Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations | Paper Citation Record

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations LLaMA: Open and Efficient Foundation Language Models

Reference 2

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 3

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This paper cites Visual instruction tuning.Adv.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Visual instruction tuning.Adv

Reference 4

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This paper cites Improved Baselines with Visual Instruction Tuning.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Improved Baselines with Visual Instruction Tuning

Reference 5

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This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 6

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 7

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This paper cites Qwen Technical Report.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Qwen Technical Report

Reference 8

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Observation cfe75358-ff73-4c7d-8cbe-b2cfef5b7b6e · outbound

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 9

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Observation f1d4b41e-4c28-4897-82a9-ea75f1ccdfe5 · outbound

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Hallucination of Multimodal Large Language Models: A Survey

Reference 10

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Observation 66e2ef9a-7aaf-41da-9155-14405f3ba299 · outbound

This paper cites Nullu: Mitigating object hallucinations in large vision-language models via halluspace projection.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Nullu: Mitigating object hallucinations in large vision-language models via halluspace projection

Reference 11

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Observation f12fe377-086e-4152-a5f1-8a0c152760c5 · outbound

This paper cites Truthprint: Mitigating lvlm object hallucination via latent truthful-guided pre-intervention.arXiv preprint arXiv:2503.10602, 2025.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Truthprint: Mitigating lvlm object hallucination via latent truthful-guided pre-intervention.arXiv preprint arXiv:2503.10602, 2025

Reference 12

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Observation 0473397b-fec5-45da-b3c0-13bccfee3515 · outbound

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Analyzing and mitigating object hallucination in large vision-language models

Reference 13

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Observation 7bd6d8df-457b-42e7-abf7-ea2e01419e83 · outbound

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Reference 14

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Hallucination augmented contrastive learning for multimodal large language model

Reference 15

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Observation 4c94a0d3-e0d6-4f2f-b22b-47cf3c77c1a1 · outbound

This paper cites Exposing and mitigating spurious correlations for cross-modal retrieval.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Exposing and mitigating spurious correlations for cross-modal retrieval

Reference 16

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Mitigating object hallucinations in large vision-language models through visual contrastive decoding

Reference 17

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Observation 196a4003-abf4-40d3-8f5e-25063d5a3327 · outbound

This paper cites Debiasing large visual language models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Debiasing large visual language models

Reference 18

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This paper cites Halc: Object hallucination reduction via adaptive focal-contrast decoding.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Halc: Object hallucination reduction via adaptive focal-contrast decoding

Reference 19

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Observation ad860591-dd20-4f99-9944-9c9f1db318f6 · outbound

This paper cites ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models

Reference 20

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Observation 25ab535f-ea73-4a22-8646-e069e69f639a · outbound

This paper cites Reducing hallucinations in large vision-language models via latent space steering.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Reducing hallucinations in large vision-language models via latent space steering

Reference 21

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This paper cites Where do Large Vision-Language Models Look at when Answering Questions?.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Where do Large Vision-Language Models Look at when Answering Questions?

Reference 22

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This paper cites Lvlm-intrepret: An interpretability tool for large vision-language models, 2024.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Lvlm-intrepret: An interpretability tool for large vision-language models, 2024

Reference 23

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This paper cites See What You Are Told: Visual Attention Sink in Large Multimodal Models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations See What You Are Told: Visual Attention Sink in Large Multimodal Models

Reference 24

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This paper cites Vision Transformers Need Registers.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Vision Transformers Need Registers

Reference 25

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This paper cites Massive Activations in Large Language Models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Massive Activations in Large Language Models

Reference 26

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This paper cites Object Hallucination in Image Captioning.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Object Hallucination in Image Captioning

Reference 27

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration

Reference 28

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models

Reference 29

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This paper cites Llava-phi: Efficient multi-modal assistant with small language model.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Llava-phi: Efficient multi-modal assistant with small language model

Reference 30

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 31

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This paper cites Detecting and preventing hallucinations in large vision language models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Detecting and preventing hallucinations in large vision language models

Reference 32

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 33

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This paper cites Dress: Instructing large vision-language models to align and interact with humans via natural language feedback.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Dress: Instructing large vision-language models to align and interact with humans via natural language feedback

Reference 34

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 35

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This paper cites Mitigating object hallucination in large vision-language models via image-grounded guidance.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Mitigating object hallucination in large vision-language models via image-grounded guidance

Reference 36

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This paper cites Paying more attention to image: A training-free method for alleviating hallucination in lvlms.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Paying more attention to image: A training-free method for alleviating hallucination in lvlms

Reference 37

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding

Reference 38

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Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation

Reference 39

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Multi-modal hallucination control by visual information grounding

Reference 40

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This paper cites AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models

Reference 41

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This paper cites Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention

Reference 42

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Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 43

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Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks

Reference 44

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This paper cites Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers

Reference 45

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This paper cites Transformer interpretability beyond attention visualization.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Transformer interpretability beyond attention visualization

Reference 46

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This paper cites Vl- interpret: An interactive visualization tool for interpreting vision-language transformers.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Vl- interpret: An interactive visualization tool for interpreting vision-language transformers

Reference 47

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This paper cites FastRM: An efficient and automatic explainability framework for multimodal generative models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations FastRM: An efficient and automatic explainability framework for multimodal generative models

Reference 48

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This paper cites Explaining Multi-modal Large Language Models by Analyzing their Vision Perception.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Explaining Multi-modal Large Language Models by Analyzing their Vision Perception

Reference 49

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This paper cites From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning Tasks.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning Tasks

Reference 50

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This paper cites Finding and Editing Multi-Modal Neurons in Pre-Trained Transformers.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Finding and Editing Multi-Modal Neurons in Pre-Trained Transformers

Reference 51

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Observation 6ccb7e24-eaa5-4926-accd-cfd30a528366 · outbound

This paper cites AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

Reference 52

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Evaluating object hallucination in large vision-language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:34.654011Z

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This paper cites Aligning large multimodal models with factually augmented rlhf.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Aligning large multimodal models with factually augmented rlhf

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:34.471205Z

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This paper cites Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.Adv.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.Adv

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:34.299454Z

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 56

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:34.114986Z

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This paper cites Beam Search Strategies for Neural Machine Translation.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Beam Search Strategies for Neural Machine Translation

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:31.289807Z

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

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:31.400584Z

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Observation f8bab591-d094-47bc-931f-2d936769cd25 · outbound

This paper cites Blended diffusion for text-driven editing of natural images.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Blended diffusion for text-driven editing of natural images

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:33.960642Z

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Observation b87fca78-67d1-42ec-9d67-d2cc6903bcab · outbound

This paper cites Unveiling typographic deceptions: Insights of the typographic vulnerability in large vision-language models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Unveiling typographic deceptions: Insights of the typographic vulnerability in large vision-language models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:33.708879Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2e495d30-7f69-4a63-92fe-e137a6360e93 · outbound

This paper cites Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:33.567854Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7264abe-3342-48d1-8953-f240882fb7ca · outbound

This paper cites Towards interpreting visual information processing in vision-language models.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Towards interpreting visual information processing in vision-language models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:33.440316Z

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Observation bf173a22-c628-4ba6-8068-16487a728e86 · outbound

This paper cites GPT-4 Technical Report.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations GPT-4 Technical Report

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:31.991900Z

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This paper cites Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs.

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

Reference 65

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