Paper Citation Record · LEDGER

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking

As of 18 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2606.17897.

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

pith.paper-citation-record.v1
2606.17897 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T01:15:49.958166Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

36 of 36 outbound references displayed

verified exact4
verified fuzzy0
unresolved32
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a01e3a9d-4472-45bd-b0cd-e322059c95a7 · outbound

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

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Social lstm: Human trajectory prediction in crowded spaces

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation ccb4af85-022a-47cc-b562-db7846dc489e · outbound

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

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Eigentrajectory: Low-rank descriptors for multi-modal trajectory forecasting

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 419a1e31-99a7-430e-ac85-62c6b07ea7d1 · outbound

This paper cites Mixture density networks.Aston University, 1994.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Mixture density networks.Aston University, 1994

Reference 3

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Observation 7d88b5d2-fd1c-4a7a-8372-2f33b45e4a19 · outbound

This paper cites Redunet: A white-box deep network from the principle of maximizing rate reduction.Journal of machine learning research, 23(114):1–103, 2022.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Redunet: A white-box deep network from the principle of maximizing rate reduction.Journal of machine learning research, 23(114):1–103, 2022

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 762ebd28-7c81-4b78-8dfc-98fe5503b7ca · outbound

This paper cites Personalized trajectory prediction via distribution discrimination.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Personalized trajectory prediction via distribution discrimination

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation c2469564-f28e-4ce1-92e5-0bafcec87300 · outbound

This paper cites Amenet: Attentive maps encoder network for trajectory prediction.ISPRS Journal of Photogrammetry and Remote Sensing, 172:253–266, 2021.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Amenet: Attentive maps encoder network for trajectory prediction.ISPRS Journal of Photogrammetry and Remote Sensing, 172:253–266, 2021

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 9227bb21-72f7-4022-ab9f-0d5b29c00cb6 · outbound

This paper cites Goal-gan: Multimodal trajectory prediction based on goal position estimation.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Goal-gan: Multimodal trajectory prediction based on goal position estimation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 631e06d5-bc73-4a29-a9ac-f5fd7ce88ba6 · outbound

This paper cites Probabilistic crowd gan: Multimodal pedestrian trajectory prediction using a graph vehicle-pedestrian atten- tion network.IEEE Robotics and Automation Letters, 5(4):5026–5033, 2020.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Probabilistic crowd gan: Multimodal pedestrian trajectory prediction using a graph vehicle-pedestrian atten- tion network.IEEE Robotics and Automation Letters, 5(4):5026–5033, 2020

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation a6b2bd89-6f55-4379-9477-258620451024 · outbound

This paper cites Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection.Neural networks, 108:466–478, 2018.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection.Neural networks, 108:466–478, 2018

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation a1998e1b-56aa-4487-b4a2-6b5add9629f3 · outbound

This paper cites Loki: Long term and key intentions for trajectory prediction.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Loki: Long term and key intentions for trajectory prediction

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 02568334-e992-4858-983b-bd1a6ce1c44c · outbound

This paper cites Densetnt: End-to-end trajectory prediction from dense goal sets.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Densetnt: End-to-end trajectory prediction from dense goal sets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation adc39eac-08d4-457b-a53d-398d4e5dfbd2 · outbound

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

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Social gan: Socially acceptable trajectories with generative adversarial networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 84c4696b-062c-4834-8fb3-e8b6aeedc357 · outbound

This paper cites Structural-rnn: Deep learning on spatio-temporal graphs.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Structural-rnn: Deep learning on spatio-temporal graphs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 691d6bfc-c86c-4efd-8c0b-55859cefe568 · outbound

This paper cites Intent-aware long-term prediction of pedestrian motion.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Intent-aware long-term prediction of pedestrian motion

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation cd57c7a0-8783-42b3-8b40-349d13ce0a8e · outbound

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

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation cabe9686-e218-457c-99af-1e3095361643 · outbound

This paper cites Crowds by example.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Crowds by example

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 470dfa5d-ff83-4a24-bb28-57e21752b613 · outbound

This paper cites EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:38:55.480115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e376d094-5f80-438e-9495-7860e18ca977 · outbound

This paper cites End-to-end contextual percep- tion and prediction with interaction transformer.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking End-to-end contextual percep- tion and prediction with interaction transformer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 209524ed-511d-48f5-818b-6777fc9e0e85 · outbound

This paper cites Multimodal motion prediction with stacked transformers.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Multimodal motion prediction with stacked transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 0ab54b76-b07c-472c-ae0c-28eaf9547816 · outbound

This paper cites Segmentation of multivariate mixed data via lossy data coding and compression.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Segmentation of multivariate mixed data via lossy data coding and compression

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

Source-reported events for the cited work

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Observation 2e79c2b8-86ab-4e10-8024-c8d7a0695ae0 · outbound

This paper cites Overcom- ing limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Overcom- ing limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation d04ac6f6-d7df-47f7-827f-31af319ff664 · outbound

This paper cites Smemo: social memory for trajectory forecasting.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Smemo: social memory for trajectory forecasting

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 6eb554f1-6600-4c97-99c4-aae1dd962690 · outbound

This paper cites Good judgments do not require complex cognition.Cognitive processing, 11:103–121, 2010.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Good judgments do not require complex cognition.Cognitive processing, 11:103–121, 2010

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 7e0e6c4c-10e6-4047-b57c-aeaf3b50918a · outbound

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

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation f43f37d1-82dc-4943-a1f1-8458ba97da93 · outbound

This paper cites How simple rules determine pedestrian behavior and crowd disasters.Proceedings of the National Academy of Sciences, 108(17):6884–6888, 2011.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking How simple rules determine pedestrian behavior and crowd disasters.Proceedings of the National Academy of Sciences, 108(17):6884–6888, 2011

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 5b9b2324-1411-4c57-950c-eefd55c0dcb2 · outbound

This paper cites Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:38:55.482663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 67ddf789-9273-4380-a99f-3fff37f8b673 · outbound

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

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking You’ll never walk alone: Modeling social behavior for multi-target tracking

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 1086d3d4-3a10-4f78-a72d-dcabde131384 · outbound

This paper cites Stir- net: A spatial-temporal interaction-aware recursive network for human trajectory prediction.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Stir- net: A spatial-temporal interaction-aware recursive network for human trajectory prediction

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation ee1c99d6-4045-412a-8e1e-f5c1be9fbf62 · outbound

This paper cites Sophie: An attentive gan for predicting paths compliant to social and physical constraints.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Sophie: An attentive gan for predicting paths compliant to social and physical constraints

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 02595a70-6bef-4432-ac33-0c457e4e5572 · outbound

This paper cites Multimodal interaction-aware trajectory prediction in crowded space.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Multimodal interaction-aware trajectory prediction in crowded space

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 1bb91c96-f754-4141-92b1-1cc19b28d8f1 · outbound

This paper cites Multiple futures prediction.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Multiple futures prediction

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 5e43ea0c-2966-4d20-b799-e4fca2a41f09 · outbound

This paper cites Spatio- temporal graph transformer networks for pedestrian trajectory predic- tion.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Spatio- temporal graph transformer networks for pedestrian trajectory predic- tion

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation 9fe19e1d-40b4-410a-9e7d-de0753af168c · outbound

This paper cites AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:38:55.477486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3fa3831e-bb50-4a5e-aee9-5cba8724b92b · outbound

This paper cites Map-Adaptive Goal-Based Trajectory Prediction.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Map-Adaptive Goal-Based Trajectory Prediction

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:38:55.474796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a0dc141a-b4bf-4a24-b01c-0e951277b3c1 · outbound

This paper cites Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Observation d5b8fab1-3e72-4def-bae3-10aa8762801c · outbound

This paper cites Where are you heading? dynamic trajectory prediction with expert goal examples.

Learn to Quantify Social Interaction with Constraints for Pedestrian Walking Where are you heading? dynamic trajectory prediction with expert goal examples

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-27T01:15:49.958166Z

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Pith citing papers

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