Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-29T18:51:53.451225Z
Paper Citation Record · LEDGER
As of 19 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 1 inbound Pith citation observation for arXiv:2605.26421.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-29T18:51:53.451225Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-30T11:00:39.292343Z
A source-named dated measurement, never combined with another source.
Source: cited_works
95 of 95 outbound references displayed
External citation measurements
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HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection Large Scale GAN Training for High Fidelity Natural Image Synthesis
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HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection Real-Time Deepfake Detection in the Real-World
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HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection LDR-Net: A Novel Framework for AI-generated Image Detection via Localized Discrepancy Representation
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HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection InProceedings of the Computer Vision and Pattern Recognition Conference
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HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection InProceedings of the AAAI conference on artificial intelligence, Vol
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HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection Unresolved cited work
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HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection UniEmo: Unifying Emotional Understanding and Generation with Learnable Expert Queries
Reference 95
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