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Paper Citation Record · LEDGER

Graph World Model

As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.10539.

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

pith.paper-citation-record.v1
2507.10539 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:37:11.894861Z

measured 43 of 43 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

43 of 43 outbound references displayed

  • verified exact4
  • verified fuzzy8
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24546205-eb5b-4fe1-ad01-9bf27bf0d3ea · outbound

This paper cites LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks.

Graph World Model LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks

Reference 1

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Observation 688f52a7-f3f2-490a-869c-66fb6d915311 · outbound

This paper cites Relational Multi-Task Learning: Modeling Relations between Data and Tasks.

Graph World Model Relational Multi-Task Learning: Modeling Relations between Data and Tasks

Reference 4

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local_arxiv, observed 2026-08-06T17:37:13.069665Z

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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 97fb6b5a-3d9b-49de-8d73-f466e4e75735 · outbound

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

Graph World Model Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 9

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source=pdf_text observed=2026-08-06T17:37:11.668297Z digest=sha256:6c7c2d3e31fa0ce4c2cad1ecc197a892b9de640939ded26ded23689a3ca417d5

Observation 908ddbe7-7d19-47fa-a0f4-d52e28d4167c · outbound

This paper cites In Multi-Modal-Paper, there are 58565 text-nodes, 7380 figure-nodes, and 6792 table-nodes.

Graph World Model In Multi-Modal-Paper, there are 58565 text-nodes, 7380 figure-nodes, and 6792 table-nodes

Reference 10

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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.

source=pdf_text observed=2026-08-06T17:37:11.852810Z digest=sha256:f7cbf9a6371b8980e8f42345479172a63a43e800a08491e4267c31c6e3a8ddb1

Observation 1706e829-811c-43e3-9139-0747ea931f8b · outbound

This paper cites It includes three datasets of different scales, with the sizes ranging from small to large as follows: Baby, Sports, and Clothing.

Graph World Model It includes three datasets of different scales, with the sizes ranging from small to large as follows: Baby, Sports, and Clothing

Reference 11

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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.

source=pdf_text observed=2026-08-06T17:37:11.858565Z digest=sha256:1cf9a6203d729e362ce5bc13a0284abfb29cca81b61bd4c92567f384e7a2a7e5

Observation c3877b74-ee8c-46f4-9983-55cb273be5cd · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Graph World Model Semi-Supervised Classification with Graph Convolutional Networks

Reference 13

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source=pdf_text observed=2026-08-06T17:37:11.695006Z digest=sha256:5c83ffaa22c39da3a039b24359063e644a3e46304792061db5a95933cec74450

Observation 62b6e055-9e33-47bf-8034-19ae286ab472 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Graph World Model Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 14

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source=pdf_text observed=2026-08-06T17:37:11.700718Z digest=sha256:e18d68391f6f734532373479164ad0585131281f8b674c51e6761b61fc3d3d14

Observation 94aea020-4ed2-4a53-9dfa-c5c2a9b8e749 · outbound

This paper cites How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval.

Graph World Model How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 15

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source=pdf_text observed=2026-08-06T17:37:11.706811Z digest=sha256:a167fb913621bae395cf8657d04b2dba1764eff0c8dd9c9fa51e031d430c32aa

Observation c0db666d-0e3c-4450-8925-73551a9f76d9 · outbound

This paper cites One for All: Towards Training One Graph Model for All Classification Tasks.

Graph World Model One for All: Towards Training One Graph Model for All Classification Tasks

Reference 16

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source=pdf_text observed=2026-08-06T17:37:11.713264Z digest=sha256:fabb8b0c40c7b35585b80449bb63f712d0315637ba4b963f8ec3f15db65be6d7

Observation 245fa440-bd69-4ca9-942c-0ae28d6ac339 · outbound

This paper cites A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration.

Graph World Model A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration

Reference 17

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source=pdf_text observed=2026-08-06T17:37:11.721122Z digest=sha256:7e0cccd60a7d00f302743938b975aa953fb8a2818653479d1e7a86790057ade8

Observation 2c1e1f53-9e5d-4ef0-8ddc-ee3369e35cf5 · outbound

This paper cites Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer.

Graph World Model Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer

Reference 18

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local_arxiv, observed 2026-08-06T17:37:12.637315Z

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 68dd66cb-7e76-4ba2-b229-5d5980026926 · outbound

This paper cites A Content-Driven Micro-Video Recommendation Dataset at Scale.

Graph World Model A Content-Driven Micro-Video Recommendation Dataset at Scale

Reference 19

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source=pdf_text observed=2026-08-06T17:37:11.733457Z digest=sha256:e6565f42b38f1d3e01a55a98392e027a1c5d3f9fcb2ec6d3e519bc3568955484

Observation 035e07f8-7313-4126-ac27-9b6e6c286fbb · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Graph World Model DINOv2: Learning Robust Visual Features without Supervision

Reference 20

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source=pdf_text observed=2026-08-06T17:37:11.739439Z digest=sha256:90d08073d8d5d62abfd1e8913f192c53a2024cea13dbf914a7448a15e282235b

Observation aba5e883-504e-4c5f-83cd-1cc0dc987ea8 · outbound

This paper cites Instruction Tuning with GPT-4.

Graph World Model Instruction Tuning with GPT-4

Reference 21

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source=pdf_text observed=2026-08-06T17:37:11.746775Z digest=sha256:919a1186158639f77205132567eac8622d2a19047a10870d59b6d2bb3d4c86d5

Observation 24c5bbd3-d9d1-4334-81c4-d2232be765ac · outbound

This paper cites Graph Retrieval-Augmented Generation: A Survey.

Graph World Model Graph Retrieval-Augmented Generation: A Survey

Reference 22

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source=pdf_text observed=2026-08-06T17:37:11.753365Z digest=sha256:068970197903e5c5bb8c2ac3f916d277d7c94add94081ed0e362a8e9d28c8fd8

Observation be861abd-2bdd-478b-9ce8-9ba80f13e0c5 · outbound

This paper cites Modeling Relational Data with Graph Convolutional Networks.

Graph World Model Modeling Relational Data with Graph Convolutional Networks

Reference 23

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source=pdf_text observed=2026-08-06T17:37:11.759330Z digest=sha256:dca0de27ff86dd0c307e1dac44e2912a24a7ef549b3933cbbc77d506509febb0

Observation 0d5ba0fb-9e2e-4b32-bcfb-28265ff57f78 · outbound

This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.

Graph World Model ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 25

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source=pdf_text observed=2026-08-06T17:37:11.772925Z digest=sha256:287816b59dbb661bac4e6e3647d79355e1be1c4b6b4f947e79a11b6081ac426a

Observation cca24aec-ec33-4e8f-b612-ca4cd3f9131e · outbound

This paper cites How Crucial is Transformer in Decision Transformer?.

Graph World Model How Crucial is Transformer in Decision Transformer?

Reference 26

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local_arxiv, observed 2026-08-06T17:37:12.386360Z

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 992037fc-684e-4b1f-b4bd-0b3a160ba00e · outbound

This paper cites Graph Attention Networks.

Graph World Model Graph Attention Networks

Reference 28

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source=pdf_text observed=2026-08-06T17:37:11.790648Z digest=sha256:e803832edf497aa941d137b6ad56d4fb5927b2bf671f3124b2b9f9b3bd98b457

Observation 494922af-184b-4d15-831f-03addbff4a93 · outbound

This paper cites doi: 10.18653/v1/P19-1248.

Graph World Model doi: 10.18653/v1/P19-1248

Reference 29

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source=pdf_text observed=2026-08-06T17:37:11.795814Z digest=sha256:c378c5c859f822ed8fa844a8285ef63b89be5f3f187a7ba5b0308d396a20a5d9

Observation b2a5669e-4915-4dd4-bec9-05687ca1924a · outbound

This paper cites iVideoGPT: Interactive VideoGPTs are Scalable World Models.

Graph World Model iVideoGPT: Interactive VideoGPTs are Scalable World Models

Reference 30

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Observation 526d851c-7684-4dc9-b798-26d76c1142b9 · outbound

This paper cites AgentKit: Structured LLM Reasoning with Dynamic Graphs.

Graph World Model AgentKit: Structured LLM Reasoning with Dynamic Graphs

Reference 31

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source=pdf_text observed=2026-08-06T17:37:11.809555Z digest=sha256:b5b7a6e9ea4c3ce2db3feb989746bab4c4cd3f010b90e79ff649c6adfafb582c

Observation 3139b2dd-fb0a-43e0-9152-323169400ad8 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Graph World Model Adding conditional control to text-to-image diffusion models

Reference 32

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source=pdf_text observed=2026-08-06T17:37:11.815838Z digest=sha256:662f4d1b78283fb3160964225bb725c36c48c40f6125bc8692d81aba9f5d0544

Observation 90942ef8-c7ac-44ef-abf8-2284473b892c · outbound

This paper cites Retrieval-Augmented Generation for AI-Generated Content: A Survey.

Graph World Model Retrieval-Augmented Generation for AI-Generated Content: A Survey

Reference 33

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source=pdf_text observed=2026-08-06T17:37:11.822232Z digest=sha256:dcf8a4aaf918cc951fe946709fc42b3edc2d7df53ef1f85cebc1531bb2d6a47e

Observation a1e1728a-81ed-41d9-8b96-6455f44da34f · outbound

This paper cites Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning.

Graph World Model Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning

Reference 34

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local_arxiv, observed 2026-08-06T17:37:12.036492Z

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Observation cce25671-f081-433d-a384-3e46d69ddd93 · outbound

This paper cites Language Agents as Optimizable Graphs.

Graph World Model Language Agents as Optimizable Graphs

Reference 35

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Observation cec6b718-b6c9-4e4d-9164-209892be5473 · outbound

This paper cites an unresolved cited work.

Graph World Model Unresolved cited work

Reference 36

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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 2006e9f1-414f-4281-ae16-8c5d2e88b4d5 · outbound

This paper cites Molecular struc- tures and graph representations are generated from SMILES strings, with atoms (nodes) and bonds (edges) described using natural language.

Graph World Model Molecular struc- tures and graph representations are generated from SMILES strings, with atoms (nodes) and bonds (edges) described using natural language

Reference 39

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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 816f3f3b-5b88-4457-a774-be572b991cec · outbound

This paper cites an unresolved cited work.

Graph World Model Unresolved cited work

Reference 40

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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 631cb9be-f5e2-4abc-a49d-7f19e08ddf3b · outbound

This paper cites Here we finetune it on the dataset of this task.

Graph World Model Here we finetune it on the dataset of this task

Reference 42

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raw_fallback, observed 2026-08-06T17:37:13.214723Z

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=2026-08-06T17:37:11.887951Z digest=sha256:e1fce775f3113eb636fcece3dcdf241e82ed30b40fa45e39feecf6ca3ec54a69

Observation 3dd07953-8d63-47f1-9c3b-552427510452 · outbound

This paper cites This task is to generate a response that integrates information from the retrieved documents to accurately address the user’s query.

Graph World Model This task is to generate a response that integrates information from the retrieved documents to accurately address the user’s query

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:37:13.178377Z

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=2026-08-06T17:37:11.894861Z digest=sha256:2f6c88f71a76db67e7760a560d8b228af71183beb6b433962fb14d17a41c8844

Observation 31091688-b765-4438-8ca9-566af9075a94 · outbound

This paper cites Their details are as follows: • BM25: A widely-used ranking function in information sparse retrieval.

Graph World Model Their details are as follows: • BM25: A widely-used ranking function in information sparse retrieval

Reference 2009

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verified fuzzy
raw_fallback, observed 2026-08-06T17:37:13.250737Z

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=2026-08-06T17:37:11.880336Z digest=sha256:ed26fb77f08ab301c138c4d22f7bc411540003c9f3c4fe1c45b1db73621398b9

Observation 6f6ee02b-8e36-406f-85cf-045064d774f8 · outbound

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

Graph World Model From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 2014

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Observation 77e5204f-182a-4342-b8a1-b0c335c8ae20 · outbound

This paper cites Graph Convolutional Reinforcement Learning.

Graph World Model Graph Convolutional Reinforcement Learning

Reference 2015

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source=pdf_text observed=2026-08-06T17:37:11.682878Z digest=sha256:811bc680835545809cc22cb6768faaf7c5799704b12dc6d373b9f6f6d69056f3

Observation 60c5dc34-e1fd-4164-ac2d-5df5c4a96c18 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Graph World Model LoRA: Low-Rank Adaptation of Large Language Models

Reference 2016

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source=pdf_text observed=2026-08-06T17:37:11.676498Z digest=sha256:ba320fdab1472c9d7dafbdc83f70fcfe9f35a42043762c95815f5b85abc73bf7

Observation a49c74e6-ca89-413d-9f01-cd29ea155041 · outbound

This paper cites AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments.

Graph World Model AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments

Reference 2017

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:37:11.767207Z digest=sha256:1b05ad9c4ae306e325732b6845dc6136d434d1b7300264b2cf0c8079cdc0ec5b

Observation 422dcf4e-4c0b-4814-a78b-aaed8b9cf168 · outbound

This paper cites InstructG2I: Synthesizing Images from Multimodal Attributed Graphs.

Graph World Model InstructG2I: Synthesizing Images from Multimodal Attributed Graphs

Reference 2018

Resolution
verified exact
local_arxiv, observed 2026-08-06T17:37:12.817774Z

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=2026-08-06T17:37:11.689682Z digest=sha256:070cf866a77238434dc926f5467ac63c808bbacb87934c7f6b860805665c9e5a

Observation 9f63e06b-3963-4cda-b6a2-56c9ca6d6bf2 · outbound

This paper cites M., Hao, Y ., Stoeckius, M., Smibert, P., and Satija, R.

Graph World Model M., Hao, Y ., Stoeckius, M., Smibert, P., and Satija, R

Reference 2019

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verified fuzzy
raw_fallback, observed 2026-08-06T17:37:13.442519Z

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 0dd94efa-5324-4623-91ab-57dfdad3a3f4 · outbound

This paper cites Language Models are Few-Shot Learners.

Graph World Model Language Models are Few-Shot Learners

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-06T17:37:11.630570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:37:11.630570Z digest=sha256:6057dfbae2915ccbf3213c80198797b4b03e906b445e6f2da20e03f3d25a5846

Observation a202db81-c566-4a5d-9310-2d5d1dc4a940 · outbound

This paper cites LLaGA: Large Language and Graph Assistant.

Graph World Model LLaGA: Large Language and Graph Assistant

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-06T17:37:11.642954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f5f60979-9887-4db8-9cc5-c0ec06732100 · outbound

This paper cites Exploring the po- tential of large language models (llms) in learning on graphs.

Graph World Model Exploring the po- tential of large language models (llms) in learning on graphs

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:37:13.469858Z

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=2026-08-06T17:37:11.649811Z digest=sha256:09f7f85a0dfb9f0b0bac57b2b4d5e4c10e9ea673e4e113c4d5cddf02fe486fe6

Observation 66143597-0f3b-4c5d-b2a8-aa708f4801c3 · outbound

This paper cites Relational Deep Learning: Graph Representation Learning on Relational Databases.

Graph World Model Relational Deep Learning: Graph Representation Learning on Relational Databases

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-06T17:37:11.662125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:37:11.662125Z digest=sha256:af2aacbf83264d82d47d110a27c3a6393e0a7eddda689dbbed1a025b11067546

Observation 0aab8ac4-9d2b-4e76-bd58-ff83b0be4d64 · outbound

This paper cites Longformer: The Long-Document Transformer.

Graph World Model Longformer: The Long-Document Transformer

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T17:37:11.621975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.