Paper Citation Record · LEDGER

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision

As of 22 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2608.01535.

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

pith.paper-citation-record.v1
2608.01535 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:08:24.264448Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

39 of 39 outbound references displayed

verified exact5
verified fuzzy20
unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ef47c185-c072-4157-a57e-beae37c3b550 · outbound

This paper cites Simlingo: Vision-only closed-loop autonomous driving with language- action alignment,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Simlingo: Vision-only closed-loop autonomous driving with language- action alignment,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:30.927554Z

Source-reported events for the cited work

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Observation aca5813b-c007-4a50-a17c-efda5e492cd9 · outbound

This paper cites AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:20.069331Z

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Observation 9a20e5c5-b8b1-4974-bb6c-9cd5875a6892 · outbound

This paper cites Spatialvlm: Endowing vision-language mod- els with spatial reasoning capabilities,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Spatialvlm: Endowing vision-language mod- els with spatial reasoning capabilities,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:30.714894Z

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Observation df765ff2-5d1d-459c-be46-7a256102a96b · outbound

This paper cites Robospatial: Teaching spatial understanding to 2d and 3d vision-language models for robotics,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Robospatial: Teaching spatial understanding to 2d and 3d vision-language models for robotics,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:30.485101Z

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Observation 27114329-a6fc-4fd3-b11a-aa1de136ed16 · outbound

This paper cites Vlm4d: Towards spatiotemporal awareness in vision language models,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Vlm4d: Towards spatiotemporal awareness in vision language models,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:30.294456Z

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Observation a579b2c8-45a2-4007-8703-423141e92e05 · outbound

This paper cites Depthlm: Metric depth from vision language models,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Depthlm: Metric depth from vision language models,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:20.507532Z

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Observation 4741bb01-186a-4ec0-bd45-1a6963879218 · outbound

This paper cites Rt-2: Vision-language-action models transfer web knowledge to robotic control,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Rt-2: Vision-language-action models transfer web knowledge to robotic control,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:30.045957Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e208d27b-6c49-489a-a96f-a16e44f19540 · outbound

This paper cites Drivelm: Driving with graph visual question an- swering,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Drivelm: Driving with graph visual question an- swering,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:29.766818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8a9d686b-39f4-4f24-b02f-f61ff269d6b1 · outbound

This paper cites DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:20.946744Z

Source-reported events for the cited work

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Observation f018b4b7-756e-4daa-9fda-7de07bb4afc3 · outbound

This paper cites Drivemlm: Aligning multi-modal large lan- guage models with behavioral planning states for autonomous driving,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Drivemlm: Aligning multi-modal large lan- guage models with behavioral planning states for autonomous driving,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:21.077410Z

Source-reported events for the cited work

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Observation a667e433-03bf-4d1e-b049-b539811a5124 · outbound

This paper cites SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:21.164171Z

Source-reported events for the cited work

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Observation 06cbf30d-be95-47ba-9907-df70a268932c · outbound

This paper cites FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:21.257401Z

Source-reported events for the cited work

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Observation 9b79c5e9-0f91-48c2-a21f-3c066ff5d836 · outbound

This paper cites STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:21.388777Z

Source-reported events for the cited work

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Observation 9956e748-4d61-4a2f-b9b9-34bd44f4641d · outbound

This paper cites From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:08:25.958869Z

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Observation ac363621-c3fa-42cc-b8df-378206623e90 · outbound

This paper cites Are vlms ready for autonomous driving? an em- pirical study from the reliability, data, and metric perspectives,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Are vlms ready for autonomous driving? an em- pirical study from the reliability, data, and metric perspectives,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:29.539220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5ed99b6b-8a07-4538-80e3-b57a1d92807b · outbound

This paper cites Centerfusion: Center-based radar and camera fusion for 3d object detection,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Centerfusion: Center-based radar and camera fusion for 3d object detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:29.297006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dff2bb0a-6ad1-47be-9643-e49848d43eb9 · outbound

This paper cites Rcbevdet: Radar-camera fusion in bird’s eye view for 3d object detection,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Rcbevdet: Radar-camera fusion in bird’s eye view for 3d object detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:29.062149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b93fb1eb-ca10-4c42-b5a0-7437558b6a3b · outbound

This paper cites Crkd: Enhanced camera- radar object detection with cross-modality knowledge distilla- tion,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Crkd: Enhanced camera- radar object detection with cross-modality knowledge distilla- tion,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:28.815819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7f859787-685e-4bc6-b1fd-c6748020be14 · outbound

This paper cites Depth estimation from monocular images and sparse radar data,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Depth estimation from monocular images and sparse radar data,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:28.536991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 93440d4e-9b79-456b-80bb-a3d2351a6bd6 · outbound

This paper cites Tacodepth: Towards efficient radar-camera depth estimation with one-stage fusion,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Tacodepth: Towards efficient radar-camera depth estimation with one-stage fusion,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:28.285714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 474bbd75-b831-4724-8a56-5d59b40f19e2 · outbound

This paper cites Depth estimation based on mmwave radar and camera fusion with attention mechanisms and multi-scale features for autonomous driving vehicles,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Depth estimation based on mmwave radar and camera fusion with attention mechanisms and multi-scale features for autonomous driving vehicles,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:28.065025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e98685ac-3743-405b-abff-e56ad04707a0 · outbound

This paper cites Full-velocity radar returns by radar-camera fusion,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Full-velocity radar returns by radar-camera fusion,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:27.808111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0863de55-442f-49a2-9172-d98f10a41ab6 · outbound

This paper cites Pow4r: Point-wise full-velocity esti- mation using 4d radar-camera fusion beyond radial limitations,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Pow4r: Point-wise full-velocity esti- mation using 4d radar-camera fusion beyond radial limitations,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:27.510749Z

Source-reported events for the cited work

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Observation 8d05cd7b-582e-4515-b52d-c29d0dd4a1bb · outbound

This paper cites DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:22.454237Z

Source-reported events for the cited work

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Observation 0222c4fd-8ec4-4f12-80dd-f648f684a5e2 · outbound

This paper cites Raliflow: Scene flow esti- mation with 4d radar and lidar point clouds,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Raliflow: Scene flow esti- mation with 4d radar and lidar point clouds,

Reference 25

Resolution: verified exact; raw_fallback, observed 2026-08-06T00:08:25.760282Z

Source-reported events for the cited work

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Observation 67fd4b07-3a56-4039-a0b3-c407516f0295 · outbound

This paper cites R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 67be25d9-cdb1-40db-bb17-7c86d708f6da · outbound

This paper cites Radar as a teacher: Weakly supervised vehicle detection using radar labels,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Radar as a teacher: Weakly supervised vehicle detection using radar labels,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:27.309381Z

Source-reported events for the cited work

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Observation 613ee83b-8dfc-47f2-9a4a-5924d399bccd · outbound

This paper cites Motion Segmentation from a Moving Monocular Camera.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Motion Segmentation from a Moving Monocular Camera

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:08:24.971547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 181391c7-e4c7-4f0e-8e3f-77e358b139b5 · outbound

This paper cites On Moving Object Segmentation from Monocular Video with Transformers.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision On Moving Object Segmentation from Monocular Video with Transformers

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:08:24.740167Z

Source-reported events for the cited work

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Observation 3da10449-a9ad-4ec6-ac8b-2d0236ce4151 · outbound

This paper cites Segment Any Motion in Videos.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Segment Any Motion in Videos

Reference 30

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Source-reported events for the cited work

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Observation de26c729-7025-45b3-be19-1ca62d31c9ce · outbound

This paper cites Camera-based vehicle velocity estimation from monocular video.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Camera-based vehicle velocity estimation from monocular video

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:23.220951Z

Source-reported events for the cited work

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Observation aea2e315-8466-492f-b061-b562bfd879a7 · outbound

This paper cites Kinematic 3d object detection in monocular video,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Kinematic 3d object detection in monocular video,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:27.010857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cccd1ebe-d35b-4625-9b9b-c100e971de50 · outbound

This paper cites Self-supervised monocular scene flow estimation,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Self-supervised monocular scene flow estimation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:26.786164Z

Source-reported events for the cited work

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Observation b4900de5-bfd1-4624-8d83-f8a5e16b096d · outbound

This paper cites Any4d: Unified feed-forward metric 4d reconstruction,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Any4d: Unified feed-forward metric 4d reconstruction,

Reference 34

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Source-reported events for the cited work

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Observation aa546dae-ed51-4f04-a201-c0a60ec2eb4e · outbound

This paper cites Zero-shot monocular scene flow estimation in the wild,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Zero-shot monocular scene flow estimation in the wild,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:26.635017Z

Source-reported events for the cited work

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Observation 0bbd0220-af8b-4509-92c5-edfcf3aed0ef · outbound

This paper cites nuscenes: A multimodal dataset for au- tonomous driving,.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision nuscenes: A multimodal dataset for au- tonomous driving,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:08:26.393482Z

Source-reported events for the cited work

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Observation f8d41ceb-5dcf-4678-88b0-9b2db312db56 · outbound

This paper cites Qwen3-VL Technical Report.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Qwen3-VL Technical Report

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:23.895579Z

Source-reported events for the cited work

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Observation 0d0bfa25-211d-447c-b102-35dc81f4dbd5 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:24.093133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 368334ce-a943-4ed4-94bd-76312588ab64 · outbound

This paper cites Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T00:08:24.264448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.