Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
CLIP model is an Efficient Continual Learner
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2210.03114.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T20:22:31.499009Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T03:59:33.837802Z
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 8f2c9c74-bc4c-4997-be80-b08a9a8ef0c7 · inbound
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions cites this paper.
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions CLIP model is an Efficient Continual Learner
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T20:22:31.499009Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3f9e5698-fcfd-40e3-8ce8-461a82a9e039 · inbound
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting cites this paper.
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting CLIP model is an Efficient Continual Learner
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:41:51.815702Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dad0afd0-d03e-4af2-aa0f-a2e2978888df · inbound
ChordPrompt: Orchestrating Cross-Modal Prompt Synergy for Multi-Domain Incremental Learning in CLIP cites this paper.
ChordPrompt: Orchestrating Cross-Modal Prompt Synergy for Multi-Domain Incremental Learning in CLIP CLIP model is an Efficient Continual Learner
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:11:14.045403Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e5002491-def8-4a93-8ad4-ecdbb339d08d · inbound
Mind the Gap: Preserving and Compensating for the Modality Gap in CLIP-Based Continual Learning cites this paper.
Mind the Gap: Preserving and Compensating for the Modality Gap in CLIP-Based Continual Learning CLIP model is an Efficient Continual Learner
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:09:18.136400Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 98162c38-35b1-4fe2-b3ef-91404d25c192 · inbound
Toward Verifiable Misinformation Detection: A Multi-Tool LLM Agent Framework cites this paper.
Toward Verifiable Misinformation Detection: A Multi-Tool LLM Agent Framework CLIP model is an Efficient Continual Learner
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:41:30.942350Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a847d8b3-e4b8-4e78-9239-eb1b7e68e8fc · inbound
Continual Learning with Vision-Language Models via Semantic-Geometry Preservation cites this paper.
Continual Learning with Vision-Language Models via Semantic-Geometry Preservation CLIP model is an Efficient Continual Learner
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T18:22:15.207938Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2d0b3431-29e2-487a-8262-1a806b482c1f · inbound
GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning cites this paper.
GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning CLIP model is an Efficient Continual Learner
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T05:36:01.658161Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation baf39142-a2f6-4a52-bf45-ff44d04302e6 · inbound
AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning cites this paper.
AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning CLIP model is an Efficient Continual Learner
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T13:43:28.556535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2d988036-6022-4d01-9ab0-c881b7022af5 · inbound
Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning cites this paper.
Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning CLIP model is an Efficient Continual Learner
Reference 29
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T08:23:15.409605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e702539c-a856-4066-8ac1-14a3ccc83c8a · inbound
World Engine: Towards the Era of Post-Training for Autonomous Driving cites this paper.
World Engine: Towards the Era of Post-Training for Autonomous Driving CLIP model is an Efficient Continual Learner
Reference 51
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T03:59:33.839698Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.