Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2405.17820.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T15:26:18.969743Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T20:07:21.036734Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 27d0e22b-0193-4bab-aabc-b2ad88f5a87d · inbound
Hallucination of Multimodal Large Language Models: A Survey cites this paper.
Hallucination of Multimodal Large Language Models: A Survey Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 174
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T12:33:33.164537Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 03d01345-741e-4ae5-9a86-9a68d8519573 · 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 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 c3fe9bdc-7764-4723-9f88-6a177a0ba57a · inbound
The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering cites this paper.
The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T04:22:42.219746Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cfcf8cf5-33c1-418e-8e1d-af68902fcb15 · inbound
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning cites this paper.
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:33:48.130226Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d71b25cd-7e77-4111-8c65-1c0b5196ae63 · inbound
Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration cites this paper.
Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T12:52:18.090952Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 00d24d20-2e38-4ac6-ad6b-816600d743bb · inbound
Revisit What You See: Revealing Visual Semantics in Vision Tokens to Guide LVLM Decoding cites this paper.
Revisit What You See: Revealing Visual Semantics in Vision Tokens to Guide LVLM Decoding Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 46
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T09:43:02.163288Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9c1bc693-ceb7-450a-8f2a-3c261b0085e1 · inbound
ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models cites this paper.
ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:06:54.435666Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15927bdb-d807-466a-911a-3ca3587833fb · inbound
Examining Vision Language Models through Multi-dimensional Experiments with Vision and Text Features cites this paper.
Examining Vision Language Models through Multi-dimensional Experiments with Vision and Text Features Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:58:25.135570Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3af23b1e-f599-41ea-96b6-8a3ca05d2b23 · inbound
Mitigating Multimodal Hallucination via Phase-wise Self-reward cites this paper.
Mitigating Multimodal Hallucination via Phase-wise Self-reward Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 51
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T09:38:43.253566Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e6aa967f-0906-4586-90cf-464022513aeb · inbound
Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence cites this paper.
Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T20:07:21.038650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 1a135e4f-65fb-4554-bc10-866a59161538 · inbound
SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering cites this paper.
SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T21:15:25.010442Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f2d12186-1f82-47db-b643-3deb32b5749b · inbound
C-PTQ: Fisher-weighted Channel-wise Sensitivity for Post-training Quantization of MLLMs cites this paper.
C-PTQ: Fisher-weighted Channel-wise Sensitivity for Post-training Quantization of MLLMs Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 158
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T08:36:03.325942Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.