Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.18961.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:35:19.042575Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T13:41:25.344405Z
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 8ef62c0a-4e19-4369-8c3a-cd2f698647d9 · inbound
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation cites this paper.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
Reference 69
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:35:19.042575Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0c82c02b-2987-4871-a904-3b09477e5c5a · inbound
Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation cites this paper.
Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:41:25.348027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b2bf6144-67a4-4d12-8896-0a8a62e8b1b6 · inbound
EarthSight: A Distributed Framework for Low-Latency Satellite Intelligence cites this paper.
EarthSight: A Distributed Framework for Low-Latency Satellite Intelligence Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T21:50:19.383592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5daba6f5-a624-4255-a9ad-63b846591a0d · inbound
FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning cites this paper.
FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:31:25.513883Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.