Paper Citation Record · LEDGER

HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2309.05186.

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

pith.paper-citation-record.v1
2309.05186 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T00:30:18.820624Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:29:57.747436Z

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 447af3a0-5160-491a-88d7-807ec414a3c2 · inbound

VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning cites this paper.

VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:18:49.489941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 04cf2576-b27a-4f24-8c90-f33982be130d · inbound

NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving cites this paper.

NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:46:24.192142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 59b29de4-85e7-4032-ad9c-6baae23ac584 · inbound

The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models cites this paper.

The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:50.138962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79da7d3f-bfc1-45da-9cb5-d43551c0be5f · inbound

UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving cites this paper.

UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:29:57.749280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.