Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-29T18:17:52.158294Z
Paper Citation Record · LEDGER
Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization
As of 9 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 0 inbound Pith citation observations for arXiv:2605.26501.
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
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
5 of 5 outbound references displayed
- verified exact3
- verified fuzzy0
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7176d271-9528-4a1d-af55-a273d94059b4 · outbound
This paper cites How Robust is Google's Bard to Adversarial Image Attacks?.
Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization How Robust is Google's Bard to Adversarial Image Attacks?
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T18:23:50.907500Z
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 24ef5266-77f0-41bf-a008-7888ba977f85 · outbound
This paper cites InCVPR, 6904–6913.
Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization InCVPR, 6904–6913
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T18:17:52.158294Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 50da154a-4226-48d4-a3a9-9e4b0fb06060 · outbound
This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.
Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization Hierarchical Text-Conditional Image Generation with CLIP Latents
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T18:23:50.904906Z
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 e26aab9f-c653-43bd-9514-be1e631b06f8 · outbound
This paper cites Universal Adversarial Triggers for Attacking and Analyzing NLP.
Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization Universal Adversarial Triggers for Attacking and Analyzing NLP
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T18:23:50.902580Z
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 9afc7fd5-f3b4-4eb9-a074-bc4815ce08c1 · outbound
This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.
Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T18:23:50.899700Z
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.
Pith citing papers
No inbound Pith citation observations are available.