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Paper Citation Record · LEDGER

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework

As of 12 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2412.12612.

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

pith.paper-citation-record.v1
2412.12612 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:59:58.270437Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T04:46:56.654470Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:36:45.352690Z

Reference resolution

37 of 37 outbound references displayed

  • verified exact0
  • verified fuzzy22
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 082145f8-8d0a-438b-96f1-fdf46bbc8f6e · outbound

This paper cites Select Nodes and Relationships: Based on the query type, choose nodes and relationships to form the questions.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Select Nodes and Relationships: Based on the query type, choose nodes and relationships to form the questions

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.913396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.041617Z digest=sha256:5d70f3025ceb8b00de0f27bd624717395148d8eb1e90fa9b7666214979ab1ef9

Observation 153be6b0-f282-41e0-ade9-67d98362e448 · outbound

This paper cites Structure-Grounded Pretraining for Text-to-SQL.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Structure-Grounded Pretraining for Text-to-SQL

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T13:59:58.027688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:59:58.027688Z digest=sha256:8513dd25a51721ef538ef99f782052839d8f9d0e3684eca6bf722ec81f8d88fc

Observation 661918ca-406e-4cce-84a6-2b41f2dcd00b · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.877844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.054919Z digest=sha256:01e82e965566362fdd7183b3ef8ea6cc05a870ee594a19211988614598485d2f

Observation 1bdb5974-99e9-4685-92aa-90d700ea37e8 · outbound

This paper cites Random Selection: Randomly select nodes or relationships when forming each question, ensuring diversity in the coverage.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Random Selection: Randomly select nodes or relationships when forming each question, ensuring diversity in the coverage

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.862887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.067026Z digest=sha256:3112688272a6bb1ea11821029a6a495bb8c8c8387eb5cc18fffc83c34d6db53c

Observation f6444f36-1829-425e-8137-e9ab67bac9fc · outbound

This paper cites Ensure no two questions are similar.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Ensure no two questions are similar

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.894444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.048809Z digest=sha256:78749e911d9952339cb50fe69507e3fe199ac2a63861910e31045a80b4a2f8ca

Observation 85606155-d1c8-45de-bfe7-50ee9cc0d85c · outbound

This paper cites 1 million.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework 1 million

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.830071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.085141Z digest=sha256:fcc12c34910897e76e1cc4b849fa7279b686901aec3815fbce274d00e93d50f8

Observation c60e22b4-f2ce-4f3f-83eb-deb79df401b8 · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.713505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.142262Z digest=sha256:18cfd88bf65a6204aaa1473fdfc38c31a5cae39a954bd06c79d6a1dcecad1233

Observation 4873c0d9-9b81-4b82-9089-3c5505cfd9f4 · outbound

This paper cites 2024-01-01.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework 2024-01-01

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.845851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.076272Z digest=sha256:c22b776bd86500e0fa60de8612e7efd5ed5109af332d5cfe4c3b8a9fd454767d

Observation 38466d9e-a96b-4e1f-81d1-00061ca5cc5b · outbound

This paper cites For example, if the question mentions 1 million, use 1000000; for 1.2 million, use 1200000.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework For example, if the question mentions 1 million, use 1000000; for 1.2 million, use 1200000

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.680644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.152765Z digest=sha256:e761c966e8974f999cdccc0ebf7c46bef41400df43cec3309bbb48eff8cce0c5

Observation af9154d4-5d3a-40a8-a950-acfa864653ba · outbound

This paper cites Understand which entities are crucial to construct the ground truth answer.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Understand which entities are crucial to construct the ground truth answer

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.814689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.093022Z digest=sha256:1d010a45bdf82e8fc1aa279329852476990465378384febdae57503ed293ad88

Observation 984d1f7d-6322-47e4-b9e6-de28a6662fd0 · outbound

This paper cites Include both the ground truth data and additional negative data points.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Include both the ground truth data and additional negative data points

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.799163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.099420Z digest=sha256:3d7dd435ecf91ae310dd972d41764969fa1c60d900c48bd16d1dced615714a5f

Observation 7e6a8c9a-cda7-405f-ba3a-1fb7cd180123 · outbound

This paper cites - Creating negative data points that do not match the answer but help ensure the test is comprehensive.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework - Creating negative data points that do not match the answer but help ensure the test is comprehensive

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.781601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.111204Z digest=sha256:6ec09c74b6aa12ed63ac364b979fe75c83ed8df05526faa5ddfee7f43c789eaa

Observation a9bad089-b221-430d-8e16-db00a1f5db77 · outbound

This paper cites Include details like names, summaries, and other fields, making sure the negative data does not overlap with the ground truth.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Include details like names, summaries, and other fields, making sure the negative data does not overlap with the ground truth

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.761914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.119290Z digest=sha256:d675cc71ec8717aed7373be7dcb4ddd54dc69afe8dd851292602887d67bd654a

Observation 4ffbb228-3b65-4408-ba54-9111b0c7096c · outbound

This paper cites This ensures that the negative data is limited and doesn 't overwhelm the test case.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework This ensures that the negative data is limited and doesn 't overwhelm the test case

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.745387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.126012Z digest=sha256:f4a3aafcfbe252d07aad31eba3c5f818c01d00f5ca9d07431d053e7fe629e9b9

Observation 4d277cb1-7869-4b20-b7c5-05b3fb9eaffb · outbound

This paper cites Specify which fields require unique values, using UUIDs or similar approaches.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Specify which fields require unique values, using UUIDs or similar approaches

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.729524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.135251Z digest=sha256:01dd65453821babd7a61b3a176fc374840a620009fba9e8780ca94ef3a2b9cd4

Observation 8c2ab9d3-ee91-42ac-9950-c5cac8528266 · outbound

This paper cites Use the `MATCH` statement before creating relationships to ensure that the nodes exist and the correct connections are established.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Use the `MATCH` statement before creating relationships to ensure that the nodes exist and the correct connections are established

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.698096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.147710Z digest=sha256:f8acf291e572dc9115b81df1f5d3bb65e9358ea0f020ff0985085ddab71f431a

Observation 3d80c064-f140-45d5-9088-52db7536a2b8 · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.661412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.158793Z digest=sha256:5f02795b114a5df4cb773a7e96424fcc36c03055400ba2fcdfadab0b43bd3a92

Observation a0abc45b-8a2b-46dd-b17c-138c617752aa · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.644717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.164168Z digest=sha256:d32fc5b34a4030cc31a6a4aaf7237a4de019e5585ee984d032563a8057713d74

Observation 017f2147-eae1-42f8-968d-56aafe75931e · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.628839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.169928Z digest=sha256:b77490c87f027f56e588b8e37e27150d56795700cf79f3fcb51b186d8b6f4595

Observation b54578fa-ef18-4708-b65a-bf341ca5d793 · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 22

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.612334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.174898Z digest=sha256:871efa6570b2c1582ebbf970a7ee9d4cd3fd59a4965cf3d0864c487cf9473061

Observation 80a97cc9-5766-4c6b-97a1-ece25b8a988e · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 23

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.596049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.183691Z digest=sha256:004e4a17c07744c4919e7498beb5247f10b2294e278fb7e4b938d77bc17d882d

Observation 84eb6335-7459-41e4-9897-a2afe6b2c4ab · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.579606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.197602Z digest=sha256:d4c2bcbcf937ab732598eb0f6ef43f04d86ddd87f19c16ed598daedf497b0005

Observation a6beb4b2-27ec-445a-8077-f822d8b7e922 · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.563825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.204026Z digest=sha256:8fa264cebcbe8e2f32063466f2bb54428d9704c618a8886b3f9cfe8b4da040da

Observation 84502a4f-5f3a-4c1b-98d8-ee116f9aaf45 · outbound

This paper cites Ensure negative data points are not more than five.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Ensure negative data points are not more than five

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.547757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.212166Z digest=sha256:764c4b3761b854089863a7d713a5f02152885133c17abe746cf84e964c1746f9

Observation a9c62217-9de7-4407-9100-38a874a6e95d · outbound

This paper cites Code Writing Suggestions: - Avoid errors with f-strings by using string concatenation or `.format()` when needed.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Code Writing Suggestions: - Avoid errors with f-strings by using string concatenation or `.format()` when needed

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.529900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.217400Z digest=sha256:e7d81aa23680a70f5fdf805193d50cbd45a96ed55cf843e37018a6dc0cedf145

Observation ad03d503-c7bf-4d88-9046-ff561f76e902 · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 28

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.514014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.223264Z digest=sha256:c84c5eee94b5eb25d84fa6e88180fd1e0ce02b83f4c7f15144490358faa6ab08

Observation 23f57384-3e24-47cb-bd85-fcae036ee916 · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.498386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.228716Z digest=sha256:57b8dc17ed145740c5c5866fc8a17319583f77746e44f574998e82fd31bd0b5a

Observation 1ecf91b8-4d64-407b-8bcf-662ebed22397 · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.478740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.233558Z digest=sha256:5087de78780276df9982f57901d8b91a6edbe10f0b67834ac5ce6f49aea2d3af

Observation c1e84d26-4c46-44af-9f50-c714ea3dfd65 · outbound

This paper cites an unresolved cited work.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Unresolved cited work

Reference 31

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:59:58.462186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.238330Z digest=sha256:372ea775d2c973837b9b99e6c257520201680a8a4c20f7febe05ed82f9a5bda4

Observation abc1ab82-cf7c-45fa-8e0e-d225eca8465e · outbound

This paper cites - Understand what the user needs, keeping in mind the eventual answer.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework - Understand what the user needs, keeping in mind the eventual answer

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.443888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.243775Z digest=sha256:1fd656d1c50aa0a1bb2766484d42b6ca6d1cae4189df5e04550873b1f0b2f9d2

Observation 7cbf7551-5ea5-4a29-90a1-7b1cd090c9d2 · outbound

This paper cites - Ensure that any indexes and constraints are considered when formulating your response.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework - Ensure that any indexes and constraints are considered when formulating your response

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.424342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.248893Z digest=sha256:26e32b8756eb6141c45547aefea8b490148bc074e7bebb26eef19b58790d1f67

Observation fc567c6d-5128-407b-ba1e-cfece5b70791 · outbound

This paper cites Keep track of these nodes.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Keep track of these nodes

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.402982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.253885Z digest=sha256:a53413163ddc1a64dda7a6ff80afff375162d89660789b018788805de32f8d00

Observation 04178fc5-3f44-42a4-8257-dd8443addfa6 · outbound

This paper cites Do not create imaginary relationships; only consider the relationships that are present in the schema.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Do not create imaginary relationships; only consider the relationships that are present in the schema

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.386371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.259460Z digest=sha256:87d71c4311df9b8e1e9ba48206bb3831ba26923b4f02449752b575747935cebc

Observation 74cafca9-5b8b-4bd9-89ac-1060e13fc150 · outbound

This paper cites Do not create imaginary properties; only consider the properties that are present in the schema.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Do not create imaginary properties; only consider the properties that are present in the schema

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.370382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.264659Z digest=sha256:c906a1469b7d89e3e1076cf2b3f16eddb145db367e8f2ab33584358d6652f6fe

Observation a0ce65af-94db-47a8-acb1-c49a997e11f7 · outbound

This paper cites Cypher generation plan.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework Cypher generation plan

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.352621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.270437Z digest=sha256:1df7c51ab7cf20d630d5e253e6562fff7bec69e2408b37a6358487fbcb0e889c

Observation ffdb34b6-a7be-4f8c-bd3a-707dd6ee7f37 · outbound

This paper cites A Survey on Employing Large Language Models for Text-to-SQL Tasks.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework A Survey on Employing Large Language Models for Text-to-SQL Tasks

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-11T13:59:58.034914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:59:58.034914Z digest=sha256:34990eaf0614c04d7b2c7ef671346c0951e8e755d45f04ec7ae8010470641c11

Observation 2ac35a1b-f433-4722-b0b5-ef2845c771e6 · outbound

This paper cites https://huggingface.co/datasets/ tomasonjo/text2cypher-gpt4o-clean?row=0.

Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework https://huggingface.co/datasets/ tomasonjo/text2cypher-gpt4o-clean?row=0

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:59:58.931149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:59:58.020326Z digest=sha256:f707ac86cf7718f1d73f68a5d5ba97e987caabfbaf98a6f1cb07081ce0379c43

Pith citing papers

Observation a123a844-39d9-43aa-aec0-1ff43c107562 · inbound

CYGNET: Cypher Gate for Neural Execution Triage and Cost Containment cites this paper.

CYGNET: Cypher Gate for Neural Execution Triage and Cost Containment Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:36:45.354515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-06-28T06:49:23.432383Z digest=sha256:00320d33f6760eb7b142dcd8a33a06e22ad7e65773c23a8f48439a04d27fe7bd

Observation 9bb09f7c-1d18-4ac3-b690-f5ba416c8a6b · inbound

KG2Cypher: Data-Centric Pipeline for Building Enterprise Text-to-Cypher Systems cites this paper.

KG2Cypher: Data-Centric Pipeline for Building Enterprise Text-to-Cypher Systems Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T19:13:53.471761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-06-29T04:46:56.654470Z digest=sha256:f1472f3d4d2ef1d70f930a7f3ad6ad225cf37b983a1752b36a801a499f648214