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Paper Citation Record · LEDGER

Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2411.16724.

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

pith.paper-citation-record.v1
2411.16724 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:26:18.828246Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0d31e5fd-2734-4a17-b51e-1d22a8f52548 · inbound

Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models cites this paper.

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

Resolution
unresolved
no resolver link, observed 2026-08-09T15:26:18.828246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T15:26:18.828246Z digest=sha256:af24172ed118acb9533b601e606b8e6fc18a377626fa48966c8b310c1a1ba880

Observation 27e3db79-4f22-4ca6-9cad-af91b5866b2b · inbound

CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention cites this paper.

CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T11:22:38.815007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:22:38.815007Z digest=sha256:d395507bc63b04485ccd959c1fb7fe1e7d65eac0430fefbf402bd9a88c02de8b

Observation 3a485711-a658-4265-8685-3089ac696e31 · inbound

Energy-Guided Decoding for Object Hallucination Mitigation cites this paper.

Energy-Guided Decoding for Object Hallucination Mitigation Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T18:39:27.700032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:39:27.700032Z digest=sha256:0e8ec90652210226f1872f58ede99468c385df59030716c9d9b332bf0c0f9e9a

Observation 235586a9-09bb-4037-b1d9-882a2f8f9c89 · inbound

Modality Bias in LVLMs: Analyzing and Mitigating Object Hallucination via Attention Lens cites this paper.

Modality Bias in LVLMs: Analyzing and Mitigating Object Hallucination via Attention Lens Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T05:04:23.314967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T05:04:23.314967Z digest=sha256:d0bc0c11b58deb336fc8d44507e748db577123619c00dc4f584ae6cc8a8f66fa

Observation 56fe9606-cbb1-4eb2-9601-588ed72eff14 · inbound

SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revision cites this paper.

SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revision Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T04:42:37.262710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:42:37.262710Z digest=sha256:7c3e1d5d737bec849fb420f13d0ae8ae796e881c43efa5f8001a09564f24e45a

Observation 3081177e-7ba2-4319-b1bf-aeecfdc0d251 · inbound

Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding cites this paper.

Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:31:01.421189Z

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.

source=pdf_text observed=2026-05-10T14:50:37.022338Z digest=sha256:6a22fe8e87a475335552c7734ea0a2189c1856f3faf50836ee1b744e579ee005

Observation b7a277c3-88e9-4bd0-a5dd-728fa86507e8 · inbound

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering cites this paper.

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

Reference 276

Resolution
verified exact
arxiv_id, observed 2026-05-09T23:54:45.676745Z

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.

source=arxiv_source observed=2026-05-09T23:51:47.724033Z digest=sha256:ca1d27311abb75972603d7b48aebc18bcc6f0c19293d4165bae623a42dcc1bca

Observation 0a6210a5-fcc6-462a-be07-c6a7766504ef · inbound

Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking cites this paper.

Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:46:47.139577Z

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.

source=pdf_text observed=2026-05-12T03:58:46.417344Z digest=sha256:8cfa698a06113416d5d15cdcfa663a0dce0bf6962989016224dbe05295ffa339

Observation 6edd1f80-e02e-4c04-9c09-1b61a8c3abc0 · inbound

Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking cites this paper.

Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-19T17:12:41.136474Z

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.

source=pdf_text observed=2026-05-19T17:10:13.032423Z digest=sha256:7e02a3d44596a94ff0b61ac666a51a60a6476b168d212b39517490e52476f89d

Observation 6c8b6a24-f2ca-453a-b294-e6c1cd764c2d · inbound

Verbalizable Representations Form a Global Workspace in Language Models cites this paper.

Verbalizable Representations Form a Global Workspace in Language Models Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-01T23:15:25.901469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:15:25.901469Z digest=sha256:7c750a21a79888b8d33e3314f6785975f38f6beebdf578e273b2d225552adbaf