Paper Citation Record · LEDGER

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios

As of 17 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2412.07518.

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

pith.paper-citation-record.v1
2412.07518 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:49:27.726009Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T17:42:49.902997Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T17:43:45.778447Z

Reference resolution

51 of 51 outbound references displayed

verified exact0
verified fuzzy23
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 560253cc-ca3f-41d5-a44f-6449f53628e0 · outbound

This paper cites Traffic sign interpretation via natural language description,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Traffic sign interpretation via natural language description,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.474923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d519cbc4-824d-44fc-b9a4-ecd87af6c8a3 · outbound

This paper cites Vision-language models can identify distracted driver behavior from naturalistic videos,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Vision-language models can identify distracted driver behavior from naturalistic videos,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.486113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 22688284-3a6b-4680-9a28-6061611cad03 · outbound

This paper cites GPT-4 Technical Report.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios GPT-4 Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.485882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea053f42-3b4a-4dfc-b2e4-d064e5d7d6f4 · outbound

This paper cites LLaVA-CoT: Let Vision Language Models Reason Step-by-Step.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.491807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8aa0cf9-4803-4dca-9b76-e509cf6e7709 · outbound

This paper cites Traffic scenario understanding and video captioning via guidance attention captioning network,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Traffic scenario understanding and video captioning via guidance attention captioning network,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.470772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 39e60695-488d-4931-924c-c7206c657fc8 · outbound

This paper cites Nle-dm: Natural-language explanations for decision making of autonomous driving based on semantic scene understanding,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Nle-dm: Natural-language explanations for decision making of autonomous driving based on semantic scene understanding,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.453183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 007420f7-b3c3-4a70-b4f9-527f3e7e347d · outbound

This paper cites The crossroads of llm and traffic control: A study on large language models in adaptive traffic signal control,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios The crossroads of llm and traffic control: A study on large language models in adaptive traffic signal control,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.435605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e9bfdc2b-c3f1-408d-bd7d-b2e876ddc66e · outbound

This paper cites Adapt: Action-aware driving caption transformer,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Adapt: Action-aware driving caption transformer,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.419410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4caad00b-57f0-489b-84b3-60a8fce37082 · outbound

This paper cites Hallucination of Multimodal Large Language Models: A Survey.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Hallucination of Multimodal Large Language Models: A Survey

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.519247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea11c981-cc66-47c4-a342-9d8ba91785c9 · outbound

This paper cites Mitigating hallucination in large multi-modal models via robust instruction tuning,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Mitigating hallucination in large multi-modal models via robust instruction tuning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.404672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e52d00c4-fe22-49f4-b55f-d71f1b6a376e · outbound

This paper cites Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.389316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 14cfca7f-30b6-4f5a-a70e-d181a404543f · outbound

This paper cites Hallucination augmented contrastive learning for multimodal large language model,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Hallucination augmented contrastive learning for multimodal large language model,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.375199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c135c7fa-c7b2-4f6f-b957-494d442b76d6 · outbound

This paper cites Aligning Large Multimodal Models with Factually Augmented RLHF.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.539818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f5f03e7-072c-41da-a48c-bee31b5b4ef2 · outbound

This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.545497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b742577-e943-4658-8af7-6271fd7b924e · outbound

This paper cites Improved baselines with visual instruction tuning,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Improved baselines with visual instruction tuning,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.550816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3006a15-9108-4611-bf4c-69f72fb8b517 · outbound

This paper cites mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.342307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa2eae24-06d0-4098-a4bb-bcea3eb779d7 · outbound

This paper cites Vigc: Visual instruction generation and correction,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Vigc: Visual instruction generation and correction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.327223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 217cee6b-23cc-406f-b385-a56429c77eeb · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.310873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9bbc8d18-5ea1-4a3f-86f2-ad8b28440dd7 · outbound

This paper cites Haloquest: A visual hallucination dataset for advancing multimodal reasoning,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Haloquest: A visual hallucination dataset for advancing multimodal reasoning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.296065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8b53a5e1-f295-43fc-8c5b-cf580cde0a63 · outbound

This paper cites Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.575316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69db74c2-4841-4e48-ab2e-d2e64b6e7578 · outbound

This paper cites Exploiting semantic reconstruction to mitigate hallucinations in vision-language models,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Exploiting semantic reconstruction to mitigate hallucinations in vision-language models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.281315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c8d05106-4cfe-49d3-b300-cd77af4f5b0f · outbound

This paper cites Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.585104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f55d8983-b1e8-493c-bffa-20b1f34cfd4b · outbound

This paper cites Multi-modal hallucination control by visual information grounding,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Multi-modal hallucination control by visual information grounding,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.265273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 72e2f069-7c10-4ada-8e3d-ac6c230b81df · outbound

This paper cites Vcoder: Versatile vision encoders for multimodal large language models,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Vcoder: Versatile vision encoders for multimodal large language models,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.249868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e2741b30-9e48-47e4-9341-ecc0e587c408 · outbound

This paper cites Woodpecker: Hallucination Correction for Multimodal Large Language Models.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.598698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9951bcef-f3c9-489b-87f7-388b2c1af901 · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Llava-next: Improved reasoning, ocr, and world knowledge,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.602779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a40d6cf1-3b7f-48dd-a068-df086e3dc9c3 · outbound

This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.608662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6053466-ecc6-4b89-8a39-1c8aa5c8b5e7 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.614422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8e1ba7e-1683-4635-9555-e25e151814b8 · outbound

This paper cites mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.618896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9133617-4516-4db2-a2e1-54e31fa98f05 · outbound

This paper cites Instructblip: Towards general-purpose vision- language models with instruction tuning,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Instructblip: Towards general-purpose vision- language models with instruction tuning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.223464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3ef0f0be-8626-4c68-bd4c-ca0de7cbfa1d · outbound

This paper cites Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.209398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5a5be2b5-b9ce-4173-a831-1f7c4950aeb1 · outbound

This paper cites Mitigating fine-grained hallucination by fine-tuning large vision-language models with caption rewrites,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Mitigating fine-grained hallucination by fine-tuning large vision-language models with caption rewrites,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.195405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 512f8840-b312-4dfc-95fa-5d1d3834485d · outbound

This paper cites Lion: Empowering multimodal large language model with dual-level visual knowledge,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Lion: Empowering multimodal large language model with dual-level visual knowledge,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.180287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29a8c5e6-8923-445a-a651-d985ed64bb6d · outbound

This paper cites IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.639497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01b9ae5f-3d87-43d3-b6c4-dd920cd010e9 · outbound

This paper cites HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.644096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2055866-7ffd-4b4f-91fb-5abb7bce6491 · outbound

This paper cites Analyzing and Mitigating Object Hallucination in Large Vision-Language Models.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Analyzing and Mitigating Object Hallucination in Large Vision-Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.650696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2919765-6d37-44dd-ae62-efd1334a8ba4 · outbound

This paper cites Mit- igating object hallucinations in large vision-language models through visual contrastive decoding,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Mit- igating object hallucinations in large vision-language models through visual contrastive decoding,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.166497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 55f8727b-2365-4b12-930a-b91df6cf3d9e · outbound

This paper cites Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.660582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8df3984-0dbc-4966-97b0-869cdd951af1 · outbound

This paper cites Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.665183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1eb8e3ad-da5c-4577-bb0a-de47a43f624e · outbound

This paper cites The Llama 3 Herd of Models.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios The Llama 3 Herd of Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.670707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f034bc6-2cab-4524-9aa3-fc71d2f25a07 · outbound

This paper cites Open-set image tagging with multi-grained text supervision,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Open-set image tagging with multi-grained text supervision,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.151112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 208b4375-f338-4a3f-883a-fe30a2f150fc · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.681417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 292d23c6-0e30-4c98-a736-6f5f261554ec · outbound

This paper cites Coda: A real-world road corner case dataset for object detection in autonomous driving,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Coda: A real-world road corner case dataset for object detection in autonomous driving,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.135875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9c64adaf-e376-4328-9718-990f097e2911 · outbound

This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.692477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e63b6dc-b798-45d4-9306-88e30a237e94 · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Evaluating Object Hallucination in Large Vision-Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.697705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b66d674-0547-44a6-8d72-1febadd7cbcc · outbound

This paper cites Segment everything everywhere all at once,.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Segment everything everywhere all at once,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:49:28.120382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8e2109f5-2072-4528-a0a5-ca5abd320ce1 · outbound

This paper cites YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.706573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc577c95-21a6-4b8c-b1f3-518356548657 · outbound

This paper cites YOLOv10: Real-Time End-to-End Object Detection.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios YOLOv10: Real-Time End-to-End Object Detection

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.711279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51fa0b9f-dac1-4ff4-bee6-1eec8831baa8 · outbound

This paper cites YOLOv11: An Overview of the Key Architectural Enhancements.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios YOLOv11: An Overview of the Key Architectural Enhancements

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.716220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb5d9ed6-7bbe-4ceb-bbc1-ad7da6072e75 · outbound

This paper cites Tag2Text: Guiding Vision-Language Model via Image Tagging.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Tag2Text: Guiding Vision-Language Model via Image Tagging

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.720882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation decb2201-b40a-48ed-b566-63aa403c9615 · outbound

This paper cites Recognize Anything: A Strong Image Tagging Model.

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios Recognize Anything: A Strong Image Tagging Model

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:27.726009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b38fe928-87d4-47db-943c-ad196390170f · inbound

TPS-Drive: Task-Guided Representation Purification for VLM-based Autonomous Driving cites this paper.

TPS-Drive: Task-Guided Representation Purification for VLM-based Autonomous Driving Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:43:45.780075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.