Paper Citation Record · LEDGER

Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.17820.

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

pith.paper-citation-record.v1
2405.17820 v2

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 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:33:48.130226Z

measured 0 of 1 external citation measurements

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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.