Paper Citation Record · LEDGER

RegCL: Continual Adaptation of Segment Anything Model via Model Merging

As of 21 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2507.12297.

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

pith.paper-citation-record.v1
2507.12297 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:56:46.641280Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:56:04.688129Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy26
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 882a25ad-0b41-4006-b364-5a89646cc85d · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging On the Opportunities and Risks of Foundation Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:46.488067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ec64dd-2938-474f-8be6-f8d0cdaf9674 · outbound

This paper cites Dark experience for general continual learning: a strong, simple baseline.NeurIPS, 2020.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Dark experience for general continual learning: a strong, simple baseline.NeurIPS, 2020

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.179483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7941d6a5-06a4-4e73-be6a-41bd1927c221 · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.164746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3db84a0c-0c65-4a8d-ab63-f7a4448582b5 · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:46.502215Z

Source-reported events for the cited work

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Observation a801e9fc-7063-4987-a375-0dd95c9ef167 · outbound

This paper cites A continual learning survey: Defying for- getting in classification tasks.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging A continual learning survey: Defying for- getting in classification tasks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.149007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 75d12da0-1dae-4dad-b061-528308738da2 · outbound

This paper cites Continual learning with tiny episodic memories.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Continual learning with tiny episodic memories

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.134192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 172da307-79f4-4bd2-8238-a937391c09ca · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging An image is worth 16x16 words: Transformers for image recognition at scale

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.119130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e37fb925-0855-46f7-a293-c3ce72b83726 · outbound

This paper cites Concealed object detection.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Concealed object detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.102779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 532f4451-725a-4923-8b61-fdf3721abcf7 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Parameter-efficient transfer learning for nlp

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.086789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d3b969a3-38da-417e-a8f5-88cc5e915f40 · outbound

This paper cites Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:46.530008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 950f648c-04ef-4ef1-8b36-6153e105793d · outbound

This paper cites Lora: Low-rank adaptation of large language models.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Lora: Low-rank adaptation of large language models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:46.534737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baf806eb-7b85-49e4-a738-fb058be909cd · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Kvasir-seg: A segmented polyp dataset

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:46.539317Z

Source-reported events for the cited work

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Observation d76f9140-a120-4bec-92cb-4e4b42e0e0e3 · outbound

This paper cites Dataless Knowledge Fusion by Merging Weights of Language Models.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:46.543501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5f7c6ea-5fbd-4839-9ccf-5a23269f79a5 · outbound

This paper cites Lifelong vision models with memory-constrained rehearsal.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Lifelong vision models with memory-constrained rehearsal

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.052026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 498d365c-df55-4e1a-878b-81a2a95474d5 · outbound

This paper cites Segment any- thing.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Segment any- thing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.035114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9811cf45-bc31-4d80-a6be-16c6db04c8a1 · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Overcoming catastrophic forgetting in neu- ral networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.020343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bdbff421-cd2c-4468-af83-16d496aba953 · outbound

This paper cites Anabranch network for camouflaged object segmentation.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Anabranch network for camouflaged object segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:47.005448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4be4b44f-43a5-4f70-a865-b3df09fd0b04 · outbound

This paper cites Federated learning: Challenges, methods, and future directions.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Federated learning: Challenges, methods, and future directions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.990243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 49de8274-66bb-459a-abe7-a3f5710d3416 · outbound

This paper cites Focal loss for dense object detection.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Focal loss for dense object detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.975471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bef27bbe-e599-4220-ac56-faedf1d4fd16 · outbound

This paper cites Continual semantic segmentation via structure preserving and projected feature alignment.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Continual semantic segmentation via structure preserving and projected feature alignment

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.960980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d7feb60c-666b-4563-a8e6-8997740835b3 · outbound

This paper cites Gradient episodic memory for continual learning.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Gradient episodic memory for continual learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.945408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b404ffd4-707f-4136-8b56-d79d6318a689 · outbound

This paper cites Merging models with fisher-weighted averaging.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Merging models with fisher-weighted averaging

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.930729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 88def1bc-c3dc-4200-a46c-e71669853cb5 · outbound

This paper cites Incremental learn- ing techniques for semantic segmentation.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Incremental learn- ing techniques for semantic segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.916111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fea7bcfb-36cc-49ad-806d-b29414a62b59 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.901602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aaba0bd7-b826-478b-9cf6-3d74a4428bbf · outbound

This paper cites icarl: Incremental classifier and representation learning.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging icarl: Incremental classifier and representation learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.887082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9e0917f1-89cd-433f-9fdd-4bda85a8310e · outbound

This paper cites Generative continual concept learning.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Generative continual concept learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.871047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 73a502ca-2648-49fc-aff0-06dd783dfeee · outbound

This paper cites Progressive Neural Networks.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Progressive Neural Networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:46.605987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95666584-3dc8-4cff-ae49-da9fac577441 · outbound

This paper cites Three scenarios for continual learning.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Three scenarios for continual learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:46.610787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c72b3509-b737-4748-b80b-54ee527db807 · outbound

This paper cites Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.855047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c01e31c8-f831-484e-b015-140c6b83729b · outbound

This paper cites Orthogonal Subspace Learning for Language Model Continual Learning.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Orthogonal Subspace Learning for Language Model Continual Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:46.619730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20a9ca02-06b5-4711-8b32-1f38b7486cda · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing in- ference time.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing in- ference time

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.838564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c60c3cea-867d-4b74-af1f-56774b403068 · outbound

This paper cites Medical sam adapter: Adapting segment anything model for medical im- age segmentation.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Medical sam adapter: Adapting segment anything model for medical im- age segmentation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.823463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f95081b4-f485-4204-ada9-e2a4ca03733f · outbound

This paper cites Ties-merging: Resolving interference when merging models.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Ties-merging: Resolving interference when merging models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.808656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6c3e4b7b-305a-4b75-9bfa-40fa6f248a16 · outbound

This paper cites Language models are super mario: Absorbing abilities from homologous models as a free lunch.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Language models are super mario: Absorbing abilities from homologous models as a free lunch

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.794222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 45fbf737-6870-4d4d-95f3-5bb524b14b01 · outbound

This paper cites Learning at a glance: Towards interpretable data-limited continual seman- tic segmentation via semantic-invariance modelling.

RegCL: Continual Adaptation of Segment Anything Model via Model Merging Learning at a glance: Towards interpretable data-limited continual seman- tic segmentation via semantic-invariance modelling

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:46.778980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 4da60712-2b09-4fe3-a3cd-331fc249aaa6 · inbound

DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation cites this paper.

DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation RegCL: Continual Adaptation of Segment Anything Model via Model Merging

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T17:56:04.688129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.