Paper Citation Record · LEDGER

LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2312.14074.

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

pith.paper-citation-record.v1
2312.14074 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:25:11.676998Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T08:35:32.763349Z

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 7d6a5f0a-5749-4df9-b41e-3ace01360970 · inbound

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention cites this paper.

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 113

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T23:07:42.646881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7bc74a99-82bc-40af-a80b-5fb7dfc57442 · inbound

Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security cites this paper.

Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 151

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:57:26.662863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f873526a-e012-4150-8e36-5dbeb3a814ea · inbound

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing cites this paper.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-08T16:25:11.676998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7172fd86-6175-4cdd-b5e2-6ccdce223f01 · 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 LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:06.746649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08761c9a-b025-4ba5-8f76-416a3fc6726a · inbound

Hierarchical Question-Answering for Driving Scene Understanding Using Vision-Language Models cites this paper.

Hierarchical Question-Answering for Driving Scene Understanding Using Vision-Language Models LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:23:13.595461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb680d9d-10f3-4618-99b6-1bcd80ec7deb · inbound

CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation cites this paper.

CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:20.361197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21820941-6e47-4cff-b063-4ae2e2ad74ce · inbound

Mitigating Object Hallucinations via Sentence-Level Early Intervention cites this paper.

Mitigating Object Hallucinations via Sentence-Level Early Intervention LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:35:32.766654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d8b4ee8e-ad7c-4a73-bbc2-6a7c255d168d · inbound

City-VLM: Towards Multidomain Perception Scene Understanding via Multimodal Incomplete Learning cites this paper.

City-VLM: Towards Multidomain Perception Scene Understanding via Multimodal Incomplete Learning LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T16:45:21.383374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d1dd959-0b45-452e-b3bb-8f28fb0625bc · inbound

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning cites this paper.

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T16:33:59.762003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f4dfab3-636f-405f-9f70-f39ef265b4a9 · inbound

B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding cites this paper.

B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-19T00:11:55.945147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 89509a5e-4f84-4017-9308-07e483a219ee · inbound

LightVLM: Acceleraing Large Multimodal Models with Pyramid Token Merging and KV Cache Compression cites this paper.

LightVLM: Acceleraing Large Multimodal Models with Pyramid Token Merging and KV Cache Compression LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T13:42:28.293970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55641a9e-ed4d-452a-8257-448574379654 · inbound

Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety cites this paper.

Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T11:54:15.944948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.