Paper Citation Record · LEDGER

Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2412.05334.

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

pith.paper-citation-record.v1
2412.05334 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:06:02.167060Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:46:44.168416Z

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 5c276bf2-04ca-4f40-ae3c-1a6b43941ab4 · inbound

Surprise Potential as a Measure of Interactivity in Driving Scenarios cites this paper.

Surprise Potential as a Measure of Interactivity in Driving Scenarios Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T18:26:50.365882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aa4e8c9-2163-4a9f-87bf-37312afdda51 · inbound

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback cites this paper.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:02.167060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e63a2b69-2e9b-4a18-8f80-91b4a96d8448 · inbound

Improving Traffic Signal Data Quality for the Waymo Open Motion Dataset cites this paper.

Improving Traffic Signal Data Quality for the Waymo Open Motion Dataset Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:45:19.946611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78e1d8ad-a17c-4acc-9f0d-093081f97b2c · 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 Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:46:44.170511Z

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.

Observation 9bff8959-1a96-47d1-bd56-860cd7df1397 · inbound

Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving cites this paper.

Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T11:30:39.779155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33a8e4b2-819d-4790-870b-7e1330a3f078 · 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 Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:31:26.004374Z

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.

Observation 856b6b33-650e-4e75-8a38-0ee3bbd5d889 · inbound

Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs cites this paper.

Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T00:13:57.230752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.