Paper Citation Record · LEDGER

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

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2502.01419.

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

pith.paper-citation-record.v1
2502.01419 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:09:22.538353Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:40:07.123416Z

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 341e1f61-fa3e-416f-970b-95f992795572 · inbound

Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework cites this paper.

Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:32:36.355166Z

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 25c8c43b-135f-4629-87e9-c85888966d5d · inbound

EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards cites this paper.

EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T21:09:22.538353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfb9597e-fe8a-42ce-abaf-2b0832b018fb · inbound

Thinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Models cites this paper.

Thinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Models Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:30:50.600673Z

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 dd292eed-8c43-4279-927b-c44726795d69 · inbound

TraversalBench: Challenging Paths to Follow for Vision Language Models cites this paper.

TraversalBench: Challenging Paths to Follow for Vision Language Models Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:36:02.729236Z

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 1e295fe0-14a8-46f4-81ee-d81be756772d · inbound

Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval cites this paper.

Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:31:13.633902Z

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 406b86ce-0591-4441-967b-0ab2d5884f90 · inbound

Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning cites this paper.

Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:34:01.851487Z

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 a2a9f9a6-797d-441c-a065-0f67309b9ebf · inbound

Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation cites this paper.

Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:06:29.650511Z

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 a95a305f-3762-4453-8a21-7134a1e655c8 · inbound

Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation cites this paper.

Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:07.125342Z

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 6272d759-cbc3-4095-bf0c-48fdcb8124ff · inbound

Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation cites this paper.

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:35:39.546947Z

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.