Paper Citation Record · LEDGER

Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2404.18961.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2404.18961 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:28:18.679903Z

measured 0 of 1 external citation measurements

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 c18e216a-a7e9-4c69-b2cb-004ac878b532 · inbound

Stealthy Multi-Task Adversarial Attacks cites this paper.

Stealthy Multi-Task Adversarial Attacks Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T11:45:59.004958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1120ca73-18d7-4870-ba37-2d4e6685ed1a · inbound

Parameter-Efficient Interventions for Enhanced Model Merging cites this paper.

Parameter-Efficient Interventions for Enhanced Model Merging Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:06.693783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60ea414f-d84f-42a2-9742-aaebf73c4a3d · inbound

Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond cites this paper.

Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

Reference 215

Resolution: unresolved; no resolver link, observed 2026-08-10T18:53:50.756157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82f123e3-0247-471d-96b6-d323c7dbe9de · inbound

From Low Intrinsic Dimensionality to Non-Vacuous Generalization Bounds in Deep Multi-Task Learning cites this paper.

From Low Intrinsic Dimensionality to Non-Vacuous Generalization Bounds in Deep Multi-Task Learning Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T21:34:28.373059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef7ca6de-4c62-4626-8a12-d6d72e8974aa · inbound

Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution cites this paper.

Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T20:39:54.063636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 655b9283-b192-4ed8-8b5e-90796d5fa81b · inbound

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes cites this paper.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.679903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+00:00.