Paper Citation Record · LEDGER

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction

As of 10 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2607.05705.

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

pith.paper-citation-record.v1
2607.05705 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T03:25:55.830270Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

11 of 11 outbound references displayed

verified exact5
verified fuzzy6
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e8fac5e8-46ed-4ff0-986b-6f4b294fa0cb · outbound

This paper cites Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T03:27:47.620242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec4353cd-7f8d-434d-92c7-51491962778b · outbound

This paper cites A review of deep learning-based vehicle motion pre- diction for autonomous driving.Sustainability (2071-1050), 15(20), 2023.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction A review of deep learning-based vehicle motion pre- diction for autonomous driving.Sustainability (2071-1050), 15(20), 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T03:27:47.685836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d8d968b1-a886-4317-9cc3-1372b9aaed52 · outbound

This paper cites A survey on trajectory-prediction methods for autonomous driving.IEEE Transactions on In- telligent Vehicles, 7(3):652–674, 2022.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction A survey on trajectory-prediction methods for autonomous driving.IEEE Transactions on In- telligent Vehicles, 7(3):652–674, 2022

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T03:27:47.661562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c7527d8b-caa3-4110-a8bf-4082e7ff4af7 · outbound

This paper cites SEPT: Towards Efficient Scene Representation Learning for Motion Prediction.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction SEPT: Towards Efficient Scene Representation Learning for Motion Prediction

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-11T03:27:45.585522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c366ff6-629f-4a54-adf8-4e4898202277 · outbound

This paper cites Motion transformer with global intention localization and lo- cal movement refinement.Advances in Neural Information Processing Systems, 35:6531–6543, 2022.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction Motion transformer with global intention localization and lo- cal movement refinement.Advances in Neural Information Processing Systems, 35:6531–6543, 2022

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T03:27:47.709872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 931a8e08-120e-40fe-8928-5b98776c9ee1 · outbound

This paper cites FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-11T03:27:45.530785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9eefde8-d4a4-408a-9ce0-ac1324368d4d · outbound

This paper cites Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-11T03:27:45.636528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20250e57-b6ec-4263-8070-f1eb0c789976 · outbound

This paper cites DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-11T03:27:45.611912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a4bb1455-9ef9-4800-b180-b8350d83e73e · outbound

This paper cites Tnt: Target-driven trajectory pre- diction.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction Tnt: Target-driven trajectory pre- diction

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T03:27:47.733496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6cd341d0-d866-4d0f-8e1a-756fcf11593d · outbound

This paper cites Query-centric trajectory prediction.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction Query-centric trajectory prediction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T03:27:47.756678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff7ccd53-7a2f-4d1a-9c72-33edd9c1f354 · outbound

This paper cites QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction.

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-11T03:27:45.562012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.