Paper Citation Record · LEDGER

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning

As of 3 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 2 inbound Pith citation observations for arXiv:2605.09640.

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

pith.paper-citation-record.v1
2605.09640 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T04:39:55.109863Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T16:22:51.163432Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

68 of 68 outbound references displayed

verified exact18
verified fuzzy50
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 18afadb4-a164-49c8-96ac-feb9b97819cf · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.727720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7adff334-b17e-4b2e-8e2d-080e028843a5 · outbound

This paper cites Qwen3-VL Technical Report.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Qwen3-VL Technical Report

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.748860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 95d74c2c-fd9b-462f-891e-7549a93c223b · outbound

This paper cites Seed1.5-VL Technical Report.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Seed1.5-VL Technical Report

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.709342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8c8c5eaa-22d0-4694-8ada-57f9844857de · outbound

This paper cites Qwen3.5-Omni Technical Report.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Qwen3.5-Omni Technical Report

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.578255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 31313633-618f-4adb-8f29-4b898ff8a20d · outbound

This paper cites A Survey of Reinforcement Learning for Large Reasoning Models.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning A Survey of Reinforcement Learning for Large Reasoning Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:02:25.511734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 626ca742-df9e-4adf-a9c0-7e63dbee3c2d · outbound

This paper cites Leveraging verifier-based reinforcement learning in image editing.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Leveraging verifier-based reinforcement learning in image editing

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.936860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 60428638-6ce2-4090-8dcb-5a84e89609de · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.609214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a13577ed-0895-4449-9205-7d731d91066f · outbound

This paper cites Deepseek-r1 incentivizes reasoning in llms through reinforcement learning.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Deepseek-r1 incentivizes reasoning in llms through reinforcement learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.965772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c37a1464-45e8-449c-8df9-b745bbc5c097 · outbound

This paper cites DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.668623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 9e3e5988-6b71-4cc8-bbd8-b7fbc850f6b2 · outbound

This paper cites Visual-rft: Visual reinforcement fine-tuning.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Visual-rft: Visual reinforcement fine-tuning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.928072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 80c28f4e-d809-4023-a48d-10f085f9fd3a · outbound

This paper cites To think or not to think: A study of thinking in rule-based visual reinforcement fine-tuning.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning To think or not to think: A study of thinking in rule-based visual reinforcement fine-tuning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.989364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f745aed5-1015-4d50-b581-69a86f914e53 · outbound

This paper cites Reason-rft: Reinforcement fine-tuning for visual reasoning of vision language models.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Reason-rft: Reinforcement fine-tuning for visual reasoning of vision language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.901304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f35bcf4f-a4fc-4426-945c-1ddf2be2caf4 · outbound

This paper cites Fine-r1: Make multi-modal llms excel in fine-grained visual recognition by chain-of-thought reasoning.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Fine-r1: Make multi-modal llms excel in fine-grained visual recognition by chain-of-thought reasoning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.974327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 20542c1a-48cf-49ab-9985-1b814a832e71 · outbound

This paper cites A survey on ensemble learning for data stream classification.ACM Computing Surveys, 50(2):1–36.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning A survey on ensemble learning for data stream classification.ACM Computing Surveys, 50(2):1–36

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.942148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 13e96e4a-0373-4ffc-80d8-5bf68f5f1664 · outbound

This paper cites Towards lifelong learning of large language models: A survey.ACM Computing Surveys, 57(8):1–35.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Towards lifelong learning of large language models: A survey.ACM Computing Surveys, 57(8):1–35

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.874035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 150a2d21-4779-4f00-a264-44d57bd3e6f5 · outbound

This paper cites Continual learning of large language models: A comprehensive survey.ACM Computing Surveys, 58(5):1–42.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Continual learning of large language models: A comprehensive survey.ACM Computing Surveys, 58(5):1–42

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.862045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d5831380-8e69-4ada-9f47-25a848829f51 · outbound

This paper cites Recent advances of foundation language models-based continual learning: A survey.ACM Computing Surveys, 57(5):1–38.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Recent advances of foundation language models-based continual learning: A survey.ACM Computing Surveys, 57(5):1–38

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.871171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e917a654-9219-4132-b029-3af3a87de8e2 · outbound

This paper cites Continual instruction tuning for large multimodal models.IEEE Transactions on Image Processing.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Continual instruction tuning for large multimodal models.IEEE Transactions on Image Processing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.939489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f0935907-f5e6-4b24-98a9-88de95cc85d9 · outbound

This paper cites Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-30T03:18:04.881731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3348be43-cb48-4ec9-bee0-67cc0e605f5e · outbound

This paper cites RL’s razor: Why online reinforcement learning forgets less.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning RL’s razor: Why online reinforcement learning forgets less

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.917691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation fd816cb6-dfb9-4b61-95d3-51ad07969115 · outbound

This paper cites Class-incremental learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9851–9873.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Class-incremental learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9851–9873

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.896036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2c7ea89b-9c6d-4d03-9c37-4c6c7269dbef · outbound

This paper cites A comprehensive survey of continual learning: Theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(8):5362–5383.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning A comprehensive survey of continual learning: Theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(8):5362–5383

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.983760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 0dc37fea-5223-47d1-9250-6725b1a5f503 · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.904377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 9c7cb375-0223-4f7a-b9e5-e67889da338e · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.630410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f9af5f01-8f75-4846-9117-728931cd630e · outbound

This paper cites OpenAI o1 System Card.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning OpenAI o1 System Card

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.616738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b3cc52c3-da27-47bb-b858-d0c8ae8ce930 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Proximal Policy Optimization Algorithms

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.697353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation fa11bbfb-9572-483e-b263-b96559098ef3 · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.851056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 81e5d001-a7e8-4ff0-8a35-6b7187df02fa · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.Advances in neural information processing systems, 36:53728–53741.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Direct preference optimization: Your language model is secretly a reward model.Advances in neural information processing systems, 36:53728–53741

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.858688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 802836fb-1a80-4093-988f-28ffce957c8d · outbound

This paper cites Safe rlhf: Safe reinforcement learning from human feedback.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Safe rlhf: Safe reinforcement learning from human feedback

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.950168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6b0208df-085f-485d-8f0b-4f4c5f99d97f · outbound

This paper cites Understanding r1-zero-like training: A critical perspective.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Understanding r1-zero-like training: A critical perspective

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.912291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ae7c8a15-4e61-4d48-ae45-fbb1b2749213 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.640252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation faded145-5811-42d5-8f90-29153474312c · outbound

This paper cites Group Sequence Policy Optimization.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Group Sequence Policy Optimization

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.602135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e83f8d07-d2e5-4f4e-ac4b-e4c8f5a1dd19 · outbound

This paper cites Onethinker: All-in-one reasoning model for image and video.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Onethinker: All-in-one reasoning model for image and video

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.934174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 65b2d924-8d9a-43a0-bb70-b8cb736089d3 · outbound

This paper cites Learning to prompt for continual learning.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Learning to prompt for continual learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.971348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4b863012-c640-4925-bd91-76c8e8fe54e5 · outbound

This paper cites Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need.International Journal of Computer Vision, 133(3):1012–1032.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need.International Journal of Computer Vision, 133(3):1012–1032

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.915094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c539d52d-57b9-4a22-a6c9-db39a913a428 · outbound

This paper cites S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning.Advances in Neural Information Processing Systems, 35:5682–5695.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning.Advances in Neural Information Processing Systems, 35:5682–5695

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.868395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 37d965c0-1d9f-468e-8310-caa09a2edc8c · outbound

This paper cites Non-exemplar domain incremental learning via cross-domain concept integration.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Non-exemplar domain incremental learning via cross-domain concept integration

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.890792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1803ab83-d078-489e-8573-873929ff4cbf · outbound

This paper cites Continual learning with pre-trained models: a survey.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Continual learning with pre-trained models: a survey

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.960045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ccca567a-2aab-4ee3-ac68-62231c362356 · outbound

This paper cites Scaling continual learning to 300+ tasks with bi-level routing mixture-of-experts.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Scaling continual learning to 300+ tasks with bi-level routing mixture-of-experts

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.947411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d7cadf52-6103-4ef3-979c-deb9c22e8b22 · outbound

This paper cites Inflora: Interference-free low-rank adaptation for continual learning.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Inflora: Interference-free low-rank adaptation for continual learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.893656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4323e0dd-06f0-4bef-8956-b16f4c901177 · outbound

This paper cites Boosting continual learning of vision-language models via mixture-of-experts adapters.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Boosting continual learning of vision-language models via mixture-of-experts adapters

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.977682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5ae2b456-03a6-4627-b68a-54786d847a82 · outbound

This paper cites Integrating task-specific and universal adapters for pre-trained model-based class-incremental learning.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Integrating task-specific and universal adapters for pre-trained model-based class-incremental learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.925432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation cb55bc0f-04fb-4015-aef3-09193c82da8c · outbound

This paper cites Mos: Model surgery for pre-trained model-based class-incremental learning.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Mos: Model surgery for pre-trained model-based class-incremental learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.898553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e2ef2a40-824b-4742-8a10-c730924d8885 · outbound

This paper cites Dual consolidation for pre- trained model-based domain-incremental learning.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Dual consolidation for pre- trained model-based domain-incremental learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.876808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c4ea7d2b-be8e-4a4c-aa31-d62183348c70 · outbound

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

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.882813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b9de997b-af6d-4224-a551-ce3e43c0f8db · outbound

This paper cites Learning transferable visual models from natural language supervision.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Learning transferable visual models from natural language supervision

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.854766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a0759d16-25ee-4628-971e-86d3623385de · outbound

This paper cites On-policy distillation of language models: Learning from self-generated mistakes.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning On-policy distillation of language models: Learning from self-generated mistakes

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.885236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 11df1713-e974-498b-adb6-ed49b8c69f7b · outbound

This paper cites Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-16T05:11:07.754859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation fe577b49-ff35-4641-a94f-94b1eb5b4329 · outbound

This paper cites Natural adversarial examples.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Natural adversarial examples

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.955143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b3c50211-fc77-4cfe-8e6e-a0ae25bc6833 · outbound

This paper cites Tiny imagenet visual recognition challenge.CS 231N, 7(7):3.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Tiny imagenet visual recognition challenge.CS 231N, 7(7):3

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.980684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c9c6ee55-d6c6-4c72-aabc-575fc9782471 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning The caltech-ucsd birds-200-2011 dataset

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.986433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3394ae1e-e460-4ae6-a2cf-bf5abbaca2e1 · outbound

This paper cites Few-shot class-incremental learning for classification and object detection: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(4):2924–2945.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Few-shot class-incremental learning for classification and object detection: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(4):2924–2945

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.888107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 163e31eb-88a4-4d7b-b166-b0a4a4efc2a6 · outbound

This paper cites Riemannian walk for incremental learning: Understanding forgetting and intransigence.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Riemannian walk for incremental learning: Understanding forgetting and intransigence

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.920372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4d1c8a56-ad27-42ac-91f1-8f69b06d5e0b · outbound

This paper cites Qwen3 Technical Report.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Qwen3 Technical Report

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.736496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c667a5c4-ff63-4a5e-ab10-cb76fa00c817 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.Proceedings of the National Academy of Sciences, 114(13):3521–3526.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Overcoming catastrophic forgetting in neural networks.Proceedings of the National Academy of Sciences, 114(13):3521–3526

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.907103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8b9bcd29-620f-4968-82e6-3cf6dac50e05 · outbound

This paper cites Learning without forgetting.IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12):2935–2947.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Learning without forgetting.IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12):2935–2947

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.952812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 118edcdd-2d9f-43ec-920b-0a2ad3bf6b51 · outbound

This paper cites Microsoft coco: Common objects in context.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Microsoft coco: Common objects in context

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.909686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3c1c71ca-bc32-4860-ba38-81fb8fe0f70c · outbound

This paper cites Feature pyramid networks for object detection.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Feature pyramid networks for object detection

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.957622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a67268a8-3fa0-4a61-b3d3-bcad568562ff · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.683680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e56c33ab-6d5b-4ee0-ab18-dc92f743d392 · outbound

This paper cites The Kinetics Human Action Video Dataset.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning The Kinetics Human Action Video Dataset

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:01:25.717344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 973af236-50fb-4c86-88be-5befce032fc4 · outbound

This paper cites Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.968510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2761b33e-2c8c-4d9d-a98f-4f4f6a8c84b6 · outbound

This paper cites Moment matching for multi-source domain adaptation.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Moment matching for multi-source domain adaptation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.879958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 145c4a8e-b544-44d9-ae2f-68bd39b65594 · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Deep hashing network for unsupervised domain adaptation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.963074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1df8657c-69a9-45c5-8e48-7dc5957e6e40 · outbound

This paper cites Cross-domain weakly- supervised object detection through progressive domain adaptation.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Cross-domain weakly- supervised object detection through progressive domain adaptation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.922827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ab10460c-5b7c-45a8-aa03-7040eb0c05fa · outbound

This paper cites The pascal visual object classes challenge: A retrospective.International Journal of Computer Vision, 111(1):98–136.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning The pascal visual object classes challenge: A retrospective.International Journal of Computer Vision, 111(1):98–136

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.865434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ac0f495c-0c62-4ef9-9b20-0d1df2c6c678 · outbound

This paper cites Three types of incremental learning.Nature Machine Intelligence, 4(12):1185–1197.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Three types of incremental learning.Nature Machine Intelligence, 4(12):1185–1197

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.931445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3e979672-7980-4b85-ae76-eb7681772ed0 · outbound

This paper cites End-to-end object detection with transformers.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning End-to-end object detection with transformers

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T14:06:36.944635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 94f053e3-2f0f-436d-9c8b-90a7dc05d748 · outbound

This paper cites Soft Adaptive Policy Optimization.

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning Soft Adaptive Policy Optimization

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:14:32.897338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

Observation fa34ff52-ad61-4b64-b772-5cb3f994aac2 · inbound

RL Forgets! Towards Continual Policy Optimization cites this paper.

RL Forgets! Towards Continual Policy Optimization Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-11T19:46:20.975419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06429ede-4f2f-4539-88f7-96b943a6ce5c · inbound

RL Forgets! Towards Continual Policy Optimization cites this paper.

RL Forgets! Towards Continual Policy Optimization Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T16:22:51.163432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.