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Paper Citation Record · LEDGER

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy

As of 7 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 4 inbound Pith citation observations for arXiv:2505.19516.

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

pith.paper-citation-record.v1
2505.19516 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:18:09.900947Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:57:18.685055Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T05:27:40.170228Z

Reference resolution

62 of 62 outbound references displayed

  • verified exact0
  • verified fuzzy14
  • unresolved47
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd6f2cf4-3554-46e0-b597-ec9869e3aea2 · outbound

This paper cites NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

Reference 1

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source=pdf_text observed=2026-08-07T14:18:03.116564Z digest=sha256:c93e61c7c3d02afd953df4f518bd8c133d604c8dc2c5ef0cd6c0fc407e53ffbc

Observation 60d0fefc-d7a7-4d21-a2b8-a1e55114d7e6 · outbound

This paper cites Learning from all vehicles.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Learning from all vehicles

Reference 2

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:03.243238Z digest=sha256:4a2d9d66ab47f0847ab6f1ae3d055a75ef2601f527073c934413ad0b1288b899

Observation 46dbe300-fd86-497f-aa37-ecfda224403e · outbound

This paper cites Learning by cheating.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Learning by cheating

Reference 3

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:03.397050Z digest=sha256:78ff79da9d6f3949b0a999fad84dba088c6d3d19041a780a021547dbf15680dd

Observation 4b4b7ef8-4638-4a7c-bd2f-284ee1f207cb · outbound

This paper cites Learning to drive from a world on rails.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Learning to drive from a world on rails

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:03.516283Z digest=sha256:32dd0dc9805888fefcbafff3d78ce3a0132a65a321f1b1c00d1e2eabdb68ccc5

Observation 0ebc8c4f-3ba6-4e5f-9f85-82a86a2fa44c · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy End-to-end autonomous driving: Challenges and frontiers.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 5

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source=pdf_text observed=2026-08-07T14:18:03.638785Z digest=sha256:ca88ae372c8f8605b1b49efd1e020b1999f346a6e7d5eff46a2f61f8fd439758

Observation 0420f948-c049-4de4-871e-228b085ccde4 · outbound

This paper cites VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

Reference 6

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source=pdf_text observed=2026-08-07T14:18:03.759944Z digest=sha256:6aac8e7e97b3aaf87e2ea0a275c3a3b3a55aab1d61c4081475b0e14bfa51f8f6

Observation b5160dc3-5b89-4be3-bff4-77605227f55e · outbound

This paper cites Diffusion policy: Visuomotor policy learning via action diffusion.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Diffusion policy: Visuomotor policy learning via action diffusion

Reference 7

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source=pdf_text observed=2026-08-07T14:18:03.881179Z digest=sha256:b41b35818308ef364f3f4b9fb66a39aee88bfab782111a8b7ca1253492e1e6c6

Observation c6126aea-6fc7-48d8-85e0-0835adb600c1 · outbound

This paper cites Neat: Neural attention fields for end- to-end autonomous driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Neat: Neural attention fields for end- to-end autonomous driving

Reference 8

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source=pdf_text observed=2026-08-07T14:18:03.960018Z digest=sha256:2f0bd1e5576d7081af6d931e0401996a5e42652e523a763cfb5f5f26ecf4af30

Observation b7f104f3-24a9-4bf1-bfd8-a7a3a37523bc · outbound

This paper cites Transfuser: Imitation with transformer-based sensor fusion for autonomous driving.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11):12878–12895, 2022.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Transfuser: Imitation with transformer-based sensor fusion for autonomous driving.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11):12878–12895, 2022

Reference 9

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source=pdf_text observed=2026-08-07T14:18:04.041251Z digest=sha256:92a9b1b3c032881edf787b221414de8fb2632a49ecc5339e6a5b61b91c52e6ec

Observation f98f4029-5574-412f-8d28-a150fef2e9f0 · outbound

This paper cites Exploring the limitations of behavior cloning for autonomous driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Exploring the limitations of behavior cloning for autonomous driving

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:04.176004Z digest=sha256:c5afaa10213710976ccb54b4a096e417aff4ba003796279cb47e122b640adf66

Observation a7941722-6c0f-4ebf-94af-79bd2705da19 · outbound

This paper cites Openscene: The largest up-to-date 3d occupancy prediction bench- mark in autonomous driving.https://github.com/OpenDriveLab/OpenScene, 2023.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Openscene: The largest up-to-date 3d occupancy prediction bench- mark in autonomous driving.https://github.com/OpenDriveLab/OpenScene, 2023

Reference 11

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source=pdf_text observed=2026-08-07T14:18:04.300055Z digest=sha256:af88921d6412104cc8d3930f00bee40098b217b71baf4f24dccfbb5902ad74e1

Observation 50f763c0-4372-4c05-bbe5-7caeb302816f · outbound

This paper cites Navsim: Data-driven non- reactive autonomous vehicle simulation and benchmarking.Advances in Neural Information Processing Systems, 37:28706–28719, 2024.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Navsim: Data-driven non- reactive autonomous vehicle simulation and benchmarking.Advances in Neural Information Processing Systems, 37:28706–28719, 2024

Reference 12

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source=pdf_text observed=2026-08-07T14:18:04.403708Z digest=sha256:a68b975e53977cd94a9d5943c1fb788b555d9dea06fdaf57a1007759f8b6f3c6

Observation b99b5fe2-05b3-41ef-a4c0-f9db2483ac8f · outbound

This paper cites Diffusion models beat gans on image synthesis.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Diffusion models beat gans on image synthesis

Reference 13

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source=pdf_text observed=2026-08-07T14:18:04.507651Z digest=sha256:3f8bdb120fd29b9f56746351a02409c7960ef1e97810d5eb58be2fdfc7d12039

Observation 6b165776-8548-403a-b22e-002c13eb0f79 · outbound

This paper cites Carla: An open urban driving simulator.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Carla: An open urban driving simulator

Reference 14

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raw_fallback, observed 2026-08-07T14:18:12.343133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:04.598948Z digest=sha256:f95fc160d89e3dde3df1d82bbbd9914aa831def95b1d1cbbd39e25223c4b2b43

Observation 6846fa1f-2434-47da-b221-80987397d050 · outbound

This paper cites One Step Diffusion via Shortcut Models.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy One Step Diffusion via Shortcut Models

Reference 15

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source=pdf_text observed=2026-08-07T14:18:04.769058Z digest=sha256:f2bd49af0b43826691606542954abcaca0d51040e75f2a72fe7d024dba3c3ed0

Observation 5b30a053-5522-4375-ad23-e5855740a0ee · outbound

This paper cites Deep residual learning for image recognition.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Deep residual learning for image recognition

Reference 16

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source=pdf_text observed=2026-08-07T14:18:04.876073Z digest=sha256:3affccdbde44815063d4b4a930a7d3b6ef5e773a8d22bcd094d71c33d0bae05d

Observation 3ebd06db-f4bb-4b28-9712-1daee226a844 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 17

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source=pdf_text observed=2026-08-07T14:18:04.984388Z digest=sha256:13d8cf6d214202335cdd1c55555abd2c7aa7c25014355d06668b5436a261e5ff

Observation d47cfbd7-991a-4a40-9e42-17cc6373f374 · outbound

This paper cites Model-based imitation learning for urban driving.Advances in Neural Information Processing Systems, 35:20703–20716, 2022.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Model-based imitation learning for urban driving.Advances in Neural Information Processing Systems, 35:20703–20716, 2022

Reference 18

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source=pdf_text observed=2026-08-07T14:18:05.128441Z digest=sha256:a2b5ef5373fe968432df06206d778559bac3222b2f666b654110db97202f6c14

Observation da61671b-efa3-478e-b7a5-3a7c7716304a · outbound

This paper cites St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning

Reference 19

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source=pdf_text observed=2026-08-07T14:18:05.243384Z digest=sha256:8f555f1879bc9d3bb1905177fe289c2c3d8ec5009d51d012fc60c74a80f92fd6

Observation f27e05f6-afb7-4ca0-8b9f-8563a5635294 · outbound

This paper cites Planning-oriented autonomous driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Planning-oriented autonomous driving

Reference 20

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source=pdf_text observed=2026-08-07T14:18:05.324589Z digest=sha256:e69e635d7c2f6af77c83b122ab7a176c456d1aebdac438ba5ecbedfa2997133e

Observation 86f55d5b-da1c-4567-8ded-0ea848c84357 · outbound

This paper cites Versatile behavior diffusion for generalized traffic agent simulation.arXiv preprint arXiv:2404.02524, 2024.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Versatile behavior diffusion for generalized traffic agent simulation.arXiv preprint arXiv:2404.02524, 2024

Reference 21

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source=pdf_text observed=2026-08-07T14:18:05.437102Z digest=sha256:ab9a8b932648164601c5dd800f6c52a79fb3f16e98217ecc901deb915f446451

Observation 06bf13b6-7217-4bf2-bb36-59c4fd7bfb1f · outbound

This paper cites Hidden biases of end-to-end driving models.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Hidden biases of end-to-end driving models

Reference 22

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raw_fallback, observed 2026-08-07T14:18:12.116931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:05.577357Z digest=sha256:2a904cb7ffdb2d8fb96f8c196f90adf2d89df199a45f54bb8e537a84c0a0419c

Observation d337a85b-70b0-46af-8709-0c2a5f94ceb8 · outbound

This paper cites Planning with Diffusion for Flexible Behavior Synthesis.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Planning with Diffusion for Flexible Behavior Synthesis

Reference 23

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source=pdf_text observed=2026-08-07T14:18:05.684610Z digest=sha256:f70b95afb719bf23fb4b8b867322d377e58efbba138dc8e45feb6154c49ba936

Observation a845ca4a-07e2-4662-b820-f4b5c46b3357 · outbound

This paper cites Driveadapter: Breaking the coupling barrier of perception and planning in end-to-end au- tonomous driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Driveadapter: Breaking the coupling barrier of perception and planning in end-to-end au- tonomous driving

Reference 24

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:05.791702Z digest=sha256:155c3c57a759c747336f641395123dfaa7921286c177e556f2eda4c125c26b3a

Observation e748a58c-9697-496e-aaab-a963d2754bb2 · outbound

This paper cites Think twice before driving: Towards scalable decoders for end-to-end autonomous driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Think twice before driving: Towards scalable decoders for end-to-end autonomous driving

Reference 25

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:05.960541Z digest=sha256:b5f26b1264ced759f806185e61678566af2c25c0edbc2322aac9312289fffa6d

Observation 8bc21e03-8ddd-4944-91a8-99d6724b2315 · outbound

This paper cites Vad: Vectorized scene representation for efficient autonomous driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Vad: Vectorized scene representation for efficient autonomous driving

Reference 26

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source=pdf_text observed=2026-08-07T14:18:06.122169Z digest=sha256:79ea0fc5c592fd55e77033782d10bc4170c203ff6bb08a167bbd801fbcee5371

Observation 1e9084c3-13ba-4df4-9e92-2dedd4e7eac4 · outbound

This paper cites Motiondiffuser: Controllable multi-agent motion prediction using diffusion.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Motiondiffuser: Controllable multi-agent motion prediction using diffusion

Reference 27

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source=pdf_text observed=2026-08-07T14:18:06.240992Z digest=sha256:6d550343357b597758f0582275d42b14847e0e6c1c9b6f375fb0aa86669aa5b7

Observation 23bc8f61-a88c-4809-9ecd-b91d5d3b78f9 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Elucidating the design space of diffusion-based generative models

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:06.406865Z digest=sha256:06c398d64846936f18e35912aec4ce2d9844b2c59e60556b62b1ab5375ca508a

Observation 459ba859-1d56-4576-a7c2-35492c1046ae · outbound

This paper cites An energy and gpu-computation efficient backbone network for real-time object detection.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy An energy and gpu-computation efficient backbone network for real-time object detection

Reference 29

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raw_fallback, observed 2026-08-07T14:18:11.514731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:06.569256Z digest=sha256:3a171366f54057f4169a9dc7b41c2182c5371c30fcd21f997b709bd9609f075b

Observation aec3edde-efa1-441b-97a9-d50074fbefea · outbound

This paper cites Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation

Reference 30

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source=pdf_text observed=2026-08-07T14:18:06.722198Z digest=sha256:64aaf7d33c9d86da4cec6d102236f7d449ab96b30c5babf357eed717a264492a

Observation 0c66791a-3f1f-4f73-9bac-57d0a985b36b · outbound

This paper cites Enhancing End-to-End Autonomous Driving with Latent World Model.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Enhancing End-to-End Autonomous Driving with Latent World Model

Reference 31

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source=pdf_text observed=2026-08-07T14:18:06.820904Z digest=sha256:cc313c0d5c9f3a2ee2c0b998afea9c6244c52550ce121270526d44ed123c066c

Observation 1c21bfd3-d29f-44c1-a041-538317ffec4d · outbound

This paper cites Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation

Reference 32

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source=pdf_text observed=2026-08-07T14:18:06.892855Z digest=sha256:a0fdf48b1af339a6d184995f220856594dc00dc5afbadf8697298dc46d967efd

Observation 76875995-66d3-450d-8a24-89d3aa06fd44 · outbound

This paper cites DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving

Reference 33

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source=pdf_text observed=2026-08-07T14:18:06.989885Z digest=sha256:b1bc23b09ed9c0ba498f6514101e3bedbc879a091124583c70da30ad1901e2ef

Observation 129ed9b3-1b09-4d05-8973-b29c8e2879c0 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 34

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source=pdf_text observed=2026-08-07T14:18:07.071689Z digest=sha256:4b2a3cf7486e23faaadb42872529131620a1343f46354e567d47e32e1e6c7fab

Observation d3b1be1a-5c34-421e-a45e-d6e03d108cb1 · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in Neural Information Processing Systems, 35:5775–5787, 2022.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in Neural Information Processing Systems, 35:5775–5787, 2022

Reference 35

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source=pdf_text observed=2026-08-07T14:18:07.163245Z digest=sha256:671e9301ca6329c084f9b017cfb2c9d7521f13a5c16bd1f7b356df4f7997eed2

Observation bf638e46-25e4-400a-adce-0603ae39c133 · outbound

This paper cites On distillation of guided diffusion models.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy On distillation of guided diffusion models

Reference 36

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source=pdf_text observed=2026-08-07T14:18:07.262216Z digest=sha256:c41b396fff8345bae6d907f5ff81d09f044a33d13753f343668da115ac173832

Observation f9a79089-d654-4e30-ae57-4cb86c0a9b1e · outbound

This paper cites Multi-modal fusion transformer for end-to-end autonomous driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Multi-modal fusion transformer for end-to-end autonomous driving

Reference 37

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source=pdf_text observed=2026-08-07T14:18:07.356729Z digest=sha256:a877ec7c30c9d28562633aa86de2daaec9d41bf33efa3cafaa30b79b97e4e883

Observation 7505bb5f-b720-4b8c-8440-9753a1bf5cfd · outbound

This paper cites Design- ing network design spaces.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Design- ing network design spaces

Reference 38

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source=pdf_text observed=2026-08-07T14:18:07.459649Z digest=sha256:159615a510d6e209c52ca1423ce5940b9585146f428223d1b7808a2ef83c40f4

Observation 01a1791b-5981-4539-b69d-f05ca1ba0a09 · outbound

This paper cites PlanT: Explainable Planning Transformers via Object-Level Representations.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy PlanT: Explainable Planning Transformers via Object-Level Representations

Reference 39

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source=pdf_text observed=2026-08-07T14:18:07.550817Z digest=sha256:7521610fb276a2df288bdf2a6d0b0087a1b91b3fba66b31b164aecee0e1a086e

Observation f7b8565d-9c0d-4485-a025-8c4b6295fc45 · outbound

This paper cites Motionlm: Multi-agent motion forecasting as language modeling.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Motionlm: Multi-agent motion forecasting as language modeling

Reference 40

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source=pdf_text observed=2026-08-07T14:18:07.645208Z digest=sha256:08436625d9d466bbb2790dfb1256bfd3d1c56c0e8c054b1dbca59eb73a4b123c

Observation a542222c-447f-41c7-b09b-307c15f5d3b9 · outbound

This paper cites Safety-enhanced au- tonomous driving using interpretable sensor fusion transformer.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Safety-enhanced au- tonomous driving using interpretable sensor fusion transformer

Reference 41

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:18:07.717557Z digest=sha256:70d40d08f753762e436bd862ccd5bae9d93e070d81eae87073dff6aa2a249c7e

Observation e637e5ff-8e85-4101-bb07-b8d368ffb500 · outbound

This paper cites Reasonnet: End-to-end driving with temporal and global reasoning.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Reasonnet: End-to-end driving with temporal and global reasoning

Reference 42

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source=pdf_text observed=2026-08-07T14:18:07.800047Z digest=sha256:c599d63f04a66211d712852fc0f1ef30e74049c2de251eff336a9317948934a4

Observation f00183ec-5f85-4209-b40f-92b64c3b4dfb · outbound

This paper cites Denoising Diffusion Implicit Models.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Denoising Diffusion Implicit Models

Reference 43

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source=pdf_text observed=2026-08-07T14:18:07.894980Z digest=sha256:21b2f35ea2e9f055f9fb103018d99548092055d353a35aaac4a004eea1081b0e

Observation 9d98de5e-76b8-44a4-a12e-78ed9e289556 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Score-Based Generative Modeling through Stochastic Differential Equations

Reference 44

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source=pdf_text observed=2026-08-07T14:18:07.982861Z digest=sha256:a2cc8ee8f78621f4c4a8abe0487766951608a2a325c5e3610f167907a7768445

Observation 6d3262e1-4e2f-4093-8456-4e2072106474 · outbound

This paper cites Consistency models.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Consistency models

Reference 45

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source=pdf_text observed=2026-08-07T14:18:08.062984Z digest=sha256:0a38da79261efd218088c5f654eac86788eb577dcb73b628513e4c332d61a6df

Observation c7692184-235a-4030-881b-27d9d5b39e86 · outbound

This paper cites SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

Reference 46

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source=pdf_text observed=2026-08-07T14:18:08.137046Z digest=sha256:52402a2705cf1c9a740b4340ddda37fd4aed5416da818046426d83d14aa19d05

Observation 9836df7b-49d7-40f5-8166-2478ce3933d9 · outbound

This paper cites A survey of end-to-end driving: Architectures and training methods.IEEE Transactions on Neural Networks and Learning Systems, 33(4):1364–1384, 2020.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy A survey of end-to-end driving: Architectures and training methods.IEEE Transactions on Neural Networks and Learning Systems, 33(4):1364–1384, 2020

Reference 47

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source=pdf_text observed=2026-08-07T14:18:08.230405Z digest=sha256:a51aee509deb1b0be2322a710cef8a97dbbca4abd64f82d405ce850f66e47cd9

Observation 2346e78d-4a2c-4c6d-a272-69841de924e5 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 48

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source=pdf_text observed=2026-08-07T14:18:08.321744Z digest=sha256:457df33c8a29400b8dd7acfead0c62fb547d8a1ba845d00dd07c33d02b120bad

Observation 3a6965e7-dc09-4864-aee4-bde095b1cc67 · outbound

This paper cites He-drive: Human-like end-to-end driving with vision language models.arXiv preprint arXiv:2410.05051, 2024.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy He-drive: Human-like end-to-end driving with vision language models.arXiv preprint arXiv:2410.05051, 2024

Reference 49

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source=pdf_text observed=2026-08-07T14:18:08.415792Z digest=sha256:1321157517ce38101f70c680d8f3a64c3ee96712c16ef518ec6f1a14fad8f023

Observation 8a65c3f9-4b41-49e1-bbc3-10cf8e61d931 · outbound

This paper cites Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving.arXiv preprint arXiv:2312.09245, 2023.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving.arXiv preprint arXiv:2312.09245, 2023

Reference 50

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source=pdf_text observed=2026-08-07T14:18:08.512356Z digest=sha256:a53e21815163ff745498f3068b1d0b8821b46dff61ac69af97ecf8bf814e22d3

Observation 82e408b9-d528-4dc0-a2a7-2fe2785b5ee8 · outbound

This paper cites Para-drive: Par- allelized architecture for real-time autonomous driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Para-drive: Par- allelized architecture for real-time autonomous driving

Reference 51

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source=pdf_text observed=2026-08-07T14:18:08.637355Z digest=sha256:0edd69d7f087339dad1e3ad3187df0e55581958036369588d0286e1032280dad

Observation 4ea61bcb-076a-449d-8dce-064b1ea27939 · outbound

This paper cites Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline.Advances in Neural Information Processing Systems, 35:6119–6132, 2022.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline.Advances in Neural Information Processing Systems, 35:6119–6132, 2022

Reference 52

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source=pdf_text observed=2026-08-07T14:18:08.730966Z digest=sha256:109da40846e821061eb062d2c2bd2f4b9760e5d761de91b4afd65ea3ab5ba565

Observation e0c84262-4e49-4060-b7dc-390de749b3b2 · outbound

This paper cites Goalflow: Goal-driven flow matching for multimodal trajectories generation in end-to-end autonomous driving.arXiv preprint arXiv:2503.05689, 2025.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Goalflow: Goal-driven flow matching for multimodal trajectories generation in end-to-end autonomous driving.arXiv preprint arXiv:2503.05689, 2025

Reference 53

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source=pdf_text observed=2026-08-07T14:18:08.798824Z digest=sha256:2836fc55a841841364ede70c3f55a8a2ec20685a801b2f5eb343d84173474a93

Observation beb2b229-b3ee-45fa-8424-7793f474ae69 · outbound

This paper cites Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following

Reference 54

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source=pdf_text observed=2026-08-07T14:18:08.885878Z digest=sha256:06e077f9a19294340ec43fd55cc6a3f85fa65e50dd5f206abed156192328cec0

Observation 9e6ccfbe-2bcb-426f-92c1-3122868c121b · outbound

This paper cites DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba

Reference 55

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source=pdf_text observed=2026-08-07T14:18:09.004357Z digest=sha256:a39c151af7ae03b8e7152977751fdcc99f4f008e8b2e32b893518502b4fcd623

Observation 42651330-c6b1-403b-a851-72d20a1f50e0 · outbound

This paper cites 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations

Reference 56

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source=pdf_text observed=2026-08-07T14:18:09.114586Z digest=sha256:0d5b917359bac97ee6d09bb1f2e76f1ce00f2492ec15feed671f20b5838c9bdf

Observation 76b5a46c-d10b-4d6a-bb52-29e29ec64b5b · outbound

This paper cites Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

Reference 57

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source=pdf_text observed=2026-08-07T14:18:09.271971Z digest=sha256:029b990197855f65f2edfdf1c0e4b80abd1980839845f6421952b1cf7e02967c

Observation 2fbe302c-9469-4b1d-a88c-573487fd9587 · outbound

This paper cites Scaling vision transform- ers.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Scaling vision transform- ers

Reference 58

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source=pdf_text observed=2026-08-07T14:18:09.485180Z digest=sha256:2b6e4a2ad434d2bb4c167082d320681985eccc1cc70b768b32dca289c82a8e6c

Observation c8c3af77-3094-421e-8011-bde605aa8da3 · outbound

This paper cites End-to-end urban driving by imitating a reinforcement learning coach.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy End-to-end urban driving by imitating a reinforcement learning coach

Reference 59

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source=pdf_text observed=2026-08-07T14:18:09.611130Z digest=sha256:4009da343a04a7edf017a665fc20cdae141749d155e9130d65548e8e510ede53

Observation f489fab0-eac0-49dd-bf5a-f0b04f8270c1 · outbound

This paper cites Diffusion-Based Planning for Autonomous Driving with Flexible Guidance.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Diffusion-Based Planning for Autonomous Driving with Flexible Guidance

Reference 60

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source=pdf_text observed=2026-08-07T14:18:09.718177Z digest=sha256:7b8347e7c97d3daa58bf459178b99b2f9c6554810859dad0fd425577fbddc147

Observation 618c39a2-c9a9-4ce4-a3f0-0f50ac593820 · outbound

This paper cites Hidden Biases of End-to-End Driving Datasets.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy Hidden Biases of End-to-End Driving Datasets

Reference 61

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source=pdf_text observed=2026-08-07T14:18:09.805201Z digest=sha256:b12ac497d713afaf2052f973093937ffc7e7e0bc6924006e555f276b14f49020

Observation d544bbd0-1c06-40f1-bbf1-56f52d3b9bec · outbound

This paper cites This enormous difference reveals that directly modeling the trajectory space is more effective than the noise space for tasks requiring high precision, such as autonomous driving.

DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy This enormous difference reveals that directly modeling the trajectory space is more effective than the noise space for tasks requiring high precision, such as autonomous driving

Reference 62

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raw_fallback, observed 2026-08-07T14:18:10.778486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T14:18:09.900947Z digest=sha256:15de6f84108ad1a634a2c30a2618a2b47768b93ed8486a356fffa21e7d029ca7

Pith citing papers

Observation 633b46db-50af-4c5e-8f54-4bd4b1aa5880 · inbound

PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving cites this paper.

PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy

Reference 60

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arxiv_id, observed 2026-05-19T02:57:00.678719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T02:53:09.830659Z digest=sha256:274c09b3c2522e3c84496521b68ddc1da1d5e8abf57491bfcdbcbbf9bdfc5259

Observation 2152d850-e361-47e8-9602-165697cd083b · inbound

OmniNWM: Omniscient Driving Navigation World Models cites this paper.

OmniNWM: Omniscient Driving Navigation World Models DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy

Reference 110

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source=pdf_text observed=2026-08-04T08:57:18.685055Z digest=sha256:98b8309d538bcf0275158f624bf690d993c0e9536d4f960be12586a9d0d2e4cd

Observation a56ba320-a479-463c-9124-15560cd19214 · inbound

ProDrive: Proactive Planning for Autonomous Driving via Ego-Environment Co-Evolution cites this paper.

ProDrive: Proactive Planning for Autonomous Driving via Ego-Environment Co-Evolution DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy

Reference 39

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arxiv_id, observed 2026-05-11T23:56:14.004178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-07T15:58:59.744568Z digest=sha256:56bffb4314f5b9c7148b03a5b4270a2db6f3ed7f2cd7f988f0c69e068db93534

Observation 684ed2b1-8234-4d86-a0f5-f8b469e79f81 · inbound

Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving cites this paper.

Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy

Reference 41

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arxiv_id, observed 2026-07-03T05:27:40.171854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T13:14:28.378548Z digest=sha256:de2aaca5a0361b40f976529a29685acd4bf1abf859f85d482966b377797235e1