Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
AIM: Let Any Multi-modal Large Language Models Embrace Efficient In-Context Learning
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.07588.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T23:51:14.493064Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T13:45:28.194490Z
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 2f78e198-0ccd-4c71-a409-8ae6baf53d97 · inbound
VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding cites this paper.
VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding AIM: Let Any Multi-modal Large Language Models Embrace Efficient In-Context Learning
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:51:14.493064Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 963bf08c-7699-4d14-b0ec-c5a4874f6f54 · inbound
Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks cites this paper.
Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks AIM: Let Any Multi-modal Large Language Models Embrace Efficient In-Context Learning
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:45:28.196840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.