Paper Citation Record · LEDGER

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models

As of 9 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 6 inbound Pith citation observations for arXiv:2507.00898.

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

pith.paper-citation-record.v1
2507.00898 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:13:15.203711Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:19:55.558166Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T12:15:01.137692Z

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 54a40df1-2516-4178-a8b7-89b767f966c6 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

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

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Hallucination of Multimodal Large Language Models: A Survey

Reference 2

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This paper cites Detecting and Evaluating Medical Hallucinations in Large Vision Language Models.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Detecting and Evaluating Medical Hallucinations in Large Vision Language Models

Reference 3

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This paper cites An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models

Reference 4

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Observation 3b4151fa-716d-498f-a31f-ef1c5a242ddb · outbound

This paper cites Mitigating Hallucination in Visual Language Models with Visual Supervision.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Mitigating Hallucination in Visual Language Models with Visual Supervision

Reference 5

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This paper cites HALC: Object hallucination reduc- tion via adaptive focal-contrast decoding.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models HALC: Object hallucination reduc- tion via adaptive focal-contrast decoding

Reference 6

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This paper cites Gonzalez, Ion Stoica, and Eric P.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Gonzalez, Ion Stoica, and Eric P

Reference 7

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This paper cites Glass, and Pengcheng He.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Glass, and Pengcheng He

Reference 8

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

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Instructblip: towards general-purpose vision-language models with instruction tuning

Reference 9

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Observation eb89ff8c-60ac-4469-9836-8e2cee858cf2 · outbound

This paper cites Multi-modal hal- lucination control by visual information grounding.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Multi-modal hal- lucination control by visual information grounding

Reference 10

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This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 11

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This paper cites Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

Reference 12

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This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 13

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This paper cites Hallucination augmented contrastive learn- ing for multimodal large language model.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Hallucination augmented contrastive learn- ing for multimodal large language model

Reference 14

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This paper cites Lisa: Reasoning segmentation via large language model.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Lisa: Reasoning segmentation via large language model

Reference 15

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This paper cites Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding

Reference 16

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

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 17

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This paper cites Contrastive decoding: Open-ended text gener- ation as optimization.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Contrastive decoding: Open-ended text gener- ation as optimization

Reference 18

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Evaluating object hallucination in large vision-language models

Reference 19

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Microsoft coco: Common objects in context

Reference 20

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Visual instruction tuning

Reference 21

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Improved baselines with visual instruction tuning

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

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This paper cites A Survey on Hallucination in Large Vision-Language Models.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models A Survey on Hallucination in Large Vision-Language Models

Reference 24

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This paper cites Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233.

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233

Reference 25

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Alleviating hallucinations in large vision- language models through hallucination-induced optimiza- tion

Reference 26

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Learning transferable visual models from natural language supervision

Reference 27

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Object hallucination in image captioning

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models A-okvqa: A benchmark for visual question answering using world knowl- edge

Reference 29

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models A mathematical theory of communi- cation

Reference 30

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Aligning Large Multimodal Models with Factually Augmented RLHF

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Eyes wide shut? exploring the visual shortcomings of multimodal llms

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models LLaMA: Open and Efficient Foundation Language Models

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Mitigating hallucinations in large vision-language models with instruction contrastive decoding

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Det- toolchain: A new prompting paradigm to unleash detection ability of mllm

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration

Reference 37

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Woodpecker: Hallucination Correction for Multimodal Large Language Models

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models MM-vet: Evaluating large multimodal models for integrated capabilities

Reference 39

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Contextual object detection with multi- modal large language models

Reference 40

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Incorpo- rating generative feedback for mitigating hallucinations in large vision-language models

Reference 41

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Vscan: Rethinking visual token reduction for efficient large vision-language models

Reference 42

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Ma, Si- mon Stepputtis, Deva Ramanan, Russ Salakhutdinov, Louis- Philippe Morency, Katia P

Reference 43

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ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models Is there a {object} in the image?

Reference 44

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Pith citing papers

Observation a6b45afc-743a-42c1-a244-4009a2519ade · inbound

Revis: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models cites this paper.

Revis: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models

Reference 8

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Mitigating Multimodal Hallucination via Phase-wise Self-reward cites this paper.

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Reference 45

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HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering cites this paper.

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models

Reference 218

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Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement cites this paper.

Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:47:21.420670Z

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 392264aa-5eb5-4da6-a67e-93caf0c54a51 · inbound

VisionPulse: Dynamic Visual Sparsity for Efficient Multimodal Reasoning cites this paper.

VisionPulse: Dynamic Visual Sparsity for Efficient Multimodal Reasoning ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-29T00:12:50.561358Z

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Observation 3b239e49-df4a-4aeb-92f3-5bd34ab9d73b · inbound

Disentangling Semantic Attention from Structural Bias in the Attention Manifold cites this paper.

Disentangling Semantic Attention from Structural Bias in the Attention Manifold ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-31T23:19:55.558166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.