Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T15:26:19.007163Z
Paper Citation Record · LEDGER
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
As of 10 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 9 inbound Pith citation observations for arXiv:2502.01419.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T21:09:22.538353Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T19:40:07.123416Z
Reference resolution
59 of 59 outbound references displayed
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- unresolved49
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation dc454b96-53da-4674-9971-29ee93a38a47 · outbound
This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Reference 1
- Resolution
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Source-reported events for the cited work
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Observation 929745e4-1eb3-46e7-926d-d7eee55e8359 · outbound
This paper cites Qwen Technical Report.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Qwen Technical Report
Reference 2
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Source-reported events for the cited work
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Observation c6b79058-3286-4746-ba23-fa5799509d55 · outbound
This paper cites Hallucination of Multimodal Large Language Models: A Survey.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Hallucination of Multimodal Large Language Models: A Survey
Reference 3
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5655d819-507b-4aff-b2ca-1bde8bc3465c · outbound
This paper cites Understanding Information Storage and Transfer in Multi-modal Large Language Models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Understanding Information Storage and Transfer in Multi-modal Large Language Models
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation dac0f0e0-37fd-4cc7-85ff-8b6013bf3e8d · outbound
This paper cites Y., and Furlotte, N.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Y., and Furlotte, N
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation 768ff524-1948-4ea3-8a64-8d127762fdbc · outbound
This paper cites CLAIR: Evaluating Image Captions with Large Language Models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models CLAIR: Evaluating Image Captions with Large Language Models
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation 36792edd-90c0-4ebc-a640-d0fb42ae9d61 · outbound
This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks
Reference 7
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5ba4fad-8213-4420-bc26-8d22e767bea7 · outbound
This paper cites Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges
Reference 8
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc52342b-b5e1-4817-99b3-ffa0fde8affe · outbound
This paper cites Vision transformers need registers.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Vision transformers need registers
Reference 9
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 94b37f1b-95ef-49bf-811f-ac77e0ac460a · outbound
This paper cites The Llama 3 Herd of Models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models The Llama 3 Herd of Models
Reference 10
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Source-reported events for the cited work
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Observation 6d611715-887e-40cf-9c2c-94bfbcb03611 · outbound
This paper cites Multi-modal hallucination control by visual information grounding.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Multi-modal hallucination control by visual information grounding
Reference 11
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a828e6d3-4d90-4496-be4d-97b563001f75 · outbound
This paper cites ImageInWords: Unlocking Hyper-Detailed Image Descriptions.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models ImageInWords: Unlocking Hyper-Detailed Image Descriptions
Reference 12
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 09708e40-51b5-41fc-ace0-ea80c40a84f1 · outbound
This paper cites Damro: Dive into the attention mechanism of lvlm to reduce object hallucination.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Damro: Dive into the attention mechanism of lvlm to reduce object hallucination
Reference 13
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28e1978c-33b5-4dbd-9fda-f3d2f367da4f · outbound
This paper cites Detecting and preventing hallucinations in large vision language models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Detecting and preventing hallucinations in large vision language models
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation c2292dcf-936d-46f4-8e90-083cb94d6c31 · outbound
This paper cites ChartLlama: A Multimodal LLM for Chart Understanding and Generation.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models ChartLlama: A Multimodal LLM for Chart Understanding and Generation
Reference 15
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Observation fa9046c4-4bb0-40b7-b71e-7e79621c16c6 · outbound
This paper cites A multi-modal foundation model to assist people with blindness and low vision in environmental interaction.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models A multi-modal foundation model to assist people with blindness and low vision in environmental interaction
Reference 16
- Resolution
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Observation b4a7800c-c682-4ff8-806f-a28293abd7c3 · outbound
This paper cites Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation
Reference 17
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Source-reported events for the cited work
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Observation e24ae048-8936-45a1-9ce8-d59201f9c993 · outbound
This paper cites Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models
Reference 18
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2a55e92d-2673-4baf-8a20-9360e35ac4d7 · outbound
This paper cites GPT-4o System Card.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models GPT-4o System Card
Reference 19
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0d31e5fd-2734-4a17-b51e-1d22a8f52548 · outbound
This paper cites Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens
Reference 20
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Source-reported events for the cited work
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Observation 163df10a-8836-4206-ae1b-7df13092ee82 · outbound
This paper cites See What You Are Told: Visual Attention Sink in Large Multimodal Models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models See What You Are Told: Visual Attention Sink in Large Multimodal Models
Reference 21
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Observation 1da64ded-5870-471f-85d3-d2c441a3b1fe · outbound
This paper cites Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision
Reference 22
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Observation 4984a3af-918b-41e9-a7cf-d6276e863dae · outbound
This paper cites Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
Reference 23
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 703cbab1-369b-4076-beb2-c3100d8d8972 · outbound
This paper cites Mitigating object hallucinations in large vision-language models through visual contrastive decoding.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Mitigating object hallucinations in large vision-language models through visual contrastive decoding
Reference 24
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Source-reported events for the cited work
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Observation 6db63981-1b8b-49ee-8a9a-716399238624 · outbound
This paper cites LLaVA-OneVision: Easy Visual Task Transfer.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models LLaVA-OneVision: Easy Visual Task Transfer
Reference 25
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Observation 830131d7-cf91-473e-a02c-34758493ff7b · outbound
This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Reference 26
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Cross-Modal Attention Calibration for LVLM Hallucination Mitigation
Reference 27
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Inference-time intervention: Eliciting truthful answers from a language model
Reference 28
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Evaluating Object Hallucination in Large Vision-Language Models
Reference 29
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Vila: On pre-training for visual language models
Reference 30
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Unresolved cited work
Reference 31
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Unresolved cited work
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Unresolved cited work
Reference 33
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Unresolved cited work
Reference 34
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models A Survey on Hallucination in Large Vision-Language Models
Reference 35
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Paying more attention to image: A training-free method for alleviating hallucination in lvlms
Reference 36
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models NVILA: Efficient Frontier Visual Language Models
Reference 37
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Compositional chain of thought prompting for large multimodal models
Reference 38
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Docci: Descriptions of connected and contrasting images
Reference 39
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Reference 40
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Observation f15fb0b3-9fd9-49a0-b3d1-33912980296a · outbound
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Instruction Tuning with GPT-4
Reference 41
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models SAM 2: Segment Anything in Images and Videos
Reference 42
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Grounded sam: Assembling open-world models for diverse visual tasks, 2024
Reference 43
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Object Hallucination in Image Captioning
Reference 44
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Wasserstein distance guided representation learning for domain adaptation
Reference 45
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models
Reference 46
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Calculation of the wasserstein distance between probability distributions on the line
Reference 47
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Observation 4572e567-32f1-4456-972c-1433bbfa4185 · outbound
This paper cites Attention is all you need.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Attention is all you need
Reference 48
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Observation b7d85cb7-4d73-4e51-bdfd-5863dc4c2aa4 · outbound
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Efficient Large Language Models: A Survey
Reference 49
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Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
Reference 50
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Observation 03d01345-741e-4ae5-9a86-9a68d8519573 · outbound
This paper cites Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T15:26:18.969743Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dfd7ffc4-6e19-4861-9f2e-dfd7aee0f512 · outbound
This paper cites Mitigating Object Hallucination via Concentric Causal Attention.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Mitigating Object Hallucination via Concentric Causal Attention
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T15:26:18.974611Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a03e7f78-8efd-483f-b46f-3dc9e7866ee4 · outbound
This paper cites Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T15:26:18.979301Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 253cf50f-d1b4-4129-a141-da335b064f5e · outbound
This paper cites MLLM s know where to look: Training-free perception of small visual details with multimodal LLM s.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models MLLM s know where to look: Training-free perception of small visual details with multimodal LLM s
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T15:26:19.926146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ec6a6d8e-3533-4188-af1d-8de9b73f68c0 · outbound
This paper cites Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate Hallucination in LVLMs.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate Hallucination in LVLMs
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T15:26:18.988905Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3bbe3254-050b-4a0d-8abe-bd0229721ed8 · outbound
This paper cites Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T15:26:18.993526Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c38059a1-b5b1-42dd-a0a5-60afd10374e5 · outbound
This paper cites Analyzing and Mitigating Object Hallucination in Large Vision-Language Models.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T15:26:18.998158Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42e5d5f6-d04e-408f-8854-9b108b5ce77f · outbound
This paper cites IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding.
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T15:26:19.003177Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 39c70211-9980-4c13-adc6-20dfedbd2f98 · outbound
This paper cites write newline.
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T15:26:19.007163Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 341e1f61-fa3e-416f-970b-95f992795572 · inbound
Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework cites this paper.
Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T12:32:36.355166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 25c8c43b-135f-4629-87e9-c85888966d5d · inbound
EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards cites this paper.
EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T21:09:22.538353Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cfb9597e-fe8a-42ce-abaf-2b0832b018fb · inbound
Thinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Models cites this paper.
Thinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Models Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T23:30:50.600673Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation dd292eed-8c43-4279-927b-c44726795d69 · inbound
TraversalBench: Challenging Paths to Follow for Vision Language Models cites this paper.
TraversalBench: Challenging Paths to Follow for Vision Language Models Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T10:36:02.729236Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 1e295fe0-14a8-46f4-81ee-d81be756772d · inbound
Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval cites this paper.
Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T23:31:13.633902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 406b86ce-0591-4441-967b-0ab2d5884f90 · inbound
Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning cites this paper.
Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T22:34:01.851487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a2a9f9a6-797d-441c-a065-0f67309b9ebf · inbound
Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation cites this paper.
Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T03:06:29.650511Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a95a305f-3762-4453-8a21-7134a1e655c8 · inbound
Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation cites this paper.
Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T19:40:07.125342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6272d759-cbc3-4095-bf0c-48fdcb8124ff · inbound
Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation cites this paper.
Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T09:35:39.546947Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.