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Paper Citation Record · LEDGER

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving

As of 9 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2607.19774.

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

pith.paper-citation-record.v1
2607.19774 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T11:47:32.287574Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85198fe6-a4b2-4e4f-8aba-34309f5f84f6 · outbound

This paper cites V2vnet: Vehicle-to-vehicle com- munication for joint perception and prediction,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving V2vnet: Vehicle-to-vehicle com- munication for joint perception and prediction,

Reference 1

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source=pdf_text observed=2026-08-01T11:47:30.296670Z digest=sha256:b339ec3a7ad24d0f4956c050e9d658cca559ea3b39c64bd5d70813f133a17897

Observation db9e2d1e-ecac-4291-9141-cd16f88082bb · outbound

This paper cites V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,

Reference 2

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source=pdf_text observed=2026-08-01T11:47:30.410867Z digest=sha256:dec6dd2c4a1199cda9a0fc3f28703c3ca48ea5e7d09808c69ceb0bf20d3b86e7

Observation e2feea9e-b747-46d6-92ae-c3a0849bf848 · outbound

This paper cites V2xpnp: Vehicle-to-everything spatio-temporal fusion for multi- agent perception and prediction,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving V2xpnp: Vehicle-to-everything spatio-temporal fusion for multi- agent perception and prediction,

Reference 3

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source=pdf_text observed=2026-08-01T11:47:30.495838Z digest=sha256:79031840897cc961a5d035977a6bfa0e1f494f7d925bc4b59e8c2bd4a35fa325

Observation ecc02172-8e64-4a38-8c42-d98824950231 · outbound

This paper cites Towards collaborative autonomous driving: Simulation platform and end-to-end system,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Towards collaborative autonomous driving: Simulation platform and end-to-end system,

Reference 4

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source=pdf_text observed=2026-08-01T11:47:30.649362Z digest=sha256:bd29967aeabedad816685d0235477861e5b7caaeef110fc97e48f036ca683075

Observation 9bddc308-fcbe-47c3-937c-8e551ef2b23d · outbound

This paper cites Heatv2x: Scalable heterogeneous collaborative perception via efficient alignment and interaction,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Heatv2x: Scalable heterogeneous collaborative perception via efficient alignment and interaction,

Reference 5

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source=pdf_text observed=2026-08-01T11:47:30.677988Z digest=sha256:116d691a894971359c4b9df6808afe8208c91e0bf31282ded621602bcfee8892

Observation 8643b98b-b8b6-41bd-98a2-541aff454e14 · outbound

This paper cites Height3d: A roadside visual framework based on height prediction in real 3-d space,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Height3d: A roadside visual framework based on height prediction in real 3-d space,

Reference 6

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source=pdf_text observed=2026-08-01T11:47:30.765993Z digest=sha256:5a4861d7995b58659c12c748c66695b5f7cfa46f57bf7b6b266496f312cda6cc

Observation 72a2fb59-2f67-4340-85b8-56c1752ca945 · outbound

This paper cites Pillarid: Rethinking backbone network designs for pillar-based 3d object detection in infras- tructure point cloud,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Pillarid: Rethinking backbone network designs for pillar-based 3d object detection in infras- tructure point cloud,

Reference 7

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source=pdf_text observed=2026-08-01T11:47:30.928803Z digest=sha256:04f1c5d248b1bb868010b1e9ff781c1b2620996d5e7654b0778d741be697a434

Observation 193eb885-1002-4afa-a81c-a201267cb8e0 · outbound

This paper cites Planning- oriented autonomous driving,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Planning- oriented autonomous driving,

Reference 8

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source=pdf_text observed=2026-08-01T11:47:31.072656Z digest=sha256:9b222f1a29249081c625d0493bec32f4add2e3775210938c75c53c68a38f61c6

Observation 104b7d1f-928b-40e1-a159-83404109ff53 · outbound

This paper cites Multi-modal fu- sion transformer for end-to-end autonomous driving,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Multi-modal fu- sion transformer for end-to-end autonomous driving,

Reference 9

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source=pdf_text observed=2026-08-01T11:47:31.169802Z digest=sha256:fc2304c5634c009f2873f804ede62c8a932b03c3f3e9f13f265f84eafc5cc41e

Observation c1e49aed-d6ce-47ef-b3fe-f1c186ccd884 · outbound

This paper cites Trajectory-guided control prediction for end-to-end au- tonomous driving: A simple yet strong baseline,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Trajectory-guided control prediction for end-to-end au- tonomous driving: A simple yet strong baseline,

Reference 10

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source=pdf_text observed=2026-08-01T11:47:31.268465Z digest=sha256:4100f66c3d383baed22df7cb55862f7abfbecdbfd4734542369c1506f71d8159

Observation 1c651d1e-76a9-423a-b491-1ca8cfc69952 · outbound

This paper cites End-to-end autonomous driving through v2x cooperation,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving End-to-end autonomous driving through v2x cooperation,

Reference 11

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source=pdf_text observed=2026-08-01T11:47:31.365724Z digest=sha256:552ecd063ba571300c7ece907b2ff8d1bcca6725499c1b75357a3205e516d1ab

Observation 1e72b60d-2646-4218-9c56-762eb9d20262 · outbound

This paper cites HeightFormer: Learning Height Prediction in Voxel Features for Roadside Vision Centric 3D Object Detection via Transformer.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving HeightFormer: Learning Height Prediction in Voxel Features for Roadside Vision Centric 3D Object Detection via Transformer

Reference 12

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source=pdf_text observed=2026-08-01T11:47:31.457821Z digest=sha256:74006acc97bd002958982ae5e8c255ee88300b4a1a5eb0440d99d5543648e37d

Observation 773b9fe2-9013-4e7a-b5a8-2605a453127b · outbound

This paper cites PillarMamba: Learning Local-Global Context for Roadside Point Cloud via Hybrid State Space Model.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving PillarMamba: Learning Local-Global Context for Roadside Point Cloud via Hybrid State Space Model

Reference 13

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source=pdf_text observed=2026-08-01T11:47:31.524034Z digest=sha256:b58aba4c8d8a8c5fb537e6f1ae47faf7b644db8880588b9d6499c44ea97a804e

Observation be958fed-2e0a-47f1-95b4-638329857390 · outbound

This paper cites Roadformer: Local-global feature fusion for road surface classification in autonomous driving,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Roadformer: Local-global feature fusion for road surface classification in autonomous driving,

Reference 14

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source=pdf_text observed=2026-08-01T11:47:31.616948Z digest=sha256:07dbe56add2e90b3a878952459e75550e75c4e5a97a916ae2a1133d01b5dde2c

Observation 738f3cfd-61c0-4971-9415-ce1f66686d46 · outbound

This paper cites RoadMamba: A Dual Branch Visual State Space Model for Road Surface Classification.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving RoadMamba: A Dual Branch Visual State Space Model for Road Surface Classification

Reference 15

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source=pdf_text observed=2026-08-01T11:47:31.729464Z digest=sha256:0fdf5e2cef357637fff047d6329d6e2532173bd320612d3181f635d844cbb6ec

Observation 9a49e539-29dc-470c-8ee9-30169b010a83 · outbound

This paper cites Lanemapnet: Lane network recognization and hd map construction using curve region aware temporal bird’s-eye-view perception,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Lanemapnet: Lane network recognization and hd map construction using curve region aware temporal bird’s-eye-view perception,

Reference 16

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source=pdf_text observed=2026-08-01T11:47:31.784246Z digest=sha256:fd31e90c22bc02cfbb591bbb3f1f9549b7658f99c4ee674beebc2bc0d9957ee4

Observation b231a28a-bb1e-4227-b79d-88c4b6e0d8ea · outbound

This paper cites Cooper- naut: End-to-end driving with cooperative perception for networked vehicles,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Cooper- naut: End-to-end driving with cooperative perception for networked vehicles,

Reference 17

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Observation 338a3464-b419-48b5-ac5c-35edeef258aa · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 18

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Observation ede58a68-f958-4631-8b6c-e7340fcd70ee · outbound

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

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving

Reference 19

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Observation 2690886f-2bcf-4156-af81-c25ee6bbe038 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving LLaMA: Open and Efficient Foundation Language Models

Reference 20

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source=pdf_text observed=2026-08-01T11:47:32.029542Z digest=sha256:2b45d5d20470fb8aff6616eab579658e23b292d8bc75e6321e06ce70a0f7d127

Observation 062d585a-265b-4ad8-9e96-b0ef1bf56ac2 · outbound

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

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,

Reference 21

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Observation 3546e197-dfbe-48e2-9522-3d5d1d8f84fa · outbound

This paper cites Bevformer: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Bevformer: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers,

Reference 22

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Observation 39227ddf-91e9-4603-9efb-bcc249ce5a28 · outbound

This paper cites Attention is all you need,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Attention is all you need,

Reference 23

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Observation c085bd82-f98a-4d95-9e60-59be8bd6a5f1 · outbound

This paper cites Tumtraf v2x cooperative perception dataset,.

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving Tumtraf v2x cooperative perception dataset,

Reference 24

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source=pdf_text observed=2026-08-01T11:47:32.287574Z digest=sha256:3ae994296b12189bdce5dfb8ffaae7347152869969d2240a0071f4b602ae4a6f

Pith citing papers

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