Paper Citation Record · LEDGER

IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2508.03469.

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

pith.paper-citation-record.v1
2508.03469 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T07:57:26.695279Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T15:07:03.872711Z

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 93c7c5d7-f8c4-4c9e-81c3-654d7468adf6 · inbound

StochasT: Learning with Stochastic Turn Depth for Visual Instruction Tuning cites this paper.

StochasT: Learning with Stochastic Turn Depth for Visual Instruction Tuning IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:07:03.874543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1cccbf46-ee10-4923-897e-93f11be2c53e · inbound

Visual Access Boundaries in Vision-Language Model Reasoning cites this paper.

Visual Access Boundaries in Vision-Language Model Reasoning IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T06:24:28.914749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61e76232-1dff-4502-8493-79a598079b79 · inbound

Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models cites this paper.

Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T07:57:26.695279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 591660ee-9504-4b54-97f8-a099ba5dfa38 · 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 IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models

Reference 234

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:12.946339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.