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Paper Citation Record · LEDGER

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

As of 16 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 2026-08-16T06:30:59.297886+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
raw_fallback, observed 2026-08-07T01:05:27.960618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+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:e8aa329ca096e04f6c3e6c33f9544076797f9d0004e9e5a27e136e2e419cae94

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

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+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

Resolution
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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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.702158Z digest=sha256:97be4ad20f711d0679a3eda3470e16d0b8868b03f2304c2ed8566aa2df772467

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

Resolution
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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.707053Z digest=sha256:be2f6e35bdbd3a31c16053109125feb97e5788c6276310934921a7bb7981e031

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

Resolution
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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:bf5d98d8ec9a6e1b093ea1fcd8ade588f446e2cf157ba96abcc9e747047d604c

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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verified fuzzy
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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.717070Z digest=sha256:982a0e54adee6665cf191a92d991587c98b874572f3e0b401e4318f5b6403e02

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

Resolution
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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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.722126Z digest=sha256:27b65b23b8d322697b37429199696783494f275569d5fbf887032f587a17ed6e

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
verified fuzzy
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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.726094Z digest=sha256:becca551ad34a2eecb4db969f23fcd7575683d94d73324fca91de4d417f6cc18

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

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.730191Z digest=sha256:b9c6fbdf676ebd7ce1120aa3d9cbde25344cdb9d0800ce06a90c1060d033d0ec

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

Resolution
verified fuzzy
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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.734037Z digest=sha256:55dd7499ac93f064635c3b64ccbba19cfcade31e15225b77819c0756eb5ba058

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.738097Z digest=sha256:6cc32b8a5892c1909550c79fa203dcf086e4dc2ccde52b6757376a09f8702905

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
verified exact
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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.742211Z digest=sha256:ea5b7d4cf15e804ada49bb2fcb8c847780923ef63e761d278c7421b81a12c682

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:f08710cc567262640d68f2cc536e3a850b675ee2cb2df2785677427c24781dd2

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.751113Z digest=sha256:72a9ed6b3ca468e377b5aefd9952374631fb5c20df1fe109f252e2c439618bb6

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

Resolution
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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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.755047Z digest=sha256:636cbfda77998e2e515b181160f995b05ba9bb6e91a8a3f4bab49f58d1a0e883

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

Resolution
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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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.759049Z digest=sha256:3d2ac1b3e3eee82903376c69725a172e96e21cc1fdde14d6a019a49d80040b47

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.763038Z digest=sha256:291987c1aa5b60e4dd5ca29d8ad871b173873531a49fa98944089449ad3d28e3

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

Resolution
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.767255Z digest=sha256:5e72deb685f47cef3eefe14a8369111a098e75ee675c11360484b431caf33f6a

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.771606Z digest=sha256:9b371956438c88abebad804ea64e64f86a546349f0b1c6da473eefa41d20de49

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

Resolution
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no resolver link, observed 2026-08-07T01:05:26.776323Z

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source=pdf_text observed=2026-08-07T01:05:26.776323Z digest=sha256:2a79871b423efae6ce9853aee5463f4f7f8a7de9da00f6092f14e3e5b037b3bb

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.780551Z digest=sha256:da9ea844827f211e21f2743841d478a0dca167c96fc9537703af2687171acdb3

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
verified fuzzy
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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.784579Z digest=sha256:da76b2027e897fa93617a7aa573811d7066ac804d9b486c72d2bb93d514b5dc6

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

Resolution
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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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.788569Z digest=sha256:963967702d8e4f6983b469c3792283cbdc13cef2132ad834bfe4c0e4c912ec40

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.792639Z digest=sha256:1442d7edf79f48a086e2fe6f8e5757533341688d5c88d402c36e297b728bfc9d

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.796600Z digest=sha256:bdb3d829f02cd834d32e5a10b87fc15683932ae1abb4bcf3c595ba7ed4857932

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.801040Z digest=sha256:68bfb37d8541d8df5c193c52d77b20dd8b670043ef6900bb7cb0e6d80b14fefd

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:f59c9e69029772d45218a246fc82c32102fa1d57ce4070fec6a766c9ced45a4c

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.809526Z digest=sha256:dc211f2b25ea062c638771ab93a2ec3a166bdbdd388843d6eb5408ad0d58dbfe

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
verified fuzzy
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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.813685Z digest=sha256:f1e0ece2e5d057db3ed304a285117ddffd4eeb2cf10783e68ca28624a35108fe

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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no resolver link, observed 2026-08-07T01:05:26.818254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.818254Z digest=sha256:55f86adb92c1818a37fe3bc41265f0cc46ac40dd0a3100f35f1126a8fef29844

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:05:26.822573Z digest=sha256:68ce14e2cb439c62904b44cbe0e650cb9fd4c203a5ebb6feb9a906e16d78e4fb

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T01:05:26.826762Z digest=sha256:badac990ea6cd1653309a4d3887fa8fd3da8859362109041c388b4e7c1a26807

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:fa77e9fb09cda6966e7fd1cc4abb7a5c25c65e35877036eadc34a1645b5bdb5d

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=sha256:50115b702adcd078e12fc467c7976ef0e70c16cf3036cf3374ff127c8d9893ad

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=sha256:8e37980253e02898c70eaa475a695664b0f9cb06668eb8d540f62c90080e010d

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:d9dde40f08f57900c1a229e26001afa1d840f65feccd8c1ce279f5fcd620c7ca

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=sha256:7789378cad8ce0413f8dbd51f15c65728f80e7cac9841c0c90774b67da380e85

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=sha256:6d6aeac479841a6e4d67914c3b5d7dbe6d2846258b0040ad0ab556b638c73d8c

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=sha256:12e3d3636a2c18b5e97c1afbc52e83651ed6df7aad91c8c0eb428f92f7b9cf9b

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=sha256:448e01c293904b84e3c675e1c927bb9570b04caa3fdd795d2fcfb7d3d48b547d

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=pdf_text observed=2026-08-07T01:05:26.866195Z digest=sha256:e034d68b1a33a632d2571a55d7bf9c460af00bbc520a29245e918c21622362b5

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=sha256:27372a6d4c158e9ac90640e79fbfdedfd5be574519b4c8798074afb20361c9c4

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=sha256:71f47a996acd49ce12e90a04ebb10a458793392ea9a6e54436059921005587c9

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:ed4f0018cfc0f97eab3db2736541fe41a53af268dfb69df62ee1ff43785e590e

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=sha256:05041f8fc860826b8aac08b703a83162ab13d3a4ed014ece43e9b1678d1f0b80

Pith citing papers

No inbound Pith citation observations are available.