Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.20041.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:36:36.955490Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 85f7f84a-14d3-4385-94b9-dfd096942128 · inbound
Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.
Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
Reference 190
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:36:36.955490Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4d74209a-9cf5-4e68-97fb-7456a785e78c · inbound
MagicVL-2B: Empowering Vision-Language Models on Mobile Devices with Lightweight Visual Encoders via Curriculum Learning cites this paper.
MagicVL-2B: Empowering Vision-Language Models on Mobile Devices with Lightweight Visual Encoders via Curriculum Learning Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:36:18.779333Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ff48fcbd-680d-4f9e-84fa-57a2d29b626e · inbound
EdgeFlow: Fast Cold Starts for LLMs on Mobile Devices cites this paper.
EdgeFlow: Fast Cold Starts for LLMs on Mobile Devices Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T17:05:37.471216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.