice selection for text-to-SQL generation.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task MCS-SQL: Leveraging multiple prompts and multiple-choice selection for text-to-SQL generation

Reference 9

Resolution
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raw_fallback, observed 2026-08-07T01:05:27.875351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.726094Z digest=sha256:22f0436ff6f6d5da698a1b944d715d05306b2e1058138d4d2028e64b04a521ee

Observation 30ad1fc6-17a3-41eb-9d63-b35661bd504a · outbound

This paper cites Act-sql: In-context learning for text-to-sql with automatically-generated chain-of-thought.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Act-sql: In-context learning for text-to-sql with automatically-generated chain-of-thought

Reference 10

Resolution
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raw_fallback, observed 2026-08-07T01:05:27.861656Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 222dadea-2aa2-4c05-b242-97518f6be0c2 · outbound

This paper cites DTS-SQL: Decomposed text-to-SQL with small large language models.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task DTS-SQL: Decomposed text-to-SQL with small large language models

Reference 11

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raw_fallback, observed 2026-08-07T01:05:27.847618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.734037Z digest=sha256:b90e6e15426911bbff1794a0ad300fb19b4fca62e4def159f11b7319cb85eb68

Observation f5b746de-bfe0-4126-8aeb-92992d31b9c2 · outbound

This paper cites Instruction tuning text-to-sql with large language models in the power grid domain.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Instruction tuning text-to-sql with large language models in the power grid domain

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:05:27.833818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.738097Z digest=sha256:56cbacf0848514a76a0e27aa28111acbb90f6d12703e0d200368757b89c9b980

Observation 734e384c-47e5-433f-b8b1-546f246ea60f · outbound

This paper cites MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation

Reference 13

Resolution
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local_arxiv, observed 2026-08-07T01:05:27.550109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.742211Z digest=sha256:528e9858f30b5b2f655c261a32e6c33ecb7cf03c9c4f0e581c4f14df689b8bea

Observation 65de95d1-9bdc-4005-bad5-8a33ae5267a3 · outbound

This paper cites DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.746844Z digest=sha256:01a468d1e6fb9e79455b8c4d5f62f1d224423692901313b7dab4b35bbb365c3b

Observation 7beb6259-d82f-4d9c-a245-ba901cb739c2 · outbound

This paper cites Codes: Towards building open-source language models for text-to-sql.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Codes: Towards building open-source language models for text-to-sql

Reference 15

Resolution
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raw_fallback, observed 2026-08-07T01:05:27.819798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.751113Z digest=sha256:baf5322b629fce8116854032d4bf9793b52f99ff4503243a6637e58225fde74b

Observation 613b7d55-3cdd-4a8c-8df7-e346233032e7 · outbound

This paper cites Cogsql: A cognitive framework for enhancing large language models in text-to-sql translation.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Cogsql: A cognitive framework for enhancing large language models in text-to-sql translation

Reference 16

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raw_fallback, observed 2026-08-07T01:05:27.804999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 8042fe2f-da8b-463e-a623-46b7ab1634ab · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

Reference 17

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raw_fallback, observed 2026-08-07T01:05:27.790628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.759049Z digest=sha256:bebcf9b943bf87d29d96edc1d8a515d1ac72817118268c860dbd611f3e93fe74

Observation d71a51c8-d655-4e44-9336-caac7ad71503 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 18

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T01:05:26.763038Z digest=sha256:a8616bd643d65e02a0c5328e2553ce97fd4bb5a95edabc85e21017e604ea9f66

Observation b9459e1d-7454-4c5c-9d6b-c6bcb8474ba0 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 19

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source=pdf_text observed=2026-08-07T01:05:26.767255Z digest=sha256:a0d0a382eed7a2272aa125861b07b0162bc38deb39d5c20f42a782eeb71b3b98

Observation 2ca96158-2563-4e88-942b-3eefc5c66f33 · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 20

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source=pdf_text observed=2026-08-07T01:05:26.771606Z digest=sha256:9b4dc035aa720b7616060dda3ff29da190fbd999baf8df9ccfdd391bb2032832

Observation 30ab0835-abef-46be-821d-04b704d77905 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 21

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source=pdf_text observed=2026-08-07T01:05:26.776323Z digest=sha256:0749454f1b22bfd8c631a0982733337f466f0705b7b5c3bd8c3cf3634acf7414

Observation b823f4fe-71cf-43f0-ae73-aa8a180bea40 · outbound

This paper cites Resdsql: decoupling schema linking and skeleton parsing for text-to-sql.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Resdsql: decoupling schema linking and skeleton parsing for text-to-sql

Reference 22

Resolution
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raw_fallback, observed 2026-08-07T01:05:27.776213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.780551Z digest=sha256:a50775baa57cc202fe547d30f78f3eff2a43aefb47d713a227da90bdc4e8125c

Observation 5983723a-ffe7-469a-9031-cb0816eead97 · outbound

This paper cites Catsql: Towards real world natural language to sql applications.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Catsql: Towards real world natural language to sql applications

Reference 23

Resolution
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raw_fallback, observed 2026-08-07T01:05:27.760372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.784579Z digest=sha256:325e0992d62f70ded8c6c10091fdf630d4aafc607155923ea42f74710ffa8dc9

Observation 9e8c2071-7a97-44dc-b624-a57e50dcc7fa · outbound

This paper cites RAT-SQL: Relation- aware schema encoding and linking for text-to-SQL parsers.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task RAT-SQL: Relation- aware schema encoding and linking for text-to-SQL parsers

Reference 24

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raw_fallback, observed 2026-08-07T01:05:27.744147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.788569Z digest=sha256:28426db63a3d4024a3594588285846dbf15668e9a51f2b0e7a50bf344d6c0c11

Observation 20e35a0b-9270-49cd-b601-b6d00ac91142 · outbound

This paper cites LGESQL: Line graph enhanced text-to-SQL model with mixed local and non-local relations.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task LGESQL: Line graph enhanced text-to-SQL model with mixed local and non-local relations

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:05:27.729383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.792639Z digest=sha256:ebbe0875d38a8d60bfdf3642a1009a2e3eba40b0a2045ef12010cac88974c20f

Observation e909c20a-974a-441e-baec-3a507a30d8bd · outbound

This paper cites C3: Zero-shot Text-to-SQL with ChatGPT.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task C3: Zero-shot Text-to-SQL with ChatGPT

Reference 26

Resolution
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no resolver link, observed 2026-08-07T01:05:26.796600Z

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source=pdf_text observed=2026-08-07T01:05:26.796600Z digest=sha256:3f769f57db067e0b5e6dcb70ed13cad8e20f8ef6d3514f6fdf092ad062d3519f

Observation f6091455-6bd1-4ff3-915a-6df03cb615d7 · outbound

This paper cites Enhancing text-to-SQL capabilities of large language models through tailored promptings.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Enhancing text-to-SQL capabilities of large language models through tailored promptings

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:05:27.713624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.801040Z digest=sha256:18acce4a0d46b8fbbe0028941740dffd2b33d0d53ad6dcd94efa58998872fd2a

Observation 61a5816f-ef18-4f49-abce-70901c034bc3 · outbound

This paper cites PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency

Reference 28

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no resolver link, observed 2026-08-07T01:05:26.805058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.805058Z digest=sha256:b814727bbbcc247a440c651b72cb982d7b0f212376b4f075d2bf0299cb4d47c1

Observation 9763dd9f-85f1-4b89-b7f7-32552f22f6f8 · outbound

This paper cites Sql-to-schema enhances schema linking in text-to-sql.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Sql-to-schema enhances schema linking in text-to-sql

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:05:27.699003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.809526Z digest=sha256:a327707e5899020767a145f6f6dc2361cd20301bd9cc7212517bc5c1a36ace8f

Observation 5b762f72-b5f9-49e0-8813-60144e0466b7 · outbound

This paper cites Spsql: Step-by-step parsing based framework for text-to-sql generation.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Spsql: Step-by-step parsing based framework for text-to-sql generation

Reference 30

Resolution
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raw_fallback, observed 2026-08-07T01:05:27.684479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.813685Z digest=sha256:0ca593e09bcade8f0ce07d18b26ac53b996157f62d3c067fbe5fb8d61176524b

Observation 62846c1d-333f-472a-9e40-82849381d628 · outbound

This paper cites TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios

Reference 31

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source=pdf_text observed=2026-08-07T01:05:26.818254Z digest=sha256:11effbc2e2c06858318ac77893b6ea701a9ab91797857eea24725e168fb18a8f

Observation 85f0016f-2b69-4576-bdbb-b9de8797b4f1 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Tree of thoughts: Deliberate problem solving with large language models

Reference 32

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no resolver link, observed 2026-08-07T01:05:26.822573Z

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source=pdf_text observed=2026-08-07T01:05:26.822573Z digest=sha256:69d50c111f72ec1ada57cc0f2814e9aecb999d7e6b61c115c5f2ae6e2544eb4a

Observation d9e8e808-66f4-4d24-b1ba-66e2cf35e8b4 · outbound

This paper cites Graph chain-of-thought: Augmenting large language models by reasoning on graphs.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Graph chain-of-thought: Augmenting large language models by reasoning on graphs

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:05:27.659420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:05:26.826762Z digest=sha256:abc0ab2d3deb621143b864ebd9c27d1ab89cd1a077c0eed469a255d94f80ae51

Observation 9314b0c2-61ef-45fb-95fb-099941b95cb7 · outbound

This paper cites Thinkless: LLM Learns When to Think.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Thinkless: LLM Learns When to Think

Reference 34

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source=pdf_text observed=2026-08-07T01:05:26.831361Z digest=sha256:daa49d81c9265e28c953dbe52745f5382bf11edaaaa074e66b5a0739e5385d14

Observation cc616748-2b5f-4756-b9d2-4324b1ef92d4 · outbound

This paper cites Think Only When You Need with Large Hybrid-Reasoning Models.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Think Only When You Need with Large Hybrid-Reasoning Models

Reference 35

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source=pdf_text observed=2026-08-07T01:05:26.835657Z digest=sha256:88fe21bc262fc2a5ec26aca41f245f521b84b5236e4c1e26148d6852388edffd

Observation ff41327e-9099-443a-b6f6-a5696b658846 · outbound

This paper cites Reasoning Models Can Be Effective Without Thinking.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Reasoning Models Can Be Effective Without Thinking

Reference 36

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source=pdf_text observed=2026-08-07T01:05:26.840277Z digest=sha256:453139b18ef808ac2f5d1f5083f4eb6bac28782965cbdc6093df94c944d73523

Observation 09c17e92-e127-4165-84ba-155d2f2a5544 · outbound

This paper cites Acemath: Advancing frontier math reasoning with post-training and reward modeling.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Acemath: Advancing frontier math reasoning with post-training and reward modeling

Reference 37

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source=pdf_text observed=2026-08-07T01:05:26.844659Z digest=sha256:4c09570a8bd71b93a3e950cfed99b7c990d521da9da855d8774887c25f2e04e3

Observation 39536fc5-73e8-4b6e-b543-381f67e043d0 · outbound

This paper cites Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond

Reference 38

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source=pdf_text observed=2026-08-07T01:05:26.848970Z digest=sha256:5517944a2623901674f3f7510a3ec671ab0be530a05594965f32789f60b06abe

Observation 820e95ec-8c99-4f51-b824-3c4cf18a3887 · outbound

This paper cites Rm-r1: Reward modeling as reasoning.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Rm-r1: Reward modeling as reasoning

Reference 39

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source=pdf_text observed=2026-08-07T01:05:26.853173Z digest=sha256:b9a3ab22e5a3808f250ba3f5f46c17fa79653d314105fb0dc114460695d67e79

Observation 14060f12-843d-46ac-8552-ca3433eaa9cd · outbound

This paper cites Inference-time scaling for generalist reward modeling.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Inference-time scaling for generalist reward modeling

Reference 40

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source=pdf_text observed=2026-08-07T01:05:26.857641Z digest=sha256:62b86301dfdfb1407432da847fdaa87078be8d4c74f73e04faf3990834c2da14

Observation 8fb16760-65d5-4c57-80ab-5ce231c56edd · outbound

This paper cites Fin-r1: A large language model for financial reasoning through reinforcement learning.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Fin-r1: A large language model for financial reasoning through reinforcement learning

Reference 41

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source=pdf_text observed=2026-08-07T01:05:26.862034Z digest=sha256:7add6a7093536a739912547b469524c6e909591ffe979dbb22ea0b9f8c2146f9

Observation be0a263f-0e87-4cba-bb89-6a4f8f6b4eb4 · outbound

This paper cites Table-r1: Inference-time scaling for table reasoning.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Table-r1: Inference-time scaling for table reasoning

Reference 42

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T01:05:26.866195Z digest=sha256:1826da6b519c1b5ce198e6f2a97339ca44531b32d033f26e17e469a5ffab2e27

Observation dce29aa5-736f-4e8f-a5c2-2ec95479463e · outbound

This paper cites Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning

Reference 43

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source=pdf_text observed=2026-08-07T01:05:26.870338Z digest=sha256:39449fe96c0614e2013c38341079b6ff4d1db3e236fe2cb7885371f3f44387ec

Observation 08c701c3-d4ca-4fd8-ad3d-265151569a75 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task Proximal Policy Optimization Algorithms

Reference 44

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source=pdf_text observed=2026-08-07T01:05:26.874592Z digest=sha256:208d200e6d8500adad9cb80db705f89a96933c50637edee551b5dff7cd66dbf3

Observation 62a2bf42-29eb-4f4b-9ec8-67b8364147aa · outbound

This paper cites L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 45

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source=pdf_text observed=2026-08-07T01:05:26.879105Z digest=sha256:5ae97d01bb70d819f76b1776cb331e8383e7a24dac78de7f83db999d993e5f5f

Observation 5f411909-a76e-465b-b4ba-1edfa33aaf28 · outbound

This paper cites ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning.

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

Reference 46

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source=pdf_text observed=2026-08-07T01:05:26.883411Z digest=sha256:ce993bf4461e26fe82fda4d946f2b6a7d9e99613d54f39da5edeb647931f5053

Pith citing papers

No inbound Pith citation observations are available.