Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Anytime Dense Prediction with Confidence Adaptivity
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2104.00749.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:58:13.434912Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-17T02:18:02.290260Z
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 49b800e0-3099-4151-8dc2-c9bb4f6db6b4 · inbound
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models cites this paper.
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models Anytime Dense Prediction with Confidence Adaptivity
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T02:18:02.292172Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 01747405-c07b-44df-bc3c-ad66635e46a7 · inbound
AI Flow: Perspectives, Scenarios, and Approaches cites this paper.
AI Flow: Perspectives, Scenarios, and Approaches Anytime Dense Prediction with Confidence Adaptivity
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:58:13.434912Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5be22ff9-fbb4-4592-98d1-6d273aaad627 · inbound
Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs cites this paper.
Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs Anytime Dense Prediction with Confidence Adaptivity
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:33:19.899681Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.