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Paper Citation Record · LEDGER

A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2205.13218.

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

pith.paper-citation-record.v1
2205.13218 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:50:09.671394Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T00:47:30.262121Z

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 bb85a644-bb9d-470a-9744-e073784aacf8 · inbound

Sparse Orthogonal Parameters Tuning for Continual Learning cites this paper.

Sparse Orthogonal Parameters Tuning for Continual Learning A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-23T18:08:18.768054Z

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.

source=arxiv_source observed=2026-05-23T18:06:35.511653Z digest=sha256:c5a725c1b066098f2337bb76fce731c478667522490979a68e1a4c7f1c3e6e94

Observation 2a720d3d-c98c-449d-a4eb-bceebf0e7f95 · inbound

ECC-SNN: Cost-Effective Edge-Cloud Collaboration for Spiking Neural Networks cites this paper.

ECC-SNN: Cost-Effective Edge-Cloud Collaboration for Spiking Neural Networks A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T13:50:09.671394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:09.671394Z digest=sha256:16c973d7063dfc4f17e35ced30f36132bfced9bd7ec0d98335a898a8b11217db

Observation 64fd80f3-6896-4a68-9c14-d1706b4e2432 · inbound

Continual Hyperbolic Learning of Instances and Classes cites this paper.

Continual Hyperbolic Learning of Instances and Classes A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T04:26:37.743830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:26:37.743830Z digest=sha256:71d97022e64eddec5e9dc81ce723ff12ad412a3d85ebcdf338381120ed0c9968

Observation 711e7ec4-5d18-4fa3-b3eb-371b263e18e0 · inbound

Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts cites this paper.

Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 93

Resolution
verified exact
arxiv_id, observed 2026-05-22T13:06:34.461684Z

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.

source=pdf_text observed=2026-05-22T13:06:03.463520Z digest=sha256:020f43923199c774c38f7672319ffd3d51f62d3777f953223e1076e9c45febba

Observation 3f95b71c-4b3d-4681-9e2d-42729f73221f · inbound

CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning cites this paper.

CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-04T16:55:15.102431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:55:15.102431Z digest=sha256:91a9d31abe189f511b14106c770819475e0f617fefc80ec06dd1b91c3ebe69c1

Observation 278522ee-078a-4671-83f6-8d7bd19270ed · inbound

Continual Knowledge Consolidation LORA for Domain Incremental Learning cites this paper.

Continual Knowledge Consolidation LORA for Domain Incremental Learning A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-04T09:27:54.163876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T09:27:54.163876Z digest=sha256:82c5e616534282cfb99ad74c59473ea6a3f4079b1cef7c6ed0d2f95d90bcba11

Observation 554e2c0b-0074-41e2-974b-ea7dc969ab7a · inbound

Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins cites this paper.

Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 40

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T01:51:25.582792Z

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.

source=pdf_text observed=2026-05-17T01:50:47.258991Z digest=sha256:2bea15d29f33eb14a670d93fa187d5c91cb26b06db7ff7be84366a87419ff790

Observation 5e813287-b6e6-4944-b18f-5b22904e2616 · inbound

Towards Realistic Class-Incremental Learning with Free-Flow Increments cites this paper.

Towards Realistic Class-Incremental Learning with Free-Flow Increments A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 49

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T19:58:12.388911Z

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.

source=pdf_text observed=2026-05-13T19:53:25.802690Z digest=sha256:3808fb0d505d57b5b593c81df6739353e61848dcab0af128fb4e27ec0b3406ac

Observation f27522fb-ae6f-4309-b9b6-5302d764ff78 · inbound

Memory-Efficient Continual Learning with CLIP Models cites this paper.

Memory-Efficient Continual Learning with CLIP Models A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T23:41:18.832953Z

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.

source=pdf_text observed=2026-05-07T16:26:40.820540Z digest=sha256:7adeae80a0170b656af4155620f6aaa63b48ef5cca95fb0e2ed14d0d2340573d

Observation cc72b112-560e-4507-b77b-72ddc2074361 · inbound

Online Continual Learning with Dynamic Label Hierarchies cites this paper.

Online Continual Learning with Dynamic Label Hierarchies A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T06:37:26.788153Z

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.

source=pdf_text observed=2026-05-13T06:36:09.110519Z digest=sha256:6e90cacf692554ea6f27d2e0e6e499b9924acdcefcc28d2e0099c7792123e9e0

Observation ce2667a1-4e43-468b-9ad9-dd318f94405e · inbound

Beyond Point-wise Neural Collapse: A Topology-Aware Hierarchical Classifier for Class-Incremental Learning cites this paper.

Beyond Point-wise Neural Collapse: A Topology-Aware Hierarchical Classifier for Class-Incremental Learning A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T06:37:26.503861Z

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.

source=arxiv_source observed=2026-05-13T06:37:19.697078Z digest=sha256:05a23d8abc67f0eaaf67a06b1134e87cb70ef0664bcf31b1ba7130395582cd18

Observation 75d48079-5d3d-475b-9342-a39ce5ae61e6 · inbound

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers cites this paper.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T00:47:30.264532Z

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.

source=pdf_text observed=2026-06-27T17:03:01.884380Z digest=sha256:335e74819e28abf899ec1c78b8e633ce635aec35922448abd444a9821c03cc78