Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.11435.
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 2026-08-07T00:40:33.146095Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T00:55:35.369724Z
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 e815d805-3e6b-4730-ac3d-2c1b2306718b · inbound
Continual Learning for Generative AI: From LLMs to MLLMs and Beyond cites this paper.
Continual Learning for Generative AI: From LLMs to MLLMs and Beyond InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
Reference 206
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:40:33.146095Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3472e23-18fd-4c6e-86dc-2b8e9399d0ec · inbound
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach cites this paper.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:56.244506Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0f26cd18-3ee1-4528-862d-dc756cd84d06 · inbound
Routing-Based Continual Learning for Multimodal Large Language Models cites this paper.
Routing-Based Continual Learning for Multimodal Large Language Models InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
Reference 66
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T00:55:35.371979Z
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.