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Paper Citation Record · LEDGER

Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2304.11116.

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

pith.paper-citation-record.v1
2304.11116 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:16:33.127876Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T17:07:12.272339Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 007dfc09-0ba1-4e43-bba5-e4293bcb4ce8 · inbound

Retrieval-Augmented Generation for Large Language Models: A Survey cites this paper.

Retrieval-Augmented Generation for Large Language Models: A Survey Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

Reference 109

Resolution
verified exact
arxiv_id, observed 2026-05-24T05:13:56.711893Z

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=2026-05-24T05:10:25.171044Z digest=sha256:7cf410ff73fa8a665c5678530777bfa7dd9c905fd720c3135dd1c74912bfde0e

Observation e054c77d-c582-44c5-bff9-6d264f23e50a · inbound

A Survey on Knowledge Distillation of Large Language Models cites this paper.

A Survey on Knowledge Distillation of Large Language Models Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T23:31:11.644373Z

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=arxiv_source observed=2026-05-17T23:31:11.213552Z digest=sha256:7a90e4b6233dc63bc4abab99f57d47ba83f2a19f2dbf517fe2b524914802cbf4

Observation c255448a-212a-459c-926b-3ff0ac88b588 · inbound

From Local to Global: A Graph RAG Approach to Query-Focused Summarization cites this paper.

From Local to Global: A Graph RAG Approach to Query-Focused Summarization Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

Reference 75

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:10:58.106562Z

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=arxiv_source observed=2026-05-11T05:10:57.816312Z digest=sha256:20699c5952c8eb41dd2ce08f894c7daa8b5791ed6495d5f0013a476757602c88

Observation a71905a6-9c0f-4685-87fb-3d03f048be13 · inbound

GraphRunner: A Multi-Stage Framework for Efficient and Accurate Graph-Based Retrieval cites this paper.

GraphRunner: A Multi-Stage Framework for Efficient and Accurate Graph-Based Retrieval Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T18:16:33.127876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:16:33.127876Z digest=sha256:82b8c2caff2406f9ecacc753b173f4bf838488f5e6600d10507ae4013114ebe5

Observation 9b729be4-50b1-4e53-8c58-faedb5ddf492 · inbound

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering cites this paper.

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-05T22:55:20.898267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:55:20.898267Z digest=sha256:564f7699856afd7fe088cf3ab1f3161538ad83f5d643fcc84b3606141c62da08

Observation 7f91beac-a9f7-4a56-905b-c524931a3f01 · inbound

EGL-SCA: Structural Credit Assignment for Co-Evolving Instructions and Tools in Graph Reasoning Agents cites this paper.

EGL-SCA: Structural Credit Assignment for Co-Evolving Instructions and Tools in Graph Reasoning Agents Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:26:25.879336Z

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=2026-05-12T04:16:22.289519Z digest=sha256:05d1053e163463f8903ca7bbd27bfa5caccec5ea39dfa6a9d0f825ad96178b49

Observation 7aeb5341-3372-4248-a70b-8b71d7a411cc · inbound

GraphReview: Scientific Paper Evaluation via LLM-Based Graph Message Passing cites this paper.

GraphReview: Scientific Paper Evaluation via LLM-Based Graph Message Passing Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T18:33:50.761580Z

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=2026-06-29T18:27:16.024909Z digest=sha256:3bd69fef64ed759a616b91ae8fd70c7137b9680434115a0803390a9916610639

Observation 72836ff9-5891-45eb-b9bb-de6213a59f9a · inbound

Are Large Language Models Suitable for Graph Computation? Progress and Prospects cites this paper.

Are Large Language Models Suitable for Graph Computation? Progress and Prospects Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

Reference 260

Resolution
verified exact
arxiv_id, observed 2026-07-02T17:07:12.274234Z

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=arxiv_source observed=2026-06-27T22:15:03.223540Z digest=sha256:b5e8c53e976b7f1c8b756966681dd5979b228fd3dc599cb5a30e663cf70aab5a