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Paper Citation Record · LEDGER

Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2410.05582.

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

pith.paper-citation-record.v1
2410.05582 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:46:33.618655Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:48:39.571719Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
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  • 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 a226a2d0-f3b1-4aca-be3d-c7a0307ed3c3 · inbound

Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling cites this paper.

Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T14:46:33.618655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:46:33.618655Z digest=sha256:dbf8210b6a9f8d1a7abb7d0a17e3891eb22cdec879b9a6939e7c4f32148c7275

Observation e537f5a7-12df-478e-b541-889395048913 · inbound

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation cites this paper.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T12:45:48.866794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:45:48.866794Z digest=sha256:e5920f4ba457fc9106c8a8a9968b9624e2a04a04663c485ce9b6fcbd611c9752

Observation cbdb0f18-27ab-4a17-b528-c2fbcf67f858 · inbound

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving cites this paper.

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 97

Resolution
unresolved
no resolver link, observed 2026-08-07T04:46:48.775841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:46:48.775841Z digest=sha256:4a48854bdfc7c6b898a003af7cccc8e0113ed7644fe98c46072e890ad8556d49

Observation da35f5c0-5c4a-45d5-966b-c8241884670d · 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 Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:46:44.198642Z

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.

source=pdf_text observed=2026-05-14T21:46:43.955825Z digest=sha256:8a768e95af0603d1fa606ff0c6360e0ba502ad4a56438971df1726375a1ba971

Observation 428f1cfd-7fd3-4cd3-b859-83fe0c46c665 · inbound

RDPO: Real Data Preference Optimization for Physics Consistency Video Generation cites this paper.

RDPO: Real Data Preference Optimization for Physics Consistency Video Generation Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:49.116391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:49.116391Z digest=sha256:781efeaaee3e98b51648b40a9d59bce861a8cc73d3ed97781626b841a9c04838

Observation fad6d6dc-3dd3-439b-ab0b-64ebaa7bfc6d · inbound

DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction cites this paper.

DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T21:56:06.190230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:56:06.190230Z digest=sha256:c63f70af2fbb0200a83eec0afff80e6ee31897e4fe8f893d52c03fce5822d44d

Observation 80fd0502-6b62-45d3-9672-cee026d09007 · inbound

A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach cites this paper.

A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-03T16:52:51.982939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:52:51.982939Z digest=sha256:04cf7b9be49c300ee2a0a9659416f370ffc1dcae9f4e817cbc1355b6610f5e5d

Observation c8c6119a-69a2-4d16-9d4b-c82d337215df · inbound

Pulse Breathing Dynamics in a Mode-Locked Laser measured via SHG autocorrelation cites this paper.

Pulse Breathing Dynamics in a Mode-Locked Laser measured via SHG autocorrelation Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-07-13T18:29:56.718560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T18:29:56.718560Z digest=sha256:95fd553a430f52d243d3b07636e2708c0f6b984b0fb85fb0c26689e4ef08af1a

Observation e9d4edf1-5119-4f17-8189-9e38c16eb4e8 · 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 Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T06:31:26.058928Z

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.

source=arxiv_source observed=2026-05-12T04:13:37.421188Z digest=sha256:8741e2e3d6185c2b843138b6fdd78856f231bc7a9ca46caaaf156e66271a5cdf

Observation 6e8ab8c2-9295-40f4-92d6-5fdf1694495b · inbound

MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems cites this paper.

MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:51:27.186936Z

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.

source=pdf_text observed=2026-05-12T03:57:11.504679Z digest=sha256:5d38a2fae28ddd81067d5ca0da688dbb85aacc81b6bada59bffa274c900a1510

Observation dd78ddac-4b26-41a0-95fc-b67e9de6a64c · inbound

Teaching Vision-Language-Action Models What to See and Where to Look cites this paper.

Teaching Vision-Language-Action Models What to See and Where to Look Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T16:48:39.573070Z

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.

source=pdf_text observed=2026-07-03T16:42:13.520913Z digest=sha256:ba87a09474b96dda737ecc361af417c6755ed1e0403caaeb69661e7c13e9a752

Observation 4b5ee0db-5871-4621-a20e-b5c875ea192e · 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 Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T00:13:49.297690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:13:49.297690Z digest=sha256:a83d052699479ff57d6626a1c15c98d4d49e6d2ed87aff27fed3fdfc4cd7be63