Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.03703.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:17:42.639213Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T05:44:20.152038Z
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 6d5edeb8-2322-4990-8eb5-32e8f712159b · inbound
MECASA: Motor Execution Classification using Additive Self-Attention for Hybrid EEG-fNIRS Data cites this paper.
MECASA: Motor Execution Classification using Additive Self-Attention for Hybrid EEG-fNIRS Data CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:17:42.639213Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77bdea01-bb04-4242-9ce3-cd24fa52e2ee · inbound
iFormer: Integrating ConvNet and Transformer for Mobile Application cites this paper.
iFormer: Integrating ConvNet and Transformer for Mobile Application CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:24:25.835655Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5fd9a8da-2df9-4d35-8bd3-9a80a736a5ba · inbound
YOLO-FireAD: Efficient Fire Detection via Attention-Guided Inverted Residual Learning and Dual-Pooling Feature Preservation cites this paper.
YOLO-FireAD: Efficient Fire Detection via Attention-Guided Inverted Residual Learning and Dual-Pooling Feature Preservation CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:49:40.032381Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2a355e30-47eb-4475-b382-2f99dd4d9bb1 · inbound
SRMambaV2: Biomimetic Attention for Sparse Point Cloud Upsampling in Autonomous Driving cites this paper.
SRMambaV2: Biomimetic Attention for Sparse Point Cloud Upsampling in Autonomous Driving CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:51:33.678962Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7daad04c-00c6-4ac8-82b8-1b84d17c6381 · inbound
Lightweight Backbone Networks Only Require Adaptive Lightweight Self-Attention Mechanisms cites this paper.
Lightweight Backbone Networks Only Require Adaptive Lightweight Self-Attention Mechanisms CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T05:44:20.157238Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.