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Paper Citation Record · LEDGER

Dolphins: Multimodal Language Model for Driving

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2312.00438.

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

pith.paper-citation-record.v1
2312.00438 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:33:41.797744Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T16:35:42.142836Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2df64d50-fdae-4ad5-9a76-5bf2c79e2cf9 · inbound

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving cites this paper.

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving Dolphins: Multimodal Language Model for Driving

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-23T16:35:42.147144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-23T16:35:24.063578Z digest=sha256:fa8b816b2964b40e607a1efff7f1d9416a5364e76b22d6b55e22dc60a46b4ec6

Observation 5b039ff6-a4d9-47d5-99f3-f1ff7c772e5f · inbound

A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future cites this paper.

A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future Dolphins: Multimodal Language Model for Driving

Reference 274

Resolution
unresolved
no resolver link, observed 2026-08-11T12:33:41.797744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:33:41.797744Z digest=sha256:7b9bf044d7999d4a4c34a0a76adb82041d366335562108384673f1ee94cfb61a

Observation 8d2eb5e0-e746-41d3-ae7c-91c0612868ce · inbound

Visual Large Language Models for Generalized and Specialized Applications cites this paper.

Visual Large Language Models for Generalized and Specialized Applications Dolphins: Multimodal Language Model for Driving

Reference 164

Resolution
unresolved
no resolver link, observed 2026-08-10T22:08:09.527124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:08:09.527124Z digest=sha256:8cff4c8c7109a8ca8c80a57137ad2d090b5f85df54c359a93793669227cdc7d0

Observation e66cdf29-19c8-4b39-8f1e-a7e7e353c8f8 · inbound

Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives cites this paper.

Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives Dolphins: Multimodal Language Model for Driving

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-10T21:44:55.880123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:44:55.880123Z digest=sha256:cd11dd1d76ff2cd683a640b2ce4a2da6f8b340c63aa3562520e38740b3633e20

Observation 91d70c6a-5fcb-43cd-b09f-25ba7e4b8510 · inbound

LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking cites this paper.

LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking Dolphins: Multimodal Language Model for Driving

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-10T20:34:11.911195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:34:11.911195Z digest=sha256:3faf7180fdf83a40e705acfbe9fc68f5fc805c3e26e53ed0632a3488716d40be

Observation 68bb0ec4-245a-4f04-8f36-e3f0ec66f53d · inbound

Explainability for Vision Foundation Models: A Survey cites this paper.

Explainability for Vision Foundation Models: A Survey Dolphins: Multimodal Language Model for Driving

Reference 94

Resolution
unresolved
no resolver link, observed 2026-08-10T17:26:35.310664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:26:35.310664Z digest=sha256:36d510509548dd3d9734bcb46440c0be640b033fafe1668bf4feb02b0015f7eb

Observation 9999d067-60d3-4ceb-ad95-aa859421de92 · inbound

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving cites this paper.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Dolphins: Multimodal Language Model for Driving

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-10T15:55:07.852163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:55:07.852163Z digest=sha256:32fa2479666049d28a0e95c8d71fc6aba00e2841ef67d4260da5d5aa3e479684

Observation a5409517-a9f0-48ae-a5f2-665bc24bc018 · inbound

S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation cites this paper.

S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation Dolphins: Multimodal Language Model for Driving

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T12:38:43.273602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:38:43.273602Z digest=sha256:5b69e205ff1a06c8ee7edb4aca4b9dc6af1f5088e80fb7f6546d2a9300fc13f5

Observation 16892d25-c216-4f37-903a-92aefed9ae5b · inbound

AD^2-Bench: A Hierarchical CoT Benchmark for MLLM in Autonomous Driving under Adverse Conditions cites this paper.

AD^2-Bench: A Hierarchical CoT Benchmark for MLLM in Autonomous Driving under Adverse Conditions Dolphins: Multimodal Language Model for Driving

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T04:49:28.374098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:49:28.374098Z digest=sha256:d850f351e6f2a0f9b584d677ba0e200a1e935c8102f0f0279d20764cc742f2d1

Observation 6dd443bf-64e6-47c3-be1f-8389e6fb831e · inbound

UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understanding cites this paper.

UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understanding Dolphins: Multimodal Language Model for Driving

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T21:52:23.026538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:52:23.026538Z digest=sha256:59d9d271afa2beec232fc908c83b25d7e46a9d8bb6e9814da24d4c52ca838374

Observation 1baa118f-46e3-49ca-8045-ca67ee201dbf · inbound

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting cites this paper.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Dolphins: Multimodal Language Model for Driving

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T00:28:52.246338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:28:52.246338Z digest=sha256:5cf83b09830af65f32eaca46f64a1da5b02b5a9f680b34578a8cbf83bc7a44ec