Paper Citation Record · LEDGER

Fine-tuned Language Models are Continual Learners

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2205.12393.

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

pith.paper-citation-record.v1
2205.12393 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:55:45.869630Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

7
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 088d001f-b535-4102-a070-ac335ce20e9c · inbound

Galactica: A Large Language Model for Science cites this paper.

Galactica: A Large Language Model for Science Fine-tuned Language Models are Continual Learners

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:53:21.959267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63171207-1059-4f26-bec0-72c3482cce06 · inbound

Galactica: A Large Language Model for Science cites this paper.

Galactica: A Large Language Model for Science Fine-tuned Language Models are Continual Learners

Reference 231

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:53:22.247054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fef3287f-2ff2-486b-a3b9-367890187930 · inbound

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey cites this paper.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Fine-tuned Language Models are Continual Learners

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-11T12:46:50.261759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afbcb2d5-bdaa-489d-8914-ea1258c57fc7 · inbound

Full-Parameter Continual Pretraining of Gemma2: Insights into Fluency and Domain Knowledge cites this paper.

Full-Parameter Continual Pretraining of Gemma2: Insights into Fluency and Domain Knowledge Fine-tuned Language Models are Continual Learners

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:45.869630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 876fcb0d-c7a0-4449-8848-27872f3580e3 · 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 Fine-tuned Language Models are Continual Learners

Reference 182

Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:31.412869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69806bc6-14b8-432a-a742-e40cacc1d830 · inbound

Teaching a Language Model to Speak the Language of Tools cites this paper.

Teaching a Language Model to Speak the Language of Tools Fine-tuned Language Models are Continual Learners

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:48:45.557004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac7c0b51-1edd-4d8f-b8b4-268c5c654326 · inbound

A Simple Baseline for Stable and Plastic Neural Networks cites this paper.

A Simple Baseline for Stable and Plastic Neural Networks Fine-tuned Language Models are Continual Learners

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T17:42:27.048832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6af2b75-2fc6-4134-a5d9-3a469c5fa4d6 · 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 Fine-tuned Language Models are Continual Learners

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-05T21:04:55.994653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f474f86c-d755-46a3-bd24-4ee8b44f878a · inbound

Mitigating Catastrophic Forgetting in Continual Learning through Model Growth cites this paper.

Mitigating Catastrophic Forgetting in Continual Learning through Model Growth Fine-tuned Language Models are Continual Learners

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T12:49:51.260602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7094a6e7-9ece-4101-bf2e-18381f212eba · inbound

Learning to Discover at Test Time cites this paper.

Learning to Discover at Test Time Fine-tuned Language Models are Continual Learners

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-16T05:16:04.212044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 430cc406-25a6-4570-bdbb-387d0dce9743 · inbound

Robust Policy Optimization to Prevent Catastrophic Forgetting cites this paper.

Robust Policy Optimization to Prevent Catastrophic Forgetting Fine-tuned Language Models are Continual Learners

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-16T05:37:24.187688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.