Pith. sign in

Paper Citation Record · LEDGER

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction

As of 21 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2502.02504.

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

pith.paper-citation-record.v1
2502.02504 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:59:06.798046Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

68 of 68 outbound references displayed

  • verified exact0
  • verified fuzzy60
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 307ad4e8-32b4-476d-befd-284f4fc2cac3 · outbound

This paper cites Intention-aware online pomdp planning for autonomous driving in a crowd,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Intention-aware online pomdp planning for autonomous driving in a crowd,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.342931Z

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.

source=pdf_text observed=2026-08-09T11:59:06.635944Z digest=sha256:e016c33fd5cdfa0eb73afe6b942604887280121f7ac2af9a5ec911d7f532fdcf

Observation 1c433a05-a259-4921-8b4c-043fad2024ce · outbound

This paper cites Multimodal pedestrian trajectory prediction using probabilistic proposal network,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Multimodal pedestrian trajectory prediction using probabilistic proposal network,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.335539Z

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.

source=pdf_text observed=2026-08-09T11:59:06.639253Z digest=sha256:6077a61781d49f46006a7cc71b4efffad79b5950ad1e9e13fc85357b3a780171

Observation 2e4d28a4-6cb0-4459-9b9f-d765673cb6c0 · outbound

This paper cites Reciprocal twin networks for pedestrian motion learning and future path prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Reciprocal twin networks for pedestrian motion learning and future path prediction,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.327869Z

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.

source=pdf_text observed=2026-08-09T11:59:06.642084Z digest=sha256:4d878dd7ebe12f3b06788e4b91072b5b71192f42b9dc3e5485db68856d44ec34

Observation e6e0ef2a-b4b5-4767-9dbd-fe14c39d397b · outbound

This paper cites Prediction of pedestrian crossing behavior based on surveillance video,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Prediction of pedestrian crossing behavior based on surveillance video,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.319652Z

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.

source=pdf_text observed=2026-08-09T11:59:06.644781Z digest=sha256:0394b08a0b433d673855f897bbcb793dd03e73cf55b12661ec2814038d056eb2

Observation 3f24e7c0-63a9-4254-9489-152634059a48 · outbound

This paper cites Trajectorycnn: a new spatio-temporal feature learning network for human motion prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Trajectorycnn: a new spatio-temporal feature learning network for human motion prediction,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.311340Z

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.

source=pdf_text observed=2026-08-09T11:59:06.647605Z digest=sha256:0ac8073c7e558cdf2c7e8415d49fcf04582548a9713d1b7c4e82007cd30ffcc4

Observation d65bbc65-c2c6-42e3-9d37-caaba4ba0136 · outbound

This paper cites Exploring spatio–temporal graph convolution for video- based human–object interaction recognition,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Exploring spatio–temporal graph convolution for video- based human–object interaction recognition,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.302482Z

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.

source=pdf_text observed=2026-08-09T11:59:06.650162Z digest=sha256:2e0ef41459f75b1082c133ecc8f7e9f5695858431e40a46722f2876a459cecbb

Observation 6dfc8bf5-81ff-44ad-a394-504f4203a030 · outbound

This paper cites Sgcn: Sparse graph convolution network for pedestrian trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Sgcn: Sparse graph convolution network for pedestrian trajectory prediction,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.294136Z

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.

source=pdf_text observed=2026-08-09T11:59:06.652997Z digest=sha256:8ebddc1af326f56ae4b8e4afb76479b10ad865d744de0132751b8072f913c8ae

Observation 00cfdcb6-8ba9-4eb8-9eaf-1911daac5677 · outbound

This paper cites Multiclass-sgcn: Sparse graph- based trajectory prediction with agent class embedding,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Multiclass-sgcn: Sparse graph- based trajectory prediction with agent class embedding,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.286137Z

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.

source=pdf_text observed=2026-08-09T11:59:06.655450Z digest=sha256:b168452d5d115eb107a9874be3cc6ed3ea42af9dd9ccfb607fc1b62b78bc5b7e

Observation 4403e06b-648f-4e59-ad31-1662e527ff19 · outbound

This paper cites Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.277590Z

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.

source=pdf_text observed=2026-08-09T11:59:06.657725Z digest=sha256:6dd16b38988b924af1d53935a4c74e755c52cd0553cadf8265b40ee27aa22543

Observation bd5c0688-82ab-4570-a8b9-c560b4e5f780 · outbound

This paper cites Stgat: Modeling spatial-temporal interactions for human trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Stgat: Modeling spatial-temporal interactions for human trajectory prediction,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.269108Z

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.

source=pdf_text observed=2026-08-09T11:59:06.660026Z digest=sha256:496109e4ca90f56a6387462670dae7da3d17959d1ec0b58f4a08cc4c6af4f1f3

Observation 2c4de28b-ad08-4a07-9d4c-9e790be80cbd · outbound

This paper cites Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.260674Z

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.

source=pdf_text observed=2026-08-09T11:59:06.662289Z digest=sha256:bab9bd38d476c3d33bd511a2437cb198f516be677c78b4d02097c6228730d987

Observation 6919dd4d-e0f3-4c51-ab59-a24c66b9ffc0 · outbound

This paper cites Learning pedestrian group repre- sentations for multi-modal trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Learning pedestrian group repre- sentations for multi-modal trajectory prediction,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.252699Z

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.

source=pdf_text observed=2026-08-09T11:59:06.664593Z digest=sha256:85f3b2091634f759f6d13e4dc5f0548c1ac4292a308f81d4c441d05160a1f5da

Observation 7851724e-ee05-4cf2-8b9f-ab2a07221eff · outbound

This paper cites Eigentrajectory: Low-rank descriptors for multi-modal trajectory forecasting,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Eigentrajectory: Low-rank descriptors for multi-modal trajectory forecasting,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.244993Z

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.

source=pdf_text observed=2026-08-09T11:59:06.666931Z digest=sha256:8b2b525ce22c5bdb37c2bcbe9d698817904530b8268a919a1702e61df7ecf382

Observation 0cf10ef6-b1f5-41d0-9f0c-0a7bfce9ac5b · outbound

This paper cites A set of control points conditioned pedestrian trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction A set of control points conditioned pedestrian trajectory prediction,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.237140Z

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.

source=pdf_text observed=2026-08-09T11:59:06.669277Z digest=sha256:ff26a6f425168f713c4cd12d99d9f47831d487502881f559a843dfdfc3a3607b

Observation 41f878d8-8f11-4e51-9f49-0296fdfa08f6 · outbound

This paper cites Graph Attention Networks,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Graph Attention Networks,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.229166Z

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.

source=pdf_text observed=2026-08-09T11:59:06.671633Z digest=sha256:d721012353421b0721c71c5604b20a9cdad0af44bbbbc7eab9ffbdfb2640c46b

Observation 430910ee-ab81-4800-a91e-e71fba923ce0 · outbound

This paper cites Long short-term memory,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Long short-term memory,

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-09T11:59:06.674277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:59:06.674277Z digest=sha256:a7074cc5a98776f08ee134dbed534d9738c3272b83d0db60e42aa2c21a0d3669

Observation 619da840-cdca-4bfc-ab97-9d5ddb06fb49 · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Semi-supervised classification with graph convolutional networks,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.216499Z

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.

source=pdf_text observed=2026-08-09T11:59:06.676951Z digest=sha256:680027f8c6f3d582c734b1352d8cfa02b1a4cae5ce1f74f822131ee8538f30bc

Observation e1c9cf35-d3e5-42b6-a9a2-c262be01124f · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-09T11:59:06.679327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:59:06.679327Z digest=sha256:abd9ce7158b39ee3b1781184345cb8ff81f48b301fe29a6736773c1cda711554

Observation e71f1c33-c79c-4cc3-9057-6142060bb88d · outbound

This paper cites Nodemixup: Tackling under-reaching for graph neural networks,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Nodemixup: Tackling under-reaching for graph neural networks,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.208883Z

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.

source=pdf_text observed=2026-08-09T11:59:06.681998Z digest=sha256:2a2ce9d877d1a653e8492c1ee0def1d5a29e4405c796c7105441cd79f1a4c9f1

Observation 33768dd4-d79d-47ce-b366-f1b8165740fa · outbound

This paper cites Understanding over- squashing in gnns through the lens of effective resistance,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Understanding over- squashing in gnns through the lens of effective resistance,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.200298Z

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.

source=pdf_text observed=2026-08-09T11:59:06.684259Z digest=sha256:ef10d72f0118bbc3e66464c49d0dcc76744a75e3d402f2bafee5c94ecafbe703

Observation 00994ed6-9ef6-472c-ae84-08090c02a911 · outbound

This paper cites Fully- connected spatial-temporal graph for multivariate time-series data,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Fully- connected spatial-temporal graph for multivariate time-series data,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.191562Z

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.

source=pdf_text observed=2026-08-09T11:59:06.686526Z digest=sha256:cc22fd3bffb35f3c60ceb023866ce025e64697c1c388cb34747ff571382bb235

Observation 31ca2fdf-c965-4eaa-806c-3aff5d260f6f · outbound

This paper cites FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.182506Z

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.

source=pdf_text observed=2026-08-09T11:59:06.688780Z digest=sha256:79a41cc69881d53ef9d2527ebcf46e0910fa4083c6e47a6c70a4ad12fede419d

Observation 04275537-c399-4af7-8513-d885c6dd9e2a · outbound

This paper cites Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.172827Z

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.

source=pdf_text observed=2026-08-09T11:59:06.690936Z digest=sha256:b6e1d90488d039a964966ffd68f40f345f9b00bac8c201b42e2340941eff3e2b

Observation e18fa079-2180-42aa-ba55-f61d70f69273 · outbound

This paper cites Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-09T11:59:06.693038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:59:06.693038Z digest=sha256:2ed29ee62c671718fc307909f7537d781815569bd4b048ac593805b179b20198

Observation b6b961d7-c86d-43af-ab59-5bf970cd533a · outbound

This paper cites Deciphering spatio-temporal graph forecasting: A causal lens and treatment,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Deciphering spatio-temporal graph forecasting: A causal lens and treatment,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.164784Z

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.

source=pdf_text observed=2026-08-09T11:59:06.695503Z digest=sha256:a3bc3977d26e19471f662839083598b9fedcfe263a29fda61f3a998541fc47bb

Observation c933578c-bfe5-4bac-9760-8b55699b700e · outbound

This paper cites Heterogeneous graph convolutional neural network via hodge-laplacian for brain functional data,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Heterogeneous graph convolutional neural network via hodge-laplacian for brain functional data,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.156550Z

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.

source=pdf_text observed=2026-08-09T11:59:06.698016Z digest=sha256:62318ba77d3f3b1909a99585e0dc65f58ef83e2f9004b15e132909670067393d

Observation e8c39842-b2ee-418f-b27b-4722b2a75f29 · outbound

This paper cites Deep dual graph attention auto-encoder for community detection,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Deep dual graph attention auto-encoder for community detection,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.148384Z

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.

source=pdf_text observed=2026-08-09T11:59:06.700516Z digest=sha256:b80ec10e37df4796614e2e7acd4dadd2d0b26460197764a2922ad5197dc472bd

Observation 9c90ecbc-efd3-4270-8fc8-73dc143b5537 · outbound

This paper cites First-order operators and boundary triples,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction First-order operators and boundary triples,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.140507Z

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.

source=pdf_text observed=2026-08-09T11:59:06.703237Z digest=sha256:f23afab2f342c1f848b450becd561321d0a6c447c1dec4141d66de7a9224d547

Observation 0f1743d0-7f35-42f3-8a2e-09449a4ba54d · outbound

This paper cites Attention is all you need,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Attention is all you need,

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-09T11:59:06.705857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:59:06.705857Z digest=sha256:bf2544969ac796419c5853fb528038f916b43b173e31ef00f3167a87a0f1cff0

Observation 96f5a4cf-4079-4ca1-a1e5-3f2cad96744d · outbound

This paper cites You’ll never walk alone: Modeling social behavior for multi-target tracking,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction You’ll never walk alone: Modeling social behavior for multi-target tracking,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.127914Z

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.

source=pdf_text observed=2026-08-09T11:59:06.708604Z digest=sha256:45cc3ee465d12412ec3c501258a7ca535baeff304ac40a65e5a435ec70b0c5bf

Observation b8be0f26-881e-4595-b4c1-7be17a8be52d · outbound

This paper cites Crowds by example,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Crowds by example,

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-09T11:59:06.711157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:59:06.711157Z digest=sha256:71662e3f3af61b90ca48f59923309cd85ba267319a74e357edf0236e43b0c141

Observation ec4e5718-f74f-4342-9435-0e685abc67d8 · outbound

This paper cites Learning social etiquette: Human trajectory understanding in crowded scenes,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Learning social etiquette: Human trajectory understanding in crowded scenes,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.115280Z

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.

source=pdf_text observed=2026-08-09T11:59:06.713749Z digest=sha256:c94e46f358ea991ebd4cd8360ef9f4b177f96fb106bdda0a22a001232958f60e

Observation 6d2435d3-d135-4d74-a71f-34500cb5d945 · outbound

This paper cites Social lstm: Human trajectory prediction in crowded spaces,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Social lstm: Human trajectory prediction in crowded spaces,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.107804Z

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.

source=pdf_text observed=2026-08-09T11:59:06.716613Z digest=sha256:211243a41baaba1ab9fc65ff0f79481c5088994beb3e6e9ee410fc4f20a6fb52

Observation 7db22741-504d-41b7-b18b-490a2ea0fc1e · outbound

This paper cites Social gan: Socially acceptable trajectories with generative adversarial networks,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Social gan: Socially acceptable trajectories with generative adversarial networks,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.100880Z

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.

source=pdf_text observed=2026-08-09T11:59:06.719090Z digest=sha256:50d6541551b84edab1c618357b13abeb57bba7a07d4b210c0471484f4dd4cb94

Observation 3ec7274f-abe9-43f5-8a50-84d224c285ee · outbound

This paper cites Geometric features informed multi-person human-object interaction recognition in videos,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Geometric features informed multi-person human-object interaction recognition in videos,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.093819Z

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.

source=pdf_text observed=2026-08-09T11:59:06.721689Z digest=sha256:5c4002f42a8605bead137faf101e7119aefd6d9c5552037db74fe8182f4020df

Observation 480a977a-7cf5-4d9f-85b4-997e98ad7ab1 · outbound

This paper cites Spatial temporal graph convolutional networks for skeleton-based action recognition,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Spatial temporal graph convolutional networks for skeleton-based action recognition,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.086788Z

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.

source=pdf_text observed=2026-08-09T11:59:06.723945Z digest=sha256:74cc05cdc4fa9c31ddc7a859cf53a77f77407714b4d4bb9bfb874c87086a2b5f

Observation b49f8800-3b75-422d-adf3-79229fb65c5d · outbound

This paper cites Skeleton-based human ac- tion recognition via large-kernel attention graph convolutional network,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Skeleton-based human ac- tion recognition via large-kernel attention graph convolutional network,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.078463Z

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.

source=pdf_text observed=2026-08-09T11:59:06.726232Z digest=sha256:2a31b976f8f56d48ff77acfedbb1c21b401c7160f6336212c9e4e944e55d3b9e

Observation 4c1bdaa8-deae-48ae-a13d-789882e47ec1 · outbound

This paper cites Multiphysical graph neural network (mp-gnn) for covid-19 drug design,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Multiphysical graph neural network (mp-gnn) for covid-19 drug design,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.070310Z

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.

source=pdf_text observed=2026-08-09T11:59:06.728389Z digest=sha256:e7b6325f35b5b94d45aa645b621f7ecc3af2b3219e0f4d3dc6b9b092515f775c

Observation a1a32bc8-79fa-4cf0-a035-76e0c1084ffc · outbound

This paper cites Neural graph collaborative filtering,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Neural graph collaborative filtering,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.062367Z

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.

source=pdf_text observed=2026-08-09T11:59:06.730546Z digest=sha256:3327181a574401239cfb0d8b6db359812aaf1f55d43a7820871a92f9bc1a000b

Observation b941f8f7-7a34-470d-afe6-b5d040b0652a · outbound

This paper cites Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.054233Z

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.

source=pdf_text observed=2026-08-09T11:59:06.732720Z digest=sha256:afbf841557d42d99ffef118a04711c35b2f093e61ef48838eceafdd84fbbc8ad

Observation e18a352b-fe1a-4b2f-9647-791c18291fe5 · outbound

This paper cites Trajectory unified transformer for pedestrian trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Trajectory unified transformer for pedestrian trajectory prediction,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.047043Z

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.

source=pdf_text observed=2026-08-09T11:59:06.734961Z digest=sha256:dccf44f3b6e473af9edc6850dcc004a7016052c1ec2fe643d0bced912b88b461

Observation 20d3dac6-e9b6-45f3-ae9c-95e1a7100f8d · outbound

This paper cites Uncovering the missing pattern: Unified framework towards trajectory imputation and prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Uncovering the missing pattern: Unified framework towards trajectory imputation and prediction,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.039343Z

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.

source=pdf_text observed=2026-08-09T11:59:06.737119Z digest=sha256:911d498e43230cbdbf601f1ef4d4efe9a98ca93107cedda25d358d7ce46c07a4

Observation 27dec39b-2654-49ff-8ae5-348dfb60c15e · outbound

This paper cites Mfan: Mixing feature attention network for trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Mfan: Mixing feature attention network for trajectory prediction,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.031080Z

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.

source=pdf_text observed=2026-08-09T11:59:06.739235Z digest=sha256:b4617c243419efa31925d4eac2c19c0f4d233b2ab5023492815eeac57e382400

Observation 61dad6cc-43b9-495f-b387-8476d0b19b39 · outbound

This paper cites Socialvae: Human trajectory prediction using timewise latents,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Socialvae: Human trajectory prediction using timewise latents,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.024024Z

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.

source=pdf_text observed=2026-08-09T11:59:06.741328Z digest=sha256:cc939d7b0e4dc941b8ae344d9fadcc7e2bcadf5b0f8bf5e83fec6dc407564d0f

Observation f0ac1135-052c-411e-a954-d9c10c2caaf3 · outbound

This paper cites Aut- ofocusing for synthetic aperture imaging based on pedestrian trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Aut- ofocusing for synthetic aperture imaging based on pedestrian trajectory prediction,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.016215Z

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.

source=pdf_text observed=2026-08-09T11:59:06.743723Z digest=sha256:8629556e483530b3d83b9cc4dedaf46f00ec1992dc57cd8c20a285ccbdee88cc

Observation bb2c2707-edb2-426c-a434-46251a32fb27 · outbound

This paper cites Context-aware human trajectories prediction via latent variational model,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Context-aware human trajectories prediction via latent variational model,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.008966Z

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.

source=pdf_text observed=2026-08-09T11:59:06.745878Z digest=sha256:971a11c586ff35da6a6b1b35acaae08eae4066d0ac10d7f936ebadab508c7dbe

Observation ad013bea-6c0c-4bdc-8d8f-fe9be223f72c · outbound

This paper cites Mrgtraj: A novel non- autoregressive approach for human trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Mrgtraj: A novel non- autoregressive approach for human trajectory prediction,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:07.001515Z

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.

source=pdf_text observed=2026-08-09T11:59:06.748095Z digest=sha256:e9bf0d82723b956b49f468bc0f70e18eb6849dfd63f287e8b69046d3531c7946

Observation e1543f24-2c2c-42bd-91af-d1e6eef4ad79 · outbound

This paper cites Pedestrian trajectory prediction using dynamics-based deep learning,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Pedestrian trajectory prediction using dynamics-based deep learning,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.994145Z

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.

source=pdf_text observed=2026-08-09T11:59:06.750294Z digest=sha256:d09c010ad092c38615559b6ceb3642ae2b42644cfea4486451891e240e543271

Observation ce18d079-c348-4cdd-8e3f-8848d4ce59d3 · outbound

This paper cites Minimizing effective resistance of a graph,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Minimizing effective resistance of a graph,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.987221Z

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.

source=pdf_text observed=2026-08-09T11:59:06.752456Z digest=sha256:d5619fd4785d6142a9128223539f9d1a23a446d3f83726ea538a04a29b06fd13

Observation 96a88f96-c1fb-40dc-9b7e-7d59736960a4 · outbound

This paper cites The moore–penrose inverse of the normalized graph lapla- cian,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction The moore–penrose inverse of the normalized graph lapla- cian,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.980059Z

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.

source=pdf_text observed=2026-08-09T11:59:06.754724Z digest=sha256:914fb47b72b0df6c0250062f4b8d5fa8b0a9b7c9c2e4f7f6523f47aba65adfc0

Observation 6dfda90a-d8f6-4cd1-8308-b628a9424201 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Imagenet classification with deep convolutional neural networks,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.971763Z

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.

source=pdf_text observed=2026-08-09T11:59:06.756976Z digest=sha256:04fd574d2893c2d9f22e266182843a61dbc7c0eda32e9887ea89197b0c08a247

Observation e6ccf175-1f8f-481e-9b2c-2de0f8970728 · outbound

This paper cites How Attentive are Graph Attention Networks?.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction How Attentive are Graph Attention Networks?

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-09T11:59:06.759178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:59:06.759178Z digest=sha256:61407de765a7f749a81cd5b964a8b763e504b144c5cb06470adfa65450937186

Observation 9f07a5a1-3680-4b08-bdee-a86cc799ab07 · outbound

This paper cites Multi- stream representation learning for pedestrian trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Multi- stream representation learning for pedestrian trajectory prediction,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.963562Z

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.

source=pdf_text observed=2026-08-09T11:59:06.761789Z digest=sha256:b343adbc2d6d4db0e9df4162140fb1ae87e353c01b78b690ea3e0e6baf1c0677

Observation c653cda2-b02d-44b6-8d3c-bdaf703ab60b · outbound

This paper cites Autoregressive Image Generation without Vector Quantization.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Autoregressive Image Generation without Vector Quantization

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-09T11:59:06.764258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:59:06.764258Z digest=sha256:05898bf728e049ff221e9b08e4b04ef4926c4bc13f20da9d53e0d6fa04f991c2

Observation 524cb327-8e28-4314-b748-504baa4b13a6 · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-09T11:59:06.766838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:59:06.766838Z digest=sha256:6a84728967eaa2fd94041032aa8f4d1634d2b7dee2fae9d7fa9ae66d79646769

Observation 697f8ac6-af5d-475b-a528-6e13b41a3386 · outbound

This paper cites Social- implicit: Rethinking trajectory prediction evaluation and the effective- ness of implicit maximum likelihood estimation,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Social- implicit: Rethinking trajectory prediction evaluation and the effective- ness of implicit maximum likelihood estimation,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.955640Z

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.

source=pdf_text observed=2026-08-09T11:59:06.769500Z digest=sha256:67497c9066149573be199cc31af51d488a8459530f1fd7add673b3ed8ef08226

Observation 7fa620cc-7d0a-43b0-89e2-0410a038b387 · outbound

This paper cites Remember intentions: Retrospective-memory-based trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Remember intentions: Retrospective-memory-based trajectory prediction,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.947270Z

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.

source=pdf_text observed=2026-08-09T11:59:06.771797Z digest=sha256:277525a4232d69d6a7fca6ba45343354c891fbfac7f4f3efb66f8234022cab95

Observation 226edc5c-9c60-44fe-a6d2-c39f07cca434 · outbound

This paper cites Leapfrog diffusion model for stochastic trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Leapfrog diffusion model for stochastic trajectory prediction,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.940078Z

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.

source=pdf_text observed=2026-08-09T11:59:06.773952Z digest=sha256:94fe7814c3d9e043390618211c15f4cefc33002431f30be9b1d876405ea92dc7

Observation a5221120-2ec2-4a0f-88b9-4d464b37cb7f · outbound

This paper cites Eqmotion: Equivariant multi-agent motion prediction with invariant interaction reasoning,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Eqmotion: Equivariant multi-agent motion prediction with invariant interaction reasoning,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.932507Z

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.

source=pdf_text observed=2026-08-09T11:59:06.776161Z digest=sha256:d9b36580c0f9ecba5ecdb07c327583dfcdbde183151e53a316589e3ac0e0bed4

Observation dfb7fc0b-016d-4eb1-8883-c7db8e5c3816 · outbound

This paper cites Smemo: social memory for trajectory forecasting,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Smemo: social memory for trajectory forecasting,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.924990Z

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.

source=pdf_text observed=2026-08-09T11:59:06.778313Z digest=sha256:e8e3dbf7bfe605f65b018b01a68f670e521c95032b6a22f7ff38bbe6573a5c17

Observation f23fe64d-cb8f-499b-8496-cdad23955845 · outbound

This paper cites Singulartrajectory: Universal trajec- tory predictor using diffusion model,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Singulartrajectory: Universal trajec- tory predictor using diffusion model,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.917585Z

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.

source=pdf_text observed=2026-08-09T11:59:06.780613Z digest=sha256:a7d25c7549083a79f9db60b596929f0f9cc1285d55ba1515ade3cab1ed5bec9d

Observation 2c5e9af1-9706-495d-89f8-9e15ec68a0fe · outbound

This paper cites Higher- order relational reasoning for pedestrian trajectory prediction,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Higher- order relational reasoning for pedestrian trajectory prediction,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.909856Z

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.

source=pdf_text observed=2026-08-09T11:59:06.782827Z digest=sha256:c38a5d1121fdb05e778e531e926a00e06594cd1bb1d04ced43125b59ea6a1624

Observation 20b79224-5356-4abe-9529-0d851ead0552 · outbound

This paper cites Adaptive trajectory prediction via transferable gnn,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Adaptive trajectory prediction via transferable gnn,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.901988Z

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.

source=pdf_text observed=2026-08-09T11:59:06.785339Z digest=sha256:23d2acbb6044961809cb432368f4801e62069cd559cd9a0be20960e6e5bd71ea

Observation 8a7fa034-1f38-4469-9a18-6a5a1a564db3 · outbound

This paper cites Inductive representation learning on large graphs,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Inductive representation learning on large graphs,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.893622Z

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.

source=pdf_text observed=2026-08-09T11:59:06.787874Z digest=sha256:e7d26e8baaecabcc34fbdc7eb81ecb9f9d150036c745ecf13c2024f3918d91b1

Observation 70b2abd7-6b2f-49d2-a371-f036515a8140 · outbound

This paper cites Skeleton-based action recog- nition using sparse spatio-temporal gcn with edge effective resistance,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Skeleton-based action recog- nition using sparse spatio-temporal gcn with edge effective resistance,

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.885671Z

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.

source=pdf_text observed=2026-08-09T11:59:06.790421Z digest=sha256:757e1f9c86dfcfee40bc51cfbcd0c0ee46c459171def53b31fe7efaddeaa1efa

Observation 2e622f9e-bf20-4a61-9004-92b0b9bd90cd · outbound

This paper cites Hdmixer: Hierarchical dependency with extendable patch for multivariate time series forecasting,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Hdmixer: Hierarchical dependency with extendable patch for multivariate time series forecasting,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.877981Z

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.

source=pdf_text observed=2026-08-09T11:59:06.792888Z digest=sha256:155b36f49fdb04c34d0675c3c133d7953323577b4111c24b968b49bcbd79ce2a

Observation b5f638f6-c38a-49c5-9ac9-db7c9b85d35a · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Flashattention: Fast and memory-efficient exact attention with io-awareness,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.869730Z

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.

source=pdf_text observed=2026-08-09T11:59:06.795473Z digest=sha256:9ff05e59672ba6c6b16d2c62b1e0032be389fc715dcbfaf7a0070be7b5f61755

Observation d217ed4e-44b2-4e5e-8f64-d884e1698492 · outbound

This paper cites Efficient transformers: A survey,.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction Efficient transformers: A survey,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T11:59:06.860328Z

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.

source=pdf_text observed=2026-08-09T11:59:06.798046Z digest=sha256:cc8fb2f06039589a1b7dd1237872ccf3f26635c44af1945c8d37a7e1f27e8bca

Pith citing papers

No inbound Pith citation observations are available.