MAPF-World: Action World Model for Multi-Agent Path Finding | Paper Citation Record

Observation e73894f8-676c-49ce-831a-03466f719c19 · outbound

This paper cites Generating human motion from textual descriptions with discrete representations.

MAPF-World: Action World Model for Multi-Agent Path Finding Generating human motion from textual descriptions with discrete representations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:27.040304Z

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Observation 6c542fca-d31d-45f9-a0ce-fc9024bc2d58 · outbound

This paper cites Motiondiffuse: Text-driven human motion generation with diffusion model.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(6):4115–4128, 2024.

MAPF-World: Action World Model for Multi-Agent Path Finding Motiondiffuse: Text-driven human motion generation with diffusion model.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(6):4115–4128, 2024

Reference 2

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Observation 24fd7db9-405c-4516-ad52-802037930b20 · outbound

This paper cites Motiongpt: Finetuned llms are general-purpose motion generators.

MAPF-World: Action World Model for Multi-Agent Path Finding Motiongpt: Finetuned llms are general-purpose motion generators

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.936597Z

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Observation 6c1f36c7-215e-4921-bff3-4fc072a80819 · outbound

This paper cites Motiongpt: Human motion as a foreign language.Proceedings of the NeurIPS, pages 20067–20079, 2023.

MAPF-World: Action World Model for Multi-Agent Path Finding Motiongpt: Human motion as a foreign language.Proceedings of the NeurIPS, pages 20067–20079, 2023

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.917587Z

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Observation fd08967b-c4a1-4ad3-b978-cedc6f54d976 · outbound

This paper cites Generating diverse and natural 3d human motions from text.

MAPF-World: Action World Model for Multi-Agent Path Finding Generating diverse and natural 3d human motions from text

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.811120Z

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Observation b9564780-1891-4cf5-8932-c16f77298378 · outbound

This paper cites Remodiffuse: Retrieval-augmented motion diffusion model.

MAPF-World: Action World Model for Multi-Agent Path Finding Remodiffuse: Retrieval-augmented motion diffusion model

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.784349Z

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Observation 1f5a2451-a74b-499a-92cd-4868cb613e91 · outbound

This paper cites ViMo: Generating Motions from Casual Videos.

MAPF-World: Action World Model for Multi-Agent Path Finding ViMo: Generating Motions from Casual Videos

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:23.558758Z

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Observation 54fd318f-306c-4c6f-89ac-5734561a5809 · outbound

This paper cites Neural discrete representation learning.Proceedings of the NeurIPS, 30, 2017.

MAPF-World: Action World Model for Multi-Agent Path Finding Neural discrete representation learning.Proceedings of the NeurIPS, 30, 2017

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.760058Z

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Observation 996d6552-8567-4707-bbcd-8c5a1f2ae048 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

MAPF-World: Action World Model for Multi-Agent Path Finding Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:23.609661Z

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Observation 1c1396f6-886b-4c45-ba4c-0377742957d2 · outbound

This paper cites VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding.

MAPF-World: Action World Model for Multi-Agent Path Finding VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:23.663659Z

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Observation bbdf5349-0aee-44ec-88f8-383caf4dd076 · outbound

This paper cites Motion-x: a large-scale 3d expressive whole-body human motion dataset.

MAPF-World: Action World Model for Multi-Agent Path Finding Motion-x: a large-scale 3d expressive whole-body human motion dataset

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.643138Z

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Observation 414d2ce1-abdd-4971-8afe-abda4915c611 · outbound

This paper cites Human motion generation: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(4):2430–2449, 2024.

MAPF-World: Action World Model for Multi-Agent Path Finding Human motion generation: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(4):2430–2449, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.549824Z

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Observation 8f5399af-d827-4827-b365-9b4a62ac675f · outbound

This paper cites Momask: Generative masked modeling of 3d human motions.

MAPF-World: Action World Model for Multi-Agent Path Finding Momask: Generative masked modeling of 3d human motions

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.537346Z

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Observation f2a3b06a-cd40-4047-8eef-937921134cc2 · outbound

This paper cites Omg: Towards open-vocabulary motion generation via mixture of controllers.

MAPF-World: Action World Model for Multi-Agent Path Finding Omg: Towards open-vocabulary motion generation via mixture of controllers

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.411433Z

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Observation d5edbc3a-64cf-40dd-b870-29112b64cb00 · outbound

This paper cites Motionclip: Exposing human motion generation to clip space.

MAPF-World: Action World Model for Multi-Agent Path Finding Motionclip: Exposing human motion generation to clip space

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.328861Z

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Observation ee635c18-cf9d-434b-8c33-10e6b26f17b3 · outbound

This paper cites Plan, posture and go: Towards open-vocabulary text-to-motion generation.

MAPF-World: Action World Model for Multi-Agent Path Finding Plan, posture and go: Towards open-vocabulary text-to-motion generation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.316326Z

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Observation 23349fb3-582e-428f-b3cc-8f3584479b56 · outbound

This paper cites Textual Decomposition Then Sub-motion-space Scattering for Open-Vocabulary Motion Generation.

MAPF-World: Action World Model for Multi-Agent Path Finding Textual Decomposition Then Sub-motion-space Scattering for Open-Vocabulary Motion Generation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:23.690889Z

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Observation c69a274c-6c53-40b6-baec-b8e5a524ddbb · outbound

This paper cites Being comes from not-being: Open-vocabulary text-to-motion generation with wordless training.

MAPF-World: Action World Model for Multi-Agent Path Finding Being comes from not-being: Open-vocabulary text-to-motion generation with wordless training

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.251254Z

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Observation 49d14313-9ed9-422a-a39e-db5d4a30e0e5 · outbound

This paper cites Attention is all you need.

MAPF-World: Action World Model for Multi-Agent Path Finding Attention is all you need

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.094024Z

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Observation 55d907ca-42ce-42ec-82d8-79d430b24fbe · outbound

This paper cites Human motion diffusion model.

MAPF-World: Action World Model for Multi-Agent Path Finding Human motion diffusion model

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.082171Z

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Observation a1d2a04f-09ad-41bd-aa98-8bac47a14694 · outbound

This paper cites Denoising diffusion probabilistic models.Proceedings of the NeurIPS, pages 6840–6851, 2020.

MAPF-World: Action World Model for Multi-Agent Path Finding Denoising diffusion probabilistic models.Proceedings of the NeurIPS, pages 6840–6851, 2020

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.067027Z

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Observation bbcf4c41-00db-41a5-ac23-475d98354c28 · outbound

This paper cites Executing your commands via motion diffusion in latent space.

MAPF-World: Action World Model for Multi-Agent Path Finding Executing your commands via motion diffusion in latent space

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:26.003855Z

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Observation 87b93e2b-94e3-429a-a673-34228f1edb2c · outbound

This paper cites Openagi: When llm meets domain experts.

MAPF-World: Action World Model for Multi-Agent Path Finding Openagi: When llm meets domain experts

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.979537Z

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Observation 477a4049-0a54-4ecb-a95c-b4f2ffc42b0d · outbound

This paper cites MotionLLM: Understanding Human Behaviors from Human Motions and Videos.

MAPF-World: Action World Model for Multi-Agent Path Finding MotionLLM: Understanding Human Behaviors from Human Motions and Videos

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:23.718787Z

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Observation 24438d48-8da1-444d-a31e-3f31b50e504f · outbound

This paper cites MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding.

MAPF-World: Action World Model for Multi-Agent Path Finding MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:23.735914Z

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Observation b4e47378-3adc-4f0d-8b8b-b98d49ed34d4 · outbound

This paper cites MotionBank: A Large-scale Video Motion Benchmark with Disentangled Rule-based Annotations.

MAPF-World: Action World Model for Multi-Agent Path Finding MotionBank: A Large-scale Video Motion Benchmark with Disentangled Rule-based Annotations

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:23.740893Z

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Observation 58ae76ab-d18c-4e7a-99b7-d6760a7bf46c · outbound

This paper cites Large motion model for unified multi-modal motion generation.

MAPF-World: Action World Model for Multi-Agent Path Finding Large motion model for unified multi-modal motion generation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.853897Z

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Observation 56ccab24-08ba-4035-9b6c-9bb2b8c91f12 · outbound

This paper cites The action similarity labeling challenge.IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):615–621, 2011.

MAPF-World: Action World Model for Multi-Agent Path Finding The action similarity labeling challenge.IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):615–621, 2011

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.840358Z

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Observation 3bf0070f-5787-41b6-b8c8-e553195aadeb · outbound

This paper cites Hmdb: a large video database for human motion recognition.

MAPF-World: Action World Model for Multi-Agent Path Finding Hmdb: a large video database for human motion recognition

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.773488Z

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Observation ea1e783b-f946-4c58-b002-ff0eb4ea13f5 · outbound

This paper cites The Kinetics Human Action Video Dataset.

MAPF-World: Action World Model for Multi-Agent Path Finding The Kinetics Human Action Video Dataset

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:23.886937Z

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Observation e3f2fec4-6502-49b6-b2b0-7fa62ef09818 · outbound

This paper cites From actemes to action: A strongly-supervised representa- tion for detailed action understanding.

MAPF-World: Action World Model for Multi-Agent Path Finding From actemes to action: A strongly-supervised representa- tion for detailed action understanding

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.683892Z

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Observation d7de81a6-7e0a-4086-9960-f9b733e1fbcb · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

MAPF-World: Action World Model for Multi-Agent Path Finding UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.003224Z

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Observation 934a2ca7-2b78-43d5-a63f-28820b649266 · outbound

This paper cites Ntu rgb+ d: A large scale dataset for 3d human activity analysis.

MAPF-World: Action World Model for Multi-Agent Path Finding Ntu rgb+ d: A large scale dataset for 3d human activity analysis

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.617224Z

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Observation 1659a3f3-b1fe-4d51-b5ea-8d695c08fc17 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

MAPF-World: Action World Model for Multi-Agent Path Finding Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.012456Z

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Observation c4ad3f4d-1bc9-4903-8217-e8c010c4c8f0 · outbound

This paper cites HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding.

MAPF-World: Action World Model for Multi-Agent Path Finding HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.016809Z

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Observation 7dc1aeb1-7874-4149-99ac-093d99c6639f · outbound

This paper cites Alphapose: Whole-body regional multi-person pose estimation and tracking in real-time.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7157–7173, 2022.

MAPF-World: Action World Model for Multi-Agent Path Finding Alphapose: Whole-body regional multi-person pose estimation and tracking in real-time.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7157–7173, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.554751Z

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Observation 3659b86a-bd31-47aa-8eae-12faa3492dbc · outbound

This paper cites Learning transferable visual models from natural language supervision.

MAPF-World: Action World Model for Multi-Agent Path Finding Learning transferable visual models from natural language supervision

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.538037Z

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Observation b60d3890-a815-47ae-8a63-893c2b4fbf65 · outbound

This paper cites Motionbert: A unified perspective on learning human motion representations.

MAPF-World: Action World Model for Multi-Agent Path Finding Motionbert: A unified perspective on learning human motion representations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.384774Z

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Observation 265c0833-da7f-4141-a283-83f08945555d · outbound

This paper cites InternVideo: General Video Foundation Models via Generative and Discriminative Learning.

MAPF-World: Action World Model for Multi-Agent Path Finding InternVideo: General Video Foundation Models via Generative and Discriminative Learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.032141Z

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Observation 5ade095b-5019-4343-b5c3-5404701bda3d · outbound

This paper cites Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning.Neurocomputing, 508:293–304, 2022.

MAPF-World: Action World Model for Multi-Agent Path Finding Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning.Neurocomputing, 508:293–304, 2022

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.365888Z

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Observation b26bc8a2-39e7-4df3-ae92-73527e977d62 · outbound

This paper cites Exploring text-to-motion generation with human preference.

MAPF-World: Action World Model for Multi-Agent Path Finding Exploring text-to-motion generation with human preference

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.254216Z

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Observation bdd12df4-7fc6-408f-9a15-519302bb92ef · outbound

This paper cites MoDiPO: text-to-motion alignment via AI-feedback-driven Direct Preference Optimization.

MAPF-World: Action World Model for Multi-Agent Path Finding MoDiPO: text-to-motion alignment via AI-feedback-driven Direct Preference Optimization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.214742Z

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Observation a74b9e68-1b52-4ce4-a666-01b84e245f89 · outbound

This paper cites Direct preference optimization: your language model is secretly a reward model.

MAPF-World: Action World Model for Multi-Agent Path Finding Direct preference optimization: your language model is secretly a reward model

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.130777Z

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Observation b0ab2962-a2a3-4969-8be5-802d978872db · outbound

This paper cites Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward.

MAPF-World: Action World Model for Multi-Agent Path Finding Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.223101Z

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Observation 5470d96f-b13d-4e6a-b625-5f7f9d113cb4 · outbound

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

MAPF-World: Action World Model for Multi-Agent Path Finding LoRA: Low-Rank Adaptation of Large Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.227243Z

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Observation 1c6a8a48-bcac-4cf0-b983-30e84de13cec · outbound

This paper cites MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm.

MAPF-World: Action World Model for Multi-Agent Path Finding MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.231311Z

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Observation e5d7f021-0a42-4754-a3f2-ac2c6f301f04 · outbound

This paper cites Motionlcm: Real- time controllable motion generation via latent consistency model.

MAPF-World: Action World Model for Multi-Agent Path Finding Motionlcm: Real- time controllable motion generation via latent consistency model

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.111760Z

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Observation 59bd8c89-6e90-48ee-8202-822caee55047 · outbound

This paper cites Motionclr: Motion generation and training-free editing via understanding attention mechanisms.arXiv e-prints, pages arXiv–2410, 2024.

MAPF-World: Action World Model for Multi-Agent Path Finding Motionclr: Motion generation and training-free editing via understanding attention mechanisms.arXiv e-prints, pages arXiv–2410, 2024

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.096886Z

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Observation 81ab0483-9bb2-4539-b943-710844e0a058 · outbound

This paper cites Bipo: Bidirec- tional partial occlusion network for text-to-motion synthesis.arXiv preprint arXiv:2412.00112, 2024.

MAPF-World: Action World Model for Multi-Agent Path Finding Bipo: Bidirec- tional partial occlusion network for text-to-motion synthesis.arXiv preprint arXiv:2412.00112, 2024

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.243102Z

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Observation a7bc765b-ad41-4b88-b56c-914ef18f3ac4 · outbound

This paper cites Stablemofusion: Towards robust and efficient diffusion-based motion generation framework.

MAPF-World: Action World Model for Multi-Agent Path Finding Stablemofusion: Towards robust and efficient diffusion-based motion generation framework

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:25.026577Z

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Observation f8d7b1ee-20c1-4b11-bf77-e2f9279fba9e · outbound

This paper cites Mogents: Motion generation based on spatial-temporal joint modeling.Proceedings of the NeurIPS, pages 130739–130763, 2024.

MAPF-World: Action World Model for Multi-Agent Path Finding Mogents: Motion generation based on spatial-temporal joint modeling.Proceedings of the NeurIPS, pages 130739–130763, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:24.903676Z

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Observation ef8c3217-492c-4bf0-9fcb-4eaf6c335cf7 · outbound

This paper cites LaMP: Language-motion pretraining for motion generation, retrieval, and captioning.

MAPF-World: Action World Model for Multi-Agent Path Finding LaMP: Language-motion pretraining for motion generation, retrieval, and captioning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:24.890477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6611ea53-ce71-430d-baa1-5e0072ea51be · outbound

This paper cites Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs.

MAPF-World: Action World Model for Multi-Agent Path Finding Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.257359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8fb658b-02e2-47de-96fd-cc03d6709936 · outbound

This paper cites Scaling Large Motion Models with Million-Level Human Motions.

MAPF-World: Action World Model for Multi-Agent Path Finding Scaling Large Motion Models with Million-Level Human Motions

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.292408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2127f474-9eea-4589-8e8e-7887d920db70 · outbound

This paper cites ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model.

MAPF-World: Action World Model for Multi-Agent Path Finding ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.345896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 016a70af-37cd-42d0-8171-45c5c730fd49 · outbound

This paper cites Avatargpt: All-in-one framework for motion understanding planning generation and beyond.

MAPF-World: Action World Model for Multi-Agent Path Finding Avatargpt: All-in-one framework for motion understanding planning generation and beyond

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:24.877661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4595dd2c-da89-4cde-a573-2497056c73bf · outbound

This paper cites Internvideo2: Scaling foundation models for multimodal video understanding.

MAPF-World: Action World Model for Multi-Agent Path Finding Internvideo2: Scaling foundation models for multimodal video understanding

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:28:24.865532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bdda8fd7-6c08-4ffa-a704-bd04fb79b14b · outbound

This paper cites Gaussian Error Linear Units (GELUs).

MAPF-World: Action World Model for Multi-Agent Path Finding Gaussian Error Linear Units (GELUs)

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T17:28:24.478104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers