Paper Citation Record · LEDGER

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models

As of 8 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2507.15652.

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

pith.paper-citation-record.v1
2507.15652 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:32:34.571356Z

measured 42 of 42 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2130b2a7-bad8-4f6e-87ed-024feb8423c5 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 1

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Observation 17c919b7-2fdc-491c-b2cc-9c284d6233e7 · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 2

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Observation a0314bc4-10ea-407c-9e23-8526f66cd422 · outbound

This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 3

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Observation d560e042-2d66-46c3-9ac1-0b5f3b2520d9 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 4

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Observation fcf4d8bd-c3ec-4d25-8c61-ea90b40d5844 · outbound

This paper cites GPT-4 Technical Report.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models GPT-4 Technical Report

Reference 5

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Observation 593148cc-a9ac-4956-8084-0a63a7873b55 · outbound

This paper cites Improved baselines with visual instruction tuning,.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Improved baselines with visual instruction tuning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:35.067278Z

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Observation d9b48a2b-da9c-441b-806f-bbcc140f388d · outbound

This paper cites ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:34.455945Z

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Observation 277e86f3-a42b-477d-b914-a29cc941b790 · outbound

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

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Hallucination of Multimodal Large Language Models: A Survey

Reference 8

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Observation 782f701a-cbda-4b39-9432-d2fffed004b8 · outbound

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

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models A Survey on Hallucination in Large Vision-Language Models

Reference 9

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Observation 6b3533b2-517e-4638-adca-81adca8f7420 · outbound

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

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Evaluating Object Hallucination in Large Vision-Language Models

Reference 10

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Observation 0c1bf0fe-28d4-4ef2-a113-4ddc74b94663 · outbound

This paper cites Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Reference 11

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Source-reported events for the cited work

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Observation 6a2627c0-c68d-4aff-87a2-6975aa66c9b1 · outbound

This paper cites A Survey of Hallucination in Large Foundation Models.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models A Survey of Hallucination in Large Foundation Models

Reference 12

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Observation 4180b87b-fc99-4342-8b17-1fff9bf367cf · outbound

This paper cites HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale

Reference 13

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Observation 67f260e9-e7ee-46aa-a772-65389cf85913 · outbound

This paper cites Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT

Reference 14

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Observation c0ece340-1017-479e-b169-fb1deadea48f · outbound

This paper cites ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models

Reference 15

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Observation e8257ff2-a84e-465c-81a6-ac3e9c0bef4c · outbound

This paper cites A survey on multimodal large language models for autonomous driving,.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models A survey on multimodal large language models for autonomous driving,

Reference 16

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Observation 348d23ca-efb2-4462-a8be-a161e57965cc · outbound

This paper cites Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving,.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving,

Reference 17

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Observation e82b4d40-579c-4b8a-ac39-81300039953e · outbound

This paper cites Evaluating a large language model on searching for gui layouts,.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Evaluating a large language model on searching for gui layouts,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:35.046906Z

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Observation d0c1b8c4-852a-40da-9bdd-f068354066ca · outbound

This paper cites DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Reference 19

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Observation d5142ab2-76c8-4829-b805-da10cfb555fe · outbound

This paper cites In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:34.499975Z

Source-reported events for the cited work

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Observation cabb3c46-a82a-4a36-9996-9b49f7605afe · outbound

This paper cites LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:34.503372Z

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Observation 7c6b1805-2852-4866-9e1b-31ee16c1f0bf · outbound

This paper cites Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:34.506797Z

Source-reported events for the cited work

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Observation 4684c0d9-e17a-4473-94a5-90294e5ce1e2 · outbound

This paper cites Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell

Reference 23

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Observation 6ef8ed9b-9686-4ae0-af3d-8573f8159d15 · outbound

This paper cites Knowledge Mechanisms in Large Language Models: A Survey and Perspective.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Knowledge Mechanisms in Large Language Models: A Survey and Perspective

Reference 24

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Observation 3d1fcd93-d40d-4938-9cb3-0713876ef7b7 · outbound

This paper cites MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:34.517821Z

Source-reported events for the cited work

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Observation 08b89adc-3f7e-4935-9b94-7cb210beee41 · outbound

This paper cites Unleashing region understanding in intermediate layers for mllm-based referring expression generation,.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Unleashing region understanding in intermediate layers for mllm-based referring expression generation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:35.036052Z

Source-reported events for the cited work

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Observation d6b394d2-9faa-4b38-a9e2-9b4122b41ac9 · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks,.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Branchynet: Fast inference via early exiting from deep neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:35.026046Z

Source-reported events for the cited work

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Observation 1c694b9b-4ce1-4710-9d8f-3a0225167cdd · outbound

This paper cites Depth-Adaptive Transformer.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Depth-Adaptive Transformer

Reference 28

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Source-reported events for the cited work

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Observation 9e416501-821c-4235-b285-89d855e11dae · outbound

This paper cites Confident adaptive language modeling,.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Confident adaptive language modeling,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:35.016928Z

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Observation 122b16fc-ee3d-4561-8175-2e63efa27f97 · outbound

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

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Hallucination augmented contrastive learning for multimodal large language model,

Reference 30

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Observation 18cf72b5-94b0-4ae0-a7ec-10d30ecc1f81 · outbound

This paper cites Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

Reference 31

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Observation 797cb5d4-3cbb-48dd-b67b-cfb374d74172 · outbound

This paper cites Mitigating object hallucina- tions in large vision-language models through visual contrastive decoding,.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Mitigating object hallucina- tions in large vision-language models through visual contrastive decoding,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:34.995099Z

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Observation d6d1e12e-8049-433f-a92f-0c05434cee76 · outbound

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

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language Models with Only Attention Sink

Reference 33

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Source-reported events for the cited work

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Observation 95d0c6dc-2568-4125-b628-eef990370a11 · outbound

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

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:34.984548Z

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Observation 3fb021ba-c070-4165-94c6-e41693bede4e · outbound

This paper cites Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models

Reference 35

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Observation 5831311d-7ac5-4f57-a000-47a7816de68c · outbound

This paper cites HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding

Reference 36

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Observation f4ff1d17-dd9f-4aeb-a2ab-d1ad4bcb5b74 · outbound

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

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:34.974822Z

Source-reported events for the cited work

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Observation 73cd3b5b-2e83-4bba-ab18-0a85af58df7e · outbound

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

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Instructblip: Towards general-purpose vision-language models with instruction tuning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:34.965168Z

Source-reported events for the cited work

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Observation 04aee0e3-03be-4a8f-95bc-bf62b28f4756 · outbound

This paper cites MiniGPT-4: Enhancing vision-language understanding with advanced large language models,.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models MiniGPT-4: Enhancing vision-language understanding with advanced large language models,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:34.954851Z

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Observation 7bb93f49-e7d5-4f2c-946b-0155508fedfc · outbound

This paper cites Qwen Technical Report.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Qwen Technical Report

Reference 40

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Observation 60c6ffac-10c6-440e-ac75-fe2fbdae59e4 · outbound

This paper cites MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs

Reference 41

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Observation ec82dec8-964a-44bd-a05c-eef91f3066bf · outbound

This paper cites Object Hallucination in Image Captioning.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Object Hallucination in Image Captioning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:34.571356Z

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Pith citing papers

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