Paper Citation Record · LEDGER

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

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 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 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:36:47.698266Z

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 2026-08-11T06:34:44.6726+00:00.

Observation 680b26c0-2b2d-4d1e-bd0e-bebc014f8516 · inbound

SAFLITE: Fuzzing Autonomous Systems via Large Language Models cites this paper.

SAFLITE: Fuzzing Autonomous Systems via Large Language Models HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T04:36:47.698266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ee5c6f2-2ec7-4ffd-8f8f-90ff05d76dc3 · inbound

MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic Scenarios cites this paper.

MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic Scenarios HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T00:42:44.293660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c826b25f-fb06-4508-9608-b2978b554abd · inbound

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

Visual Large Language Models for Generalized and Specialized Applications HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 161

Resolution: unresolved; no resolver link, observed 2026-08-10T22:08:09.516387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f878344-7e74-4daa-9e73-aac77aca1ab8 · inbound

H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving cites this paper.

H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:11.512337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d47fadd-530c-4454-b21e-1ced9eeb0264 · inbound

Embodied Scene Understanding for Vision Language Models via MetaVQA cites this paper.

Embodied Scene Understanding for Vision Language Models via MetaVQA HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T20:15:04.656528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc4c699e-0e9c-41c6-a8b9-14ca7e9af234 · inbound

Distilling Multi-modal Large Language Models for Autonomous Driving cites this paper.

Distilling Multi-modal Large Language Models for Autonomous Driving HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T19:44:58.354852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2f4adb4-faaf-4e59-8bb2-bbc09de2f695 · inbound

Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language Models with Only Attention Sink cites this paper.

Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language Models with Only Attention Sink HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:29:27.927310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4bfc406-70a5-41a0-bdb9-7887a2762fd4 · inbound

SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs cites this paper.

SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T11:14:08.526943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bf0d41e-eeaf-4fd5-9d23-953d6294c2d0 · inbound

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning cites this paper.

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:06.675526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+00:00.