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Paper Citation Record · LEDGER

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

As of 20 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 2026-08-20T06:33:59.587034+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=sha256:2148464e6a4d411391d82cbce7cc6c1cf04262951c3584763d6e15e2e41a055d

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:03.243238Z digest=sha256:e275fb48c0fba224326e9060fba281228340300bdf2d4a09cfb4d18854133cb9

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:03.397050Z digest=sha256:983cd7a815a24afc4268c7be728236c890b760fa8eb48d35cadd07d9ed0052e5

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:03.516283Z digest=sha256:405409d62a44a3be95198c034ea07ccb77aa74b7dee70194e962baf7c2f12f48

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=sha256:91f403fdf9f692c10ef65cc22a4ef33d60bc8e43fcc3f93d41f5acec4c681428

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:dce24af97ae52dd5d431b7e5f4ccf9ec164afbd8ca4ff61b1dbba7381dbba8e6

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:d12a695aec10cc08cffb217159c7dde4e974e208545df160274f72629a83417c

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:d34e81f267fc5707462d138d37491cd0ec1a01507a0b66f5e1bef19fc03cb9ca

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:dc799f039e8c6ee94b7bb4a8d646e748b4f989992974f829b637fb896630ebcd

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:04.176004Z digest=sha256:618c712815e680adc3151ba9f1d144943a160201a5b761a6e6fb2a3906f6e1bb

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:f63ab5fc4f9bb2aa01a7347cc0b2ba085e54aaa2f89a1bb9e7d3fad650a657d4

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:a4098ecd47751078bdf46d2ad5b80910cf7b3ddfee884e3b594cc5eb63460c47

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:c8617689a35a42bd01cdf6a6da114d6660f35462937e754923b93b13c9d14087

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:04.598948Z digest=sha256:2677e350599d9c3925ac3b58b27cbc2e6d3118e30f25dffa191d95f2d9997354

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=sha256:66b45492dee8f203a7998acc956cafa132548ca9730b0b3175cf7842edb8e716

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=sha256:4cde8bff15d9abda28c9249d32afad92e29a3eb210b56523c5e7f4bb1688d0dd

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:a509451f02d7ec2a7121966c708aceaa29d830f483d2a30872edc427ecd4e04a

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=sha256:7c1541d68426a1a7e11b6958881cc087b4dfb8dfe1ff658d477c53b305b5685e

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=sha256:5a39be7f853877e96e4e0b6901b223d25fec4d7817f2b98d49ca1bd5c8e2fab2

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=sha256:24b938ef70f1cff156c1f41ecb32648ffa6901757da03720842d08a5b7b7782b

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=sha256:994fe956c0ee08a8f895aee416d236d5c6fb845ff6dfcfeddafd010753b8cd2d

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:05.577357Z digest=sha256:e7da36745d1cd286bd7bf60c70aae64d19a9cf7f15837d2f4eb4b92397ffbf6b

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:a437528378db4920eee82a33534ffa45be275dd1d6802b12e2b7c0688e55b149

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:05.791702Z digest=sha256:28e1c39a2c361d2885fb269bfe8dac713bf6001bb7d88e5c8672bcc80dae1250

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:05.960541Z digest=sha256:efbb2e944853d99c3d619617bd9f1ef00bcae76d6992aa79fbffdd6eee31c54c

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=sha256:7f5e47bdc0527dc8c7f998dfcf0e7383b2fb22e54e9ae5c2594881a98e30cf8c

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=sha256:679c10541b0bdda47301073d901ad723870109e460cb19c5e045fe627c05cb43

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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raw_fallback, observed 2026-08-07T14:18:11.630147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:06.406865Z digest=sha256:68d7a82587573b718ae3ae6cb2cd0b82c712dfc99d4eca897fc48e8d830761c3

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:06.569256Z digest=sha256:432901a58f1790a0706121a4aa1fa38d01d8c761717681372db4f96a065a18e3

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:cbad39af5eef797f0921ca160a8c67f406f6290a9411c8545d9326198fcea502

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=sha256:73a5bba18d13b602ec067225ad2bdca3a6e0cd9ba664be3a679d45f6be9a75ab

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:c469242843de8b69146f0d8924eab3a692e62c0be64e190c6e36c1b0ecac4cce

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=sha256:7eb934134585cf7b348c3586fbe03a35710d458e1bb4ca186c47ded17b5b2739

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=sha256:747a80dbc4331a8d45153c3d7de8ca5311a33e0ae44b3927532b771db2cea491

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=sha256:955973688571bebb3ba3a1926cbffd039a8fea1e0abf2eddb6dad2e2f5009764

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=sha256:95bc145f51c2a15444647db81db3a797f222f5b2a455b1e3b43880979bc30f71

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=sha256:28f8d88735914d2cec58838d138965bc90c9581cf51c198e0883a207bb075103

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-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:07.459649Z digest=sha256:f6c17273be1c908c7d3a31d086e81d5ea3756eb914eaae6e51412731ee38e6a4

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=sha256:842d798e497ca91afa86d7cefdf2fde834d6bf82809c09dd2954d231b8365f0d

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-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:07.645208Z digest=sha256:dddc554787af67ebc5779a939c398687843d4073e1f29e6da5df60203d24a9fb

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

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:07.717557Z digest=sha256:9e5b764ccd96cdd93df89bcf4c42e3d9a1320e7e26d075e8c0305d409e05d806

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=sha256:445b7d4a419944d44571b862ac7eee03d9429bd0acbd79678750278a9f9814ff

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=sha256:0e2d3ed4c2bf608751286e5a51f94154c59b25bd9ebb12c0147cfa565af7a02e

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=sha256:0b97dd43b201b54ac5a801a00dfbb3d0f4d023acd13606f235aa11193d6ff737

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:ce7e855be9c229326c2db4938e1e2b29cde217dc6f3c1dfc3cfca3b297f223f1

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:d333de7dfde0574f6ebfe8b2d9a1b0a4ee1d60318aa91170d92d310a69015b77

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=sha256:34d3e28d30ca15baf90daef2f58e037093e34f5ee921cf14ec40268cda76f436

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:cb081167427bf33ff99a4b3f5e1c98948ee618d1e0288a69d57b3a57b527c685

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:d388422c8254da2a6fd6433cbf4d259c5479a764f693166696a4d92ad6ffed9b

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:cfd9fa6278c7edd188103e846e35ed40d98897a01e0d91cf7d79b1a14b3769cc

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:dfa29d605da3286a2e3686a0b3c84789fe211e11d9c8fe2d3e73c9ef08f1fe5c

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:afc4afbc42af85087ccf416a7bee7dfa4251078d016122df43249865d7fabf1c

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=sha256:24f0547e95bc9b61be8a2f8bb8e22771da16d1f9f4675d62eb38533d92f10b00

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:bd634e3fa826cf371bca6c907de84f3c24bfbb304e6427fcfadc0295726a2dbd

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=sha256:79792c5c69af90d3566f95ceeb9fdee207d6de0e048be132bb5127a7a3b11053

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:bdec3ed99a3ad458934bafa85ad290b79d635516b55a0404dcfaecfc5b014bc8

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=sha256:7b0a94d708b682a22dc5645590ff311d79196f31e5bd187014d3f010a24b78ad

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:e548241999bc6c4db4433598b2f5607d7051318c2b6a551260e781d4e8e839e7

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=sha256:5524ff7f2ad3f1427a164b71db305cf7de60f494d325c9fc9fdc3b0f06da2ace

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=sha256:349b0855fb8b89d4d98a40d8e977ea60c81e25ac0129ee0bfb2b3e15947b16ce

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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no resolver link, observed 2026-08-07T14:18:09.805201Z

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source=pdf_text observed=2026-08-07T14:18:09.805201Z digest=sha256:d1cbf89b5d65fb9ee136bf8301bf265c68bc9c8b24678703240f67025cf44139

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:18:09.900947Z digest=sha256:b4da59503be4c80e7fc0613c68a2c6b87f49964094493ade89a88e0faffd30cf

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-19T02:53:09.830659Z digest=sha256:edb98b0b2f59bc1c790c663f1b6f4b8a35861242e522f3738ad4119fa5f9a585

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=sha256:404659dbb39e46bb31535c17e5ae822ff6ad1a3383cca839c21e657b2e6f2368

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-07T15:58:59.744568Z digest=sha256:c5488ac00b0d14c59431f7e0f333b8cb6d419526f9ec40e461d058a7fb2469e0

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-27T13:14:28.378548Z digest=sha256:e815287f33f4d8b50f2d7a66a222882499d6551825df87f537792a12a60a4116