Paper Citation Record · LEDGER

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion

As of 11 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2501.13347.

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

pith.paper-citation-record.v1
2501.13347 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:18:39.062399Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T13:17:33.815644Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T13:21:24.323476Z

Reference resolution

47 of 47 outbound references displayed

verified exact2
verified fuzzy30
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa42db31-2fa7-478c-8e40-94c7bcda2ec5 · outbound

This paper cites Generative ai empowered network digital twins: Architecture, technologies, and applications,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Generative ai empowered network digital twins: Architecture, technologies, and applications,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.527221Z

Source-reported events for the cited work

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Observation b7e1abd9-5290-4bbd-a4a5-b0afb5930ce7 · outbound

This paper cites Public transport planning: When transit network connectivity meets commuting demand,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Public transport planning: When transit network connectivity meets commuting demand,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.512336Z

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Observation eddef529-29da-4701-9f64-afd80de44e95 · outbound

This paper cites Enhancing human mobility research with open and standardized datasets,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Enhancing human mobility research with open and standardized datasets,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.497732Z

Source-reported events for the cited work

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Observation 9779e2a7-6c72-477f-8514-7901ef1a6d6f · outbound

This paper cites Deepmove: Predicting human mobility with attentional recurrent networks,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Deepmove: Predicting human mobility with attentional recurrent networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.482640Z

Source-reported events for the cited work

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Observation fa776d15-0be4-4cfc-b59f-6c010dc5eacd · outbound

This paper cites A Universal Model for Human Mobility Prediction.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion A Universal Model for Human Mobility Prediction

Reference 5

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Source-reported events for the cited work

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Observation eb88abf2-5ca3-478c-9041-e87ac3a8c17a · outbound

This paper cites The timegeo modeling framework for urban mobility without travel surveys,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion The timegeo modeling framework for urban mobility without travel surveys,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.467200Z

Source-reported events for the cited work

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Observation b2489b98-aca2-4d4a-86e6-6dd027d4fa9e · outbound

This paper cites Generating mobility trajectories with retained data utility,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Generating mobility trajectories with retained data utility,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.452400Z

Source-reported events for the cited work

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Observation 396be970-8157-4e5f-8e8c-2676a968817d · outbound

This paper cites Network-less trajectory imputation,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Network-less trajectory imputation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.437501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 182ab360-11ad-41a0-bb72-f12338fc9eb9 · outbound

This paper cites Attnmove: History enhanced trajectory recovery via attentional network,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Attnmove: History enhanced trajectory recovery via attentional network,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.421931Z

Source-reported events for the cited work

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Observation ea983b16-36a2-445e-94be-77085f7e2e9e · outbound

This paper cites Representation learning and graph convolutional networks for short-term vehicle trajectory prediction,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Representation learning and graph convolutional networks for short-term vehicle trajectory prediction,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.318398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9f9c7711-f26f-4843-8d5e-5faed6fc7ffb · outbound

This paper cites Vehicle trajectory prediction and generation using lstm models and gans,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Vehicle trajectory prediction and generation using lstm models and gans,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.226283Z

Source-reported events for the cited work

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Observation 2e43eb42-b6d5-4fa9-90de-7b4fe6833890 · outbound

This paper cites Advancements in federated learning: Models, methods, and privacy,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Advancements in federated learning: Models, methods, and privacy,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.144847Z

Source-reported events for the cited work

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Observation b29e8627-665c-4df2-bdfd-3c5da8503ae2 · outbound

This paper cites DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model

Reference 13

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Source-reported events for the cited work

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Observation 2243c33a-54a4-4d36-86af-8d390d0d26aa · outbound

This paper cites Pategail: A privacy-preserving mobility trajectory generator with imitation learning,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Pategail: A privacy-preserving mobility trajectory generator with imitation learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.131524Z

Source-reported events for the cited work

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Observation ad53da97-9415-4d4e-99f2-018ed9c99bd7 · outbound

This paper cites Personalized route recommenda- tion using big trajectory data,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Personalized route recommenda- tion using big trajectory data,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.116759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0e2d2df2-8511-4930-b80d-63f3b2db24e6 · outbound

This paper cites A personalized recommendation framework with user trajectory analysis applied in location-based social network (lbsn),.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion A personalized recommendation framework with user trajectory analysis applied in location-based social network (lbsn),

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.102568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1cea652e-fc8f-42b5-8cf3-7d9fb02d8294 · outbound

This paper cites Vision-and-Language Pretrained Models: A Survey.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Vision-and-Language Pretrained Models: A Survey

Reference 17

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Source-reported events for the cited work

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Observation fdb8217c-2ad1-43ff-ac17-003078012160 · outbound

This paper cites Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:18:39.268855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 94102eb0-5298-4f40-adaa-67d79eb55c79 · outbound

This paper cites GPT-4 Technical Report.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion GPT-4 Technical Report

Reference 19

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Source-reported events for the cited work

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Observation 7522bb8b-a5fd-42b8-9299-58c3f1e008f6 · outbound

This paper cites Learning to simulate human mobility,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Learning to simulate human mobility,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.086969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8ee756a5-ecce-4a8e-a178-a81842dfa3e2 · outbound

This paper cites Periodicmove: shift-aware human mobility recovery with graph neural network,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Periodicmove: shift-aware human mobility recovery with graph neural network,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.072946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 34c31bd0-b031-41c3-a71a-1cf5d3a2c825 · outbound

This paper cites Privacy-preserving federated mobility prediction with com- pound data and model perturbation mechanism,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Privacy-preserving federated mobility prediction with com- pound data and model perturbation mechanism,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.058316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c70367b9-5309-492b-86e7-4e3ee5e639bf · outbound

This paper cites Modelling the scaling properties of human mobility,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Modelling the scaling properties of human mobility,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.041947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ae4104e8-0b42-4ec7-92f1-7a2a8e17f733 · outbound

This paper cites Mobtcast: Leveraging auxiliary trajectory forecasting for human mobility prediction,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Mobtcast: Leveraging auxiliary trajectory forecasting for human mobility prediction,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.026802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0ebac7a6-add9-4406-8330-238112aa0930 · outbound

This paper cites Stan: Spatio-temporal attention network for next location recommendation,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Stan: Spatio-temporal attention network for next location recommendation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:40.012098Z

Source-reported events for the cited work

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Observation 6ca65265-468a-4511-9a72-bbc8a34a36b3 · outbound

This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 26

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Source-reported events for the cited work

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Observation 537eb3c4-b497-4493-8775-1530b5b5a5ed · outbound

This paper cites TimeGPT-1.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion TimeGPT-1

Reference 27

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Source-reported events for the cited work

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Observation a3daa863-147c-4b5b-ac1b-d169e3032d05 · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 28

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Source-reported events for the cited work

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Observation 65496fe3-4626-4833-a08d-759e91fd0f0d · outbound

This paper cites Simulating human mobility with a trajectory generation framework based on diffusion model,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Simulating human mobility with a trajectory generation framework based on diffusion model,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.996438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7de67e94-48cf-4c22-a663-219a0ab1f29b · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 30

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This paper cites UrbanGPT: Spatio-Temporal Large Language Models.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion UrbanGPT: Spatio-Temporal Large Language Models

Reference 31

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Observation 3e124cb1-fea5-4636-b29c-2768678e86ff · outbound

This paper cites Csdi: Conditional score- based diffusion models for probabilistic time series imputation,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Csdi: Conditional score- based diffusion models for probabilistic time series imputation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.864831Z

Source-reported events for the cited work

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Observation a294c144-bf65-4419-b045-5913c4d463c0 · outbound

This paper cites Denoising diffusion probabilistic models,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Denoising diffusion probabilistic models,

Reference 33

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Observation 978c1be6-9ce4-4b15-aff9-fe3404c7d3cd · outbound

This paper cites Spatio-temporal Diffusion Point Processes.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Spatio-temporal Diffusion Point Processes

Reference 34

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Source-reported events for the cited work

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Observation 7f59f448-3485-4589-b5ed-9ad3ac24c25d · outbound

This paper cites Towards generative modeling of urban flow through knowledge-enhanced denoising diffusion,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Towards generative modeling of urban flow through knowledge-enhanced denoising diffusion,

Reference 35

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Observation d44ad4f2-e7c1-40be-b431-1516a20d4ba2 · outbound

This paper cites Line: Large-scale information network embedding,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Line: Large-scale information network embedding,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.686534Z

Source-reported events for the cited work

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Observation 20aba6c9-474f-4c9e-b83e-b5f51890ca1d · outbound

This paper cites Summary of chatgpt-related research and perspective towards the future of large language models,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Summary of chatgpt-related research and perspective towards the future of large language models,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.631816Z

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Observation 63d6d982-ee7d-4130-a8e9-2a2ea29e621a · outbound

This paper cites Human trajectory forecasting using a flow-based generative model,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Human trajectory forecasting using a flow-based generative model,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.617201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6a746853-bfd0-4eac-a53f-843d9b8ef46d · outbound

This paper cites Attention is all you need,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Attention is all you need,

Reference 39

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Source-reported events for the cited work

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Observation 3331d4eb-58c2-4884-935a-f9d80e9923d0 · outbound

This paper cites DiffWave: A Versatile Diffusion Model for Audio Synthesis.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion DiffWave: A Versatile Diffusion Model for Audio Synthesis

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T16:18:39.028582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 251f7770-e0ae-47c0-b819-c0c8b6319a15 · outbound

This paper cites Classifier-Free Diffusion Guidance.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Classifier-Free Diffusion Guidance

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T16:18:39.033181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9e80ae7-9029-42e6-acdc-c15e61ad43bc · outbound

This paper cites Behavioral Cloning from Observation.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Behavioral Cloning from Observation

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T16:18:39.037464Z

Source-reported events for the cited work

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Observation 47df5303-3e65-49a9-89ec-fc8829919c24 · outbound

This paper cites Practical synthetic human trajectories generation based on variational point processes,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Practical synthetic human trajectories generation based on variational point processes,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.589265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation aebb0ae0-a94e-493b-ba3f-e2394ec70fdf · outbound

This paper cites Estimating human trajectories and hotspots through mobile phone data,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Estimating human trajectories and hotspots through mobile phone data,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.573390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 51f3ba9f-a5de-4be9-aff7-33fc404941f6 · outbound

This paper cites Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.557249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c39fca3d-9434-4aa6-9a04-082da29df985 · outbound

This paper cites Next place prediction using mobility markov chains,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Next place prediction using mobility markov chains,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.540878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b86c3ec1-a92b-4602-9bfc-162d96876b43 · outbound

This paper cites Hst-lstm: A hierarchical spatial-temporal long- short term memory network for location prediction,.

One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion Hst-lstm: A hierarchical spatial-temporal long- short term memory network for location prediction,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:18:39.475574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation 3399cfdd-4c81-4e2b-bde8-ae7972faab58 · inbound

MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning cites this paper.

MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:21:24.325824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 39f0ed8e-c081-4962-988d-525553b4e509 · inbound

InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories cites this paper.

InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:13:01.303762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.