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Paper Citation Record · LEDGER

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning

As of 16 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2608.01635.

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

pith.paper-citation-record.v1
2608.01635 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:10:13.252046Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

54 of 54 outbound references displayed

  • verified exact4
  • verified fuzzy4
  • unresolved44
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

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Outbound references

Observation f8a9a247-2cdc-4f31-967e-344c83c85e75 · outbound

This paper cites GPT-4 Technical Report.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning GPT-4 Technical Report

Reference 1

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Observation c37d79e8-fa56-4c39-98a7-d0a1e6f10c9b · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 2

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source=pdf_text observed=2026-08-15T15:10:13.031347Z digest=sha256:b4a88703f65d9bc71cc84dc43fa9553f7f38c01e560e19a065914998f51d6a78

Observation d550d22c-b605-49b2-ae72-de55c401c120 · outbound

This paper cites Qwen2.5-VL Technical Report.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Qwen2.5-VL Technical Report

Reference 3

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source=pdf_text observed=2026-08-15T15:10:13.040882Z digest=sha256:52fdb4934d40deb0c3d8ce0283c0a24e0b26b65679fcfdd9f022879301dd4b5f

Observation 7f79ca4e-f91c-4539-af8f-949ee5c8b7f8 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 5

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source=pdf_text observed=2026-08-15T15:10:13.048917Z digest=sha256:71ce01a3f5b7f7097852a6e733c11359e46fb7c2931153b007b4a0cac6083f98

Observation cbce7e97-ed3e-4061-91e7-c5b6994c6e47 · outbound

This paper cites InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 6

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source=pdf_text observed=2026-08-15T15:10:13.053878Z digest=sha256:b291ae84b48f0c35fd62488d10780b56b2bf3d392f308b6001393ecf1603724d

Observation b7fe13db-620d-4b0d-82b4-715d503d6707 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 7

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source=pdf_text observed=2026-08-15T15:10:13.058503Z digest=sha256:f274cfeea1d545a8fe79f988228be21ab1589a18749ff6572c8840e54dff07dd

Observation b35bdf3a-9d49-44c5-ae35-eeac53f711c8 · outbound

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

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 8

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source=pdf_text observed=2026-08-15T15:10:13.062864Z digest=sha256:71671c27ec30a933d30bc68312bcc02542f076e28cb149ef096a65befad28b99

Observation 37f2b892-f65d-4f11-b6cd-7fa3ae1c5d04 · outbound

This paper cites Hidden in plain sight: VLMs overlook their visual representations.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Hidden in plain sight: VLMs overlook their visual representations

Reference 9

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source=pdf_text observed=2026-08-15T15:10:13.066770Z digest=sha256:4b320a53749cb86bb7efb868f70ac5422fddc13a5cfcce7fe5533777b1260097

Observation bac51d75-f5c4-4fb2-a92e-18c46cc5e294 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 10

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Observation e17aaac1-8b82-4ba0-a645-e0432dc272a5 · outbound

This paper cites E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 11

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source=pdf_text observed=2026-08-15T15:10:13.074455Z digest=sha256:e77eb4a32aa9cf0f010c236f5809f13eaacc72ad5eb73da5bbf704de7bb117f9

Observation 9684e802-5fe9-41d4-b923-8f1ba2e70e74 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 12

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source=pdf_text observed=2026-08-15T15:10:13.078500Z digest=sha256:e8268aea47beb9731378b0755b7d6595451b92277be1b83fd036f28c4eba8784

Observation 12dc7a2f-9fb7-4945-8cb5-32c504fc2108 · outbound

This paper cites Visual Prompt Tuning.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Visual Prompt Tuning

Reference 13

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source=pdf_text observed=2026-08-15T15:10:13.082114Z digest=sha256:907227c0d264510a89f1cdadbaef3980dcdd76badedd42182eb34673a4a5005d

Observation 309ad413-30c8-43a7-b124-7c7ef35c92d5 · outbound

This paper cites From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models

Reference 14

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source=pdf_text observed=2026-08-15T15:10:13.086299Z digest=sha256:0456a65ab313913aa357ed83b56616dd336726337c105a12a3026901905e3565

Observation 81e457f9-9a81-49bd-a68c-0130329ba1ab · outbound

This paper cites 2025.Multimodal Continual Learning with MLLMs from Multi-scenario Perspectives.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning 2025.Multimodal Continual Learning with MLLMs from Multi-scenario Perspectives

Reference 15

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T15:10:13.090066Z digest=sha256:ba30028d7dc8c10cc134989a08e71524c192772540ead5d35c8d6bce02831a93

Observation c0db908a-a066-4042-9000-895dbd37eaf8 · outbound

This paper cites 2026.Mitigating Long-Tail Bias in HOI Detection via Adaptive Diversity Cache.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning 2026.Mitigating Long-Tail Bias in HOI Detection via Adaptive Diversity Cache

Reference 16

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source=pdf_text observed=2026-08-15T15:10:13.093510Z digest=sha256:9969513e6984f2ba37d24b9031382a7a44bd586769ecf400082c306eadfe039d

Observation db2dc4bb-1474-4ec9-9357-d27ec5855f24 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 17

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Observation 0e0d2263-5b2d-4d16-bffe-10a3976b2eca · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 18

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source=pdf_text observed=2026-08-15T15:10:13.100537Z digest=sha256:05acf2bf3ff68de9d2b80f4d785678d7a5ab0b5f406783dd2022916e1aaa3d3b

Observation 23045e58-746a-45c1-b41f-cd28f84b0439 · outbound

This paper cites Khan, and Fahad Shahbaz Khan.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Khan, and Fahad Shahbaz Khan

Reference 19

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source=pdf_text observed=2026-08-15T15:10:13.104160Z digest=sha256:b300981f31194c99452457ccc2589c0d357a8816aab211a63b500c577f19ea64

Observation ad8c2032-b06c-4849-8bdc-7d428710ca76 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 20

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source=pdf_text observed=2026-08-15T15:10:13.107980Z digest=sha256:b8b4bf713e32ae4f2c6c173653b913f78bfa71f6e8f2ee1aeb940c15b0046513

Observation 2182b9a1-777d-4b2f-a8ef-6ea26a1218c2 · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning LLaVA-OneVision: Easy Visual Task Transfer

Reference 21

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source=pdf_text observed=2026-08-15T15:10:13.111459Z digest=sha256:c5f0eb677d7b08fa9e02a41f7db85a7ded43ccfd140c982fae36b5829e6cd1d6

Observation 89bb2ef1-3030-4762-a905-4c43819e7b82 · outbound

This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 22

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Observation 11feea3a-1ee2-4275-86aa-1e943d615aa3 · outbound

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

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Evaluating Object Hallucination in Large Vision-Language Models

Reference 23

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Observation d51b2a6c-967a-4056-94c4-047274a93f7e · outbound

This paper cites Depth Anything 3: Recovering the Visual Space from Any Views.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Depth Anything 3: Recovering the Visual Space from Any Views

Reference 25

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Observation d19ed299-2527-42d2-a1e5-7bbb6a31b8b9 · outbound

This paper cites SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

Reference 26

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Observation 338ac0d0-3396-4603-ac07-8d48497517ab · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 28

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source=pdf_text observed=2026-08-15T15:10:13.139583Z digest=sha256:8ba6bc09fd447587ee6b9011e4f2099bbc1690e0d01306afbf4fd01a53b78ac5

Observation 9c836d49-9359-4940-93ba-610794140d9b · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 29

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source=pdf_text observed=2026-08-15T15:10:13.143077Z digest=sha256:a11dd1eced1afb45aa880612e654e7c3477f03de217657e4e49187edbd5e213f

Observation 4872d35d-2895-49a7-87aa-0703129e4ec0 · outbound

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

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 30

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source=pdf_text observed=2026-08-15T15:10:13.150579Z digest=sha256:aa270ef51069a7c8a21e6f56c69f67c8143305c7b41088e7ea95cfe2bb452b29

Observation c621f11c-b09d-4ba2-9153-f9d45701f3e8 · outbound

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

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning DINOv2: Learning Robust Visual Features without Supervision

Reference 31

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source=pdf_text observed=2026-08-15T15:10:13.154334Z digest=sha256:bd828b8bb27125d6148a20107069f603ba215d7f8891a2afe3ba48b3b03d5dc7

Observation 1d242530-9e7c-428f-bf66-b12ab8651103 · outbound

This paper cites Bidirectional Prototype-Reward co-Evolution for Test-Time Adaptation of Vision-Language Models.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Bidirectional Prototype-Reward co-Evolution for Test-Time Adaptation of Vision-Language Models

Reference 32

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source=pdf_text observed=2026-08-15T15:10:13.158198Z digest=sha256:e7d71f5d865ed32e2efc2ca8dbe20ecd34984fcdf60a08fc809fe843d0715d0a

Observation 923c3540-74a9-4e62-8acc-04b5c39bf4a7 · outbound

This paper cites 2025.Class-A ware Prototype Learning with Negative Contrast for Test-Time Adaptation of Vision-Language Models.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning 2025.Class-A ware Prototype Learning with Negative Contrast for Test-Time Adaptation of Vision-Language Models

Reference 33

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Observation c1a24e7f-ca37-44c5-aaa3-2ff4505debdc · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 34

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source=pdf_text observed=2026-08-15T15:10:13.166088Z digest=sha256:b528a10be1890e65ccda8da380a5457860518e3962c2e125ee378512ffdf85dd

Observation 47670324-4b97-4c58-ae8f-1a8cf8d99b94 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning SAM 2: Segment Anything in Images and Videos

Reference 35

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source=pdf_text observed=2026-08-15T15:10:13.169720Z digest=sha256:533722e12a8da41622e928814017c7c6d2062b824db1b5de770b079856ee11b5

Observation 49abcba5-801d-4ff5-a088-ac89af31c9ab · outbound

This paper cites Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Reference 36

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source=pdf_text observed=2026-08-15T15:10:13.173431Z digest=sha256:5e6d32a263bada552994759d6310cdbc1845f324bbe121fdca238a6942cfcd01

Observation 3296478f-c6af-41e9-8b84-5a5db73248f6 · outbound

This paper cites DINOv3.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning DINOv3

Reference 37

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source=pdf_text observed=2026-08-15T15:10:13.177406Z digest=sha256:671cfcac20afdf0ac632e932a69a7b9e294af5edd3bc3e530c402ef693aa3352

Observation d03f54d1-bb82-4ced-9acb-fc1bdbf6942e · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 38

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source=pdf_text observed=2026-08-15T15:10:13.181142Z digest=sha256:71cfd395ed127978ce89b2c61ce4cc7ea8448517799e086c26f6084a078defad

Observation 6c77af0c-747e-42dc-b848-627ee9cf924e · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Kimi K2: Open Agentic Intelligence

Reference 39

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source=pdf_text observed=2026-08-15T15:10:13.184765Z digest=sha256:119cd61bf41f52a7367e8b271ab477a17753ee1a6e1a27cbf00817c721f39401

Observation 7416d642-e99f-4d6d-a36e-e6d99a734b60 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 40

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Observation 9a8fbc30-7b7a-4a48-9baa-cd9bc80176ce · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 41

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Observation b8a4aa43-56d9-4f97-a82b-a01e93f38214 · outbound

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

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning LLaMA: Open and Efficient Foundation Language Models

Reference 42

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Observation 57fe104e-266e-4312-b144-ceee984e14f4 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 43

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source=pdf_text observed=2026-08-15T15:10:13.200530Z digest=sha256:230a1410d141bab0425ebccfcf0ad71436c838010cc2a0a4360cd81fa421970e

Observation faf847d0-07d0-4c63-ba6f-66f2744f8d26 · outbound

This paper cites Reconstructive Visual Instruction Tuning.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Reconstructive Visual Instruction Tuning

Reference 45

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Observation 51f42715-6559-43f6-a532-b04de1468418 · outbound

This paper cites Stop Looking for Important Tokens in Multimodal Language Models: Duplication Matters More.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Stop Looking for Important Tokens in Multimodal Language Models: Duplication Matters More

Reference 46

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Observation 2547d169-cfce-4cc3-81f5-0c6aaebff641 · outbound

This paper cites Qwen3 Technical Report.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Qwen3 Technical Report

Reference 47

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Observation 9b444dfb-fa95-415c-b73c-c9f217471890 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 48

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Observation 0a8c1e7b-3ed6-4bf9-8854-bc4b81d88509 · outbound

This paper cites 2026.Holo-World: Unified Camera, Object and Weather Control for Video World Model.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning 2026.Holo-World: Unified Camera, Object and Weather Control for Video World Model

Reference 49

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 0367c7d9-a98a-44c1-9c0e-a7903022580e · outbound

This paper cites 2025.Visual Representation Alignment for Multimodal Large Language Models.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning 2025.Visual Representation Alignment for Multimodal Large Language Models

Reference 50

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Observation db43555a-3cc1-4100-86fd-a1e9da023427 · outbound

This paper cites Introducing Visual Perception Token into Multimodal Large Language Model.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Introducing Visual Perception Token into Multimodal Large Language Model

Reference 51

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Observation 1f88e854-3dd7-4b90-a786-ac89bd15ff91 · outbound

This paper cites 2024.Representation alignment for generation: Training diffusion transformers is easier than you think.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning 2024.Representation alignment for generation: Training diffusion transformers is easier than you think

Reference 52

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T15:10:13.235666Z digest=sha256:a5a0cced64a8439232de7679dec9dff7f6364754db7a280cdcd50666abf38dea

Observation 0a9b5cbe-0297-4743-80ae-cc5f2ef9f23f · outbound

This paper cites When and why vision-language models behave like bags-of-words, and what to do about it?.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning When and why vision-language models behave like bags-of-words, and what to do about it?

Reference 53

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Observation 69911a32-ad63-410b-8658-42542f184227 · outbound

This paper cites 2024.Instruct Large Language Models to Drive like Humans.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning 2024.Instruct Large Language Models to Drive like Humans

Reference 54

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raw_fallback, observed 2026-08-15T15:10:14.091431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T15:10:13.244050Z digest=sha256:e1dd6bc9b4b90f7e5271295218dc0a5b2683fe0b68791f91717154481d1dec95

Observation 9627dfaf-ce87-432f-b033-0f930ea3dc2c · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 55

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T15:10:13.247796Z digest=sha256:fcf5ea546450d1c5dcfa3a8dea52c9295402440f8c11cd38f39ae5a6baf1964f

Observation 28a7fe01-cbb5-4b7b-b256-a68cb2097384 · outbound

This paper cites ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Discrete Diffusion Models.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Discrete Diffusion Models

Reference 56

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source=pdf_text observed=2026-08-15T15:10:13.252046Z digest=sha256:df54892d153a3bd520745032ddcfa70cde37d1ab4fae432556a1723d0f924642

Observation cb58d612-29e8-4734-95b6-a344e8c145b0 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 2022

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source=pdf_text observed=2026-08-15T15:10:13.035968Z digest=sha256:dedfcd4b0c916b8b27ef374c9df64d27d01fdadfbc5c637795e0193053a560bc

Observation 0457d425-9d99-435f-97ea-37d3967ac439 · outbound

This paper cites an unresolved cited work.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning Unresolved cited work

Reference 2024

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Pith citing papers

No inbound Pith citation observations are available.