Paper Citation Record · LEDGER

Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2407.00959.

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

pith.paper-citation-record.v1
2407.00959 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:47:15.618860Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T03:00:48.147333Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a48b6272-1814-4d61-89e8-cb5581b1cd7d · inbound

CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving cites this paper.

CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:15.618860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58afaf7b-3bba-43bb-a8f4-de1681eb3256 · inbound

ZeroVO: Visual Odometry with Minimal Assumptions cites this paper.

ZeroVO: Visual Odometry with Minimal Assumptions Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T05:26:59.211513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1d85e7c-c101-43f5-80d6-297648854d35 · inbound

RoCA: Robust Cross-Domain End-to-End Autonomous Driving cites this paper.

RoCA: Robust Cross-Domain End-to-End Autonomous Driving Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T04:40:59.793901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b00264-919b-431d-af25-3d02d058be4f · inbound

AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning cites this paper.

AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:46:44.147732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a9ccdc26-e61b-4987-9153-af437198151c · inbound

SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling cites this paper.

SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T22:40:47.787448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e137ab1a-9a32-4885-b840-9e4ce1c8f8e9 · inbound

A Survey on Vision-Language-Action Models for Autonomous Driving cites this paper.

A Survey on Vision-Language-Action Models for Autonomous Driving Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 118

Resolution: unresolved; no resolver link, observed 2026-08-06T21:31:04.734583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3e99cc9-d179-489f-bba1-2a693627bbfe · inbound

World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model cites this paper.

World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T21:19:30.946025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83e55ae1-0635-4c74-b000-df6125d03a03 · inbound

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding cites this paper.

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:05.773718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d48c2c8a-1301-49e7-968a-9a613173c0ae · inbound

ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving cites this paper.

ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T17:04:50.147570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab51fd68-f1e7-46fc-926e-310b7fdaf1f1 · inbound

DriveQA: Passing the Driving Knowledge Test cites this paper.

DriveQA: Passing the Driving Knowledge Test Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-05T13:58:11.050868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ddf4100-317d-4906-8c86-395f3bd4d1be · inbound

Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey cites this paper.

Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-04T19:19:25.582674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 539ec2d6-aa7e-41d2-b193-4f765a738c95 · inbound

All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles cites this paper.

All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-18T03:00:48.150606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 621fa7a1-cda4-47b1-8025-961b17f10154 · inbound

SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles cites this paper.

SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:50:50.863811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 76f73ccd-6671-43f6-a57b-43b13b03ec15 · inbound

DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving cites this paper.

DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 102

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:31:26.236731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3db044b7-6f50-44f6-8319-adeabb5178ea · inbound

OpenLongTail: Generative Scaling of Long-Tail Driving Data cites this paper.

OpenLongTail: Generative Scaling of Long-Tail Driving Data Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-13T01:26:27.220907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.