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Paper Citation Record · LEDGER

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving

As of 16 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.10336.

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

pith.paper-citation-record.v1
2607.10336 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T12:33:06.583512Z

measured 29 of 29 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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Outbound references

Observation de9bf080-629c-44d1-80e4-a2ec149473d2 · outbound

This paper cites Planning-oriented autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Planning-oriented autonomous driving,

Reference 1

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Observation a6d9979a-9c50-4d6c-825f-263ae24c910a · outbound

This paper cites Vad: Vectorized scene representa- tion for efficient autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Vad: Vectorized scene representa- tion for efficient autonomous driving,

Reference 2

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:4f27ae987a4a3231f2c2878c0be79d055029be4546eaf2c68e9182b4796a46ee

Observation f0231e6b-58ae-4868-bb65-d2b8dfc83469 · outbound

This paper cites Sparsedrive: End-to-end autonomous driving via sparse scene representation,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Sparsedrive: End-to-end autonomous driving via sparse scene representation,

Reference 3

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Observation 629dd0c2-0a39-4827-ba02-77d88c2199a9 · outbound

This paper cites Genad: Generative end- to-end autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Genad: Generative end- to-end autonomous driving,

Reference 4

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:8e0f34113132fcd1566440f9dee7bc344087abd6f6cdb6cc52fd20968cbc97fc

Observation de6d88e8-cce2-4956-9193-a8291c402400 · outbound

This paper cites Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models

Reference 5

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Observation cd102d5e-0303-4d85-8a37-c6e5b32fb1d1 · outbound

This paper cites Don’t shake the wheel: Momentum- aware planning in end-to-end autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Don’t shake the wheel: Momentum- aware planning in end-to-end autonomous driving,

Reference 6

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Observation 32938d13-beb8-4320-afab-57f5b0013794 · outbound

This paper cites Bridging past and future: End-to-end autonomous driving with historical prediction and plan- ning,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Bridging past and future: End-to-end autonomous driving with historical prediction and plan- ning,

Reference 7

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:92df008bcb0e4102eac5cc14b809eae2ff673aaf0e78add2341eeb163de6f2ef

Observation bba021c3-1fc7-42e9-b588-c6b571dde260 · outbound

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

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Artemis: Autoregressive end-to-end trajectory planning with mixture of experts for autonomous driving,

Reference 8

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:bcbb19ccbcae7615d29c1baa2ba8f222d4736b8bc2eb53217fd8cd6c29dd5917

Observation 4f8e171c-d7c4-41c1-b275-648226943e8b · outbound

This paper cites Expertad: Enhancing autonomous driving systems with mixture of experts,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Expertad: Enhancing autonomous driving systems with mixture of experts,

Reference 9

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:83c8c967aff30ad566409d3ae37509b14dccb748490378a7522bdc886fb5ed11

Observation 327a2692-76c4-4840-b0f1-1d0f49f44eff · outbound

This paper cites DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving

Reference 10

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:3237c43f6def23c953aa403cbbdc1e79c88d3c14fb3bfefc54ec561593703996

Observation 2a7c178b-81d5-4143-a0eb-f738dc127ac6 · outbound

This paper cites Unimm- v2x: Moe-enhanced multi-level fusion for end-to-end cooperative autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Unimm- v2x: Moe-enhanced multi-level fusion for end-to-end cooperative autonomous driving,

Reference 11

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:ffda69922e5d6ccc55de1e6736d11f2f2711d5ebc68364e14a7e9261c3cf5605

Observation 22599757-a515-495f-b19e-2e28d4f6b05d · outbound

This paper cites Samoe-vla: A scene adaptive mixture-of-experts vision- language-action model for autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Samoe-vla: A scene adaptive mixture-of-experts vision- language-action model for autonomous driving,

Reference 12

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:98dad11c28d6a59e1b3f3fb34786b5426f0d24d41d274b7da3722382383a1b47

Observation 8a3de1e8-6537-46fc-88e1-4e01aad9507c · outbound

This paper cites Generalizing motion planners with mixture of experts for autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Generalizing motion planners with mixture of experts for autonomous driving,

Reference 13

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Observation 1c3d294d-0b66-4462-a32e-46d512b9c1db · outbound

This paper cites Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving,

Reference 14

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:4284e258711d87fd62f1a1472644895e2945428da4c78a1381e2fa70dce0ac96

Observation 2673c691-fb87-4900-9678-e2996c1d057e · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving nuscenes: A multimodal dataset for autonomous driving,

Reference 15

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Observation 7ac7bea6-287d-4bdb-9efb-d428c198ad15 · outbound

This paper cites Neuroncap: Photorealistic closed-loop safety testing for autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Neuroncap: Photorealistic closed-loop safety testing for autonomous driving,

Reference 16

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Observation 11ee86b6-d8f0-48b5-9dc0-5c9183f4faac · outbound

This paper cites Is ego status all you need for open-loop end-to-end autonomous driving?.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Is ego status all you need for open-loop end-to-end autonomous driving?

Reference 17

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Observation 16592dca-99df-4e30-9bed-d3850995833d · outbound

This paper cites Deep residual learning for image recognition,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Deep residual learning for image recognition,

Reference 18

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Observation 06e54ad0-7666-4503-a7f8-01111ae3bd5c · outbound

This paper cites Decoupled weight decay regularization,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Decoupled weight decay regularization,

Reference 19

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Observation bc487749-a7be-42c5-bb9c-3fd1d87551a2 · outbound

This paper cites Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving,

Reference 20

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Observation 9c080342-10a8-4bcf-9236-94f9e13a8ddf · outbound

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

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Enhancing End-to-End Autonomous Driving with Latent World Model

Reference 21

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Observation b4c7c0c4-3454-4d72-a74f-1d7e3812fa59 · outbound

This paper cites Occworld: Learning a 3d occupancy world model for autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Occworld: Learning a 3d occupancy world model for autonomous driving,

Reference 22

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Observation 993ee2cf-97cb-4ae8-8a6a-da862a76d8f0 · outbound

This paper cites Orion: A holistic end-to-end au- tonomous driving framework by vision-language instructed action generation,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Orion: A holistic end-to-end au- tonomous driving framework by vision-language instructed action generation,

Reference 23

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Observation 7300ce54-9b27-4c32-b97a-8e6cf0046393 · outbound

This paper cites Omnidrive: A holistic vision-language dataset for autonomous driving with counterfactual reasoning,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Omnidrive: A holistic vision-language dataset for autonomous driving with counterfactual reasoning,

Reference 24

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Observation 615cfd3d-7f59-41a4-8126-a47d098a3b1f · outbound

This paper cites Unidrivevla: Unifying understanding, perception, and action planning for autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Unidrivevla: Unifying understanding, perception, and action planning for autonomous driving,

Reference 25

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Observation 97bffe96-8119-46f8-9660-327b383fae97 · outbound

This paper cites Uncad: Towards safe end-to-end autonomous driving via online map uncertainty,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Uncad: Towards safe end-to-end autonomous driving via online map uncertainty,

Reference 26

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Observation 4921b26e-7a8c-4180-a212-5a09a68e2299 · outbound

This paper cites Causalvad: De-confounding end-to-end autonomous driving via causal interven- tion,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Causalvad: De-confounding end-to-end autonomous driving via causal interven- tion,

Reference 27

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source=pdf_text observed=2026-07-14T12:33:06.583512Z digest=sha256:c4f07319076ea455d300d8278f86f36c20f0ac1a2b5b49083f352eb2c0697d62

Observation 6c69f20f-1f6d-4973-9e85-41751445de22 · outbound

This paper cites Flowad: Ego-scene interactive modeling for autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Flowad: Ego-scene interactive modeling for autonomous driving,

Reference 28

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Observation ca9c240e-98ed-42e5-a680-bdf8daf3e363 · outbound

This paper cites Reasoning-vla: A fast and general vision- language-action reasoning model for autonomous driving,.

PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving Reasoning-vla: A fast and general vision- language-action reasoning model for autonomous driving,

Reference 29

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