Paper Citation Record · LEDGER

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment

As of 13 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2604.28111.

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

pith.paper-citation-record.v1
2604.28111 v3

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T16:47:09.487360Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

31 of 31 outbound references displayed

verified exact15
verified fuzzy16
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 67dcbeaf-419e-4451-8fa4-d8c0d9e0c03d · outbound

This paper cites End- to-end autonomous driving: Challenges and frontiers.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment End- to-end autonomous driving: Challenges and frontiers

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.556012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2fcefe42-735b-47b6-9b5f-25bf874f7579 · outbound

This paper cites The era of end-to-end autonomy: Transitioning from rule-based driving to large driving models.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment The era of end-to-end autonomy: Transitioning from rule-based driving to large driving models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:39.996825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation be7e3430-ca8d-46f9-b96d-7ab32ca0d3bd · outbound

This paper cites Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:39.964563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4091ec68-e420-404d-8c06-65cf4a2a43ca · outbound

This paper cites End-to-end driving with online trajectory evaluation via bev world model.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment End-to-end driving with online trajectory evaluation via bev world model

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.553850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 68df4f73-857d-48c9-b97b-dc8089f9b619 · outbound

This paper cites Centaur: Robust End-to-End Autonomous Driving with Test-Time Training.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Centaur: Robust End-to-End Autonomous Driving with Test-Time Training

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:39.961569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 692d11e9-838e-4a96-91d5-1b17a421ab36 · outbound

This paper cites Data scaling laws for end-to-end autonomous driving.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Data scaling laws for end-to-end autonomous driving

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.551601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 74d76c02-1451-4c17-b809-95f486fd424e · outbound

This paper cites Synad: Enhancing real-world end-to-end autonomous driving models through synthetic data integration.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Synad: Enhancing real-world end-to-end autonomous driving models through synthetic data integration

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.580940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cb5bdc08-b1a6-4a73-a143-236ae398b8e8 · outbound

This paper cites Diffe2e: Rethinking end-to-end driving with a hybrid diffusion-regression-classification policy.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Diffe2e: Rethinking end-to-end driving with a hybrid diffusion-regression-classification policy

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.583357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dd377ef5-62f2-4bbe-aef4-2f57addc8caf · outbound

This paper cites Distilldrive: End-to- end multi-mode autonomous driving distillation by isomorphic hetero- source planning model.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Distilldrive: End-to- end multi-mode autonomous driving distillation by isomorphic hetero- source planning model

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.586150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0ee84caf-9ed1-431b-a07e-43747ad59f37 · outbound

This paper cites Artemis: Autoregressive end-to-end trajectory planning with mixture of experts for autonomous driving.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Artemis: Autoregressive end-to-end trajectory planning with mixture of experts for autonomous driving

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.578854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f56cd369-2278-462b-91d2-91bcdae2310a · outbound

This paper cites Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-09T02:19:48.260809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 21fdace0-7817-42d1-964e-4af586ba4749 · outbound

This paper cites ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-19T16:47:39.984266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bea0acea-bc14-46cd-9d8e-eb58aa396901 · outbound

This paper cites Drivedpo: Policy learning via safety dpo for end-to-end autonomous driving.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Drivedpo: Policy learning via safety dpo for end-to-end autonomous driving

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:39.978727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a90dee56-f152-4c3e-ac4b-cdd1c9227d7d · outbound

This paper cites Takead: Preference-based post-optimization for end-to-end autonomous driving with expert takeover data.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Takead: Preference-based post-optimization for end-to-end autonomous driving with expert takeover data

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.576735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 074fa5cd-7019-456d-8d24-3fb1e26cef06 · outbound

This paper cites Rad: Training an end-to-end driving policy via large-scale 3dgs-based reinforcement learning.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Rad: Training an end-to-end driving policy via large-scale 3dgs-based reinforcement learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:39.993913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4ad210c9-151e-493c-a56c-62ce900be985 · outbound

This paper cites ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:39.973304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7bc4f183-a5f2-44ab-9094-6bf9b70850ab · outbound

This paper cites Drive&gen: Co-evaluating end-to-end driving and video generation models.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Drive&gen: Co-evaluating end-to-end driving and video generation models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.574501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b0fdb4a-9677-42db-b8ac-f9b1c1315ceb · outbound

This paper cites Futurex: Enhance end-to-end autonomous driving via latent chain-of-thought world model.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Futurex: Enhance end-to-end autonomous driving via latent chain-of-thought world model

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:39.958676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0f2915ed-c11b-4563-8fc4-8228d4fde791 · outbound

This paper cites Vggt: Visual geometry grounded transformer.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Vggt: Visual geometry grounded transformer

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.572267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e7f279ce-e04b-475b-83d3-b9e8cdf2c227 · outbound

This paper cites Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.566865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation af45a7cb-4ffb-4c82-87b5-720bb1b8b90c · outbound

This paper cites Deep residual learning for image recognition.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Deep residual learning for image recognition

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.564552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e2cb361a-9ad7-4feb-ae78-bddb56f1518a · outbound

This paper cites Classifier-Free Diffusion Guidance.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Classifier-Free Diffusion Guidance

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-19T16:47:39.970116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a9db548e-87a9-40d1-a5ae-34a15b0aed22 · outbound

This paper cites Optimal flow matching: Learning straight trajectories in just one step.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Optimal flow matching: Learning straight trajectories in just one step

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.560269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8f6ea94e-a0a4-4144-b559-0fb733974cd9 · outbound

This paper cites On unbalanced optimal transport: An analysis of sinkhorn algorithm.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment On unbalanced optimal transport: An analysis of sinkhorn algorithm

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.558088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 295159da-ed0e-4c0c-8075-b9b85866090b · outbound

This paper cites Proximal Policy Optimization Algorithms.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Proximal Policy Optimization Algorithms

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-19T16:47:39.967231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 21563eee-3c26-452a-90f7-8022bc61d5b6 · outbound

This paper cites Skill-critic: Refining learned skills for hierarchical rein- forcement learning.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Skill-critic: Refining learned skills for hierarchical rein- forcement learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.562387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 95e025e7-75ee-4b00-bb00-08d264798cde · outbound

This paper cites Reinforcement learning with inverse rewards for world model post-training.arXiv preprint arXiv:2509.23958, 2025a.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Reinforcement learning with inverse rewards for world model post-training.arXiv preprint arXiv:2509.23958, 2025a

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:39.976007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 23392d8f-cfb0-4f1c-9138-158ce4e8e6ba · outbound

This paper cites Reinforcement Learning with Action Chunking.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Reinforcement Learning with Action Chunking

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-19T16:47:39.986912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9b88851c-c213-4e1c-8dbd-9de3c983567d · outbound

This paper cites Denoising diffusion probabilistic models.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Denoising diffusion probabilistic models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T16:47:40.569235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ca1b4db5-8a7a-4dfb-bd24-aa3f0eaf056e · outbound

This paper cites Denoising Diffusion Implicit Models.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Denoising Diffusion Implicit Models

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-19T16:47:39.981262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 21548335-965c-45aa-958c-06983b1502e9 · outbound

This paper cites Flow Matching for Generative Modeling.

GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Flow Matching for Generative Modeling

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-19T16:47:39.999394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.