Paper Citation Record · LEDGER

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models

As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 6 inbound Pith citation observations for arXiv:2501.02699.

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

pith.paper-citation-record.v1
2501.02699 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

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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 2026-08-07T11:39:46.817981Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T12:33:33.048939Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation c7b9e58d-426c-4226-af10-28feab2a975b · outbound

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

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

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Observation 0a343b1a-2c71-4d75-9b27-31a539697c31 · outbound

This paper cites From colouring-in to pointillism: revisiting semantic segmentation supervision,.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models From colouring-in to pointillism: revisiting semantic segmentation supervision,

Reference 2

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This paper cites Lan- guage models are few-shot learners.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Lan- guage models are few-shot learners

Reference 3

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Observation d3008bc1-1a52-4ac6-bb86-461bc590c0b5 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models A simple framework for contrastive learning of visual representations

Reference 4

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Observation 05bad6a0-b0f8-44e1-92d0-4b8b5337f409 · outbound

This paper cites Pali: Scaling language-image learning in 100+ languages.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Pali: Scaling language-image learning in 100+ languages

Reference 5

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Observation 295dfee2-7742-4d67-bf2c-1a7c9b24c125 · outbound

This paper cites Reproducible scal- ing laws for contrastive language-image learning.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Reproducible scal- ing laws for contrastive language-image learning

Reference 6

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Observation bd63c6de-4d5c-4948-b8bd-8fc567f0c520 · outbound

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

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Gonzalez, Ion Stoica, and Eric P

Reference 7

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This paper cites Scaling instruction- finetuned language models.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Scaling instruction- finetuned language models

Reference 8

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

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Instructblip: Towards general- purpose vision-language models with instruction tuning

Reference 9

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Observation 0b528abc-5b6d-470e-bfd7-fdf62a34c9b4 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 10

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This paper cites InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model

Reference 11

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 12

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This paper cites A unified continual learn- ing framework with general parameter-efficient tuning.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models A unified continual learn- ing framework with general parameter-efficient tuning

Reference 13

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Observation 17fd81d1-ee78-43f7-a47d-00aef5545268 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Bootstrap your own latent-a new approach to self-supervised learning

Reference 14

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Observation 6fe60b39-e4c5-445c-9b7d-12051a3fbf12 · outbound

This paper cites Hal- lusionbench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision- language models.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Hal- lusionbench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision- language models

Reference 15

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This paper cites Dimension- ality reduction by learning an invariant mapping.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Dimension- ality reduction by learning an invariant mapping

Reference 16

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This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Momentum contrast for unsupervised visual rep- resentation learning

Reference 17

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This paper cites Lora: Low-rank adaptation of large language models.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Lora: Low-rank adaptation of large language models

Reference 18

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This paper cites BRA VE: Broadening the visual encoding of vision-language models.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models BRA VE: Broadening the visual encoding of vision-language models

Reference 19

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

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 20

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This paper cites Evaluating object hallucination in large vision-language models.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Evaluating object hallucination in large vision-language models

Reference 21

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This paper cites Microsoft coco: Common objects in context.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Microsoft coco: Common objects in context

Reference 22

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This paper cites Improved baselines with visual instruction tuning, 2023.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Improved baselines with visual instruction tuning, 2023

Reference 23

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EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Visual instruction tuning

Reference 24

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 25

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EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Gpt-4 technical report, 2023

Reference 26

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Observation a788af3f-93ba-4716-844f-0c764e310eea · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models DINOv2: Learning Robust Visual Features without Supervision

Reference 27

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This paper cites Grounding multimodal large language models to the world.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Grounding multimodal large language models to the world

Reference 28

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Observation 32d80f44-e30b-4224-9dce-ec26c648ec6e · outbound

This paper cites Learning transferable visual models from natural language supervision.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Learning transferable visual models from natural language supervision

Reference 29

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 30

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Observation f04221c6-25e9-404c-a1fa-84982be34d33 · outbound

This paper cites xgen-mm-phi3-mini-instruct model card, 2024.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models xgen-mm-phi3-mini-instruct model card, 2024

Reference 31

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This paper cites A-okvqa: A benchmark for visual question answering using world knowl- edge, 2022.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models A-okvqa: A benchmark for visual question answering using world knowl- edge, 2022

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This paper cites Eva-clip: Improved training techniques for clip at scale,.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Eva-clip: Improved training techniques for clip at scale,

Reference 33

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This paper cites Eyes wide shut? exploring the vi- sual shortcomings of multimodal llms.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Eyes wide shut? exploring the vi- sual shortcomings of multimodal llms

Reference 34

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EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Llama 2: Open Foundation and Fine-Tuned Chat Models

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This paper cites Pivot: Prompting for video con- tinual learning.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Pivot: Prompting for video con- tinual learning

Reference 36

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This paper cites Behind the magic, merlim: Multi- modal evaluation benchmark for large image-language mod- els, 2024.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Behind the magic, merlim: Multi- modal evaluation benchmark for large image-language mod- els, 2024

Reference 37

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This paper cites CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning

Reference 38

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EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Sigmoid loss for language image pre-training

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:12:48.405323Z

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Observation f5d1b0f0-9725-4153-b827-08613a188c29 · outbound

This paper cites Galore: Memory- efficient llm training by gradient low-rank projection, 2024.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Galore: Memory- efficient llm training by gradient low-rank projection, 2024

Reference 40

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Observation 8a0cf7ee-cb42-4269-8d08-5d482c3ed60e · outbound

This paper cites Analyzing and mitigating object hallucination in large vision-language models.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Analyzing and mitigating object hallucination in large vision-language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:12:48.065807Z

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

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 42

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This paper cites LLA” (LLaV A-1.5), “LLA*.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models LLA” (LLaV A-1.5), “LLA*

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:12:47.914917Z

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This paper cites We compare the zero-shot and linear probing performance of EAGLE-tuned VLMs against the original models.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models We compare the zero-shot and linear probing performance of EAGLE-tuned VLMs against the original models

Reference 45

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Observation d41bdf56-ba47-46dc-9a91-7014699d54b2 · outbound

This paper cites Figure 3 presents 3 additional scenarios when EAGLE effectively reduces the hallucinations of the IT-VLMs.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Figure 3 presents 3 additional scenarios when EAGLE effectively reduces the hallucinations of the IT-VLMs

Reference 46

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This paper cites The same hyperparameters are applied to both VLMs, EV A01-CLIP- g-14 and OpenAI CLIP-L-14-336.

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models The same hyperparameters are applied to both VLMs, EV A01-CLIP- g-14 and OpenAI CLIP-L-14-336

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:12:47.404903Z

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EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models Unresolved cited work

Reference 2023

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Pith citing papers

Observation 3f6fa8ed-8e71-4294-bb90-12668f424ada · inbound

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

Hallucination of Multimodal Large Language Models: A Survey EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models

Reference 159

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:33:33.052044Z

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Observation 83d1695a-9fc1-4e70-86bc-45c46c44e8d2 · inbound

MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs cites this paper.

MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:39:46.817981Z

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Observation 9e0cb9c0-6a7d-45ad-bf9a-db4528cb194b · inbound

Empowering Multimodal LLMs with External Tools: A Comprehensive Survey cites this paper.

Empowering Multimodal LLMs with External Tools: A Comprehensive Survey EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models

Reference 209

Resolution: unresolved; no resolver link, observed 2026-08-05T20:29:03.976981Z

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Observation e52a1569-1195-4c37-a629-46c10e323306 · inbound

Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings cites this paper.

Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T23:35:18.923981Z

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Observation 65cc5cbc-6c77-4dca-8e7e-da991fdcbdbd · inbound

CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation cites this paper.

CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T08:57:18.134481Z

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Observation 7e809645-c5ef-4a63-bc2b-21e91d2d3166 · inbound

DICA: Dual-Indicator Guided Contrastive Alignment in Multimodal Large Language Models cites this paper.

DICA: Dual-Indicator Guided Contrastive Alignment in Multimodal Large Language Models EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T23:32:06.683252Z

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