Paper Citation Record · LEDGER

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning

As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2507.13107.

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

pith.paper-citation-record.v1
2507.13107 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:35:25.128475Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:52:56.657593Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0a73e100-b026-4e2b-bb8b-f0082aabeff5 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning High- resolution image synthesis with latent diffusion models,

Reference 1

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Observation 312d7b08-758c-451a-ab37-50f41f3412a2 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 2

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Observation ab593036-6e58-4364-9a83-36be9008a319 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Scaling rectified flow transformers for high-resolution image synthesis,

Reference 3

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Observation c3639ddb-ce74-41b1-90dc-2aca08542d6e · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Photorealistic text-to-image diffusion models with deep language understanding,

Reference 4

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Observation af09e77f-f660-4c81-be28-06f46ff6c4c4 · outbound

This paper cites Denoising diffusion probabilistic models,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Denoising diffusion probabilistic models,

Reference 5

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Observation 6062ff73-4225-42d4-bec4-66422ab8b632 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 6

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Observation f3cf1193-8207-4bc8-8e9f-48d79380ce93 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation,

Reference 7

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Observation 7cfbcf71-78f6-40c8-9608-d9b6deae5d77 · outbound

This paper cites Controlling text-to-image diffusion by orthogo- nal finetuning,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Controlling text-to-image diffusion by orthogo- nal finetuning,

Reference 8

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Observation 297a831f-21da-4db2-8090-730d94cdffed · outbound

This paper cites Personalized residuals for concept-driven text-to-image generation,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Personalized residuals for concept-driven text-to-image generation,

Reference 9

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Observation ec2d21c5-d456-495a-b402-feda86967188 · outbound

This paper cites Sgdm: an adaptive style- guided diffusion model for personalized text to image generation,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Sgdm: an adaptive style- guided diffusion model for personalized text to image generation,

Reference 10

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Observation 3588cfb8-7f0b-4c7c-8fe0-809165cfc629 · outbound

This paper cites A two-stage personalized virtual try-on framework with shape control and texture guidance,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning A two-stage personalized virtual try-on framework with shape control and texture guidance,

Reference 11

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Observation fc6c60ca-a382-440e-8643-af4d2bd55ca6 · outbound

This paper cites Videodreamer: Customized multi-subject text-to-video generation with disen-mix finetuning on language-video foundation models,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Videodreamer: Customized multi-subject text-to-video generation with disen-mix finetuning on language-video foundation models,

Reference 12

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Observation 3db9f74a-771e-4f7e-8a62-88975d169ed3 · outbound

This paper cites Multi- concept customization of text-to-image diffusion,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Multi- concept customization of text-to-image diffusion,

Reference 13

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Observation d9d1bf7b-598e-45a8-8486-26b08d3a9ca6 · outbound

This paper cites SVDiff: Compact Parameter Space for Diffusion Fine-Tuning.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning SVDiff: Compact Parameter Space for Diffusion Fine-Tuning

Reference 14

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Observation dc317c8b-5920-460d-a1fc-47d513449e4c · outbound

This paper cites Animediff: Customized image generation of anime characters using diffusion model,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Animediff: Customized image generation of anime characters using diffusion model,

Reference 15

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Observation 5a928f4c-d777-49e5-8472-ee30b8e3f02d · outbound

This paper cites Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models,

Reference 16

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Observation a36a3d8b-3808-4c16-8510-af1d6e25d744 · outbound

This paper cites Orthogonal adaptation for modular customization of diffusion models,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Orthogonal adaptation for modular customization of diffusion models,

Reference 17

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Observation 9c2ee216-d17e-4d85-ad3c-945dae320e05 · outbound

This paper cites Multi-lora composition for image generation,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Multi-lora composition for image generation,

Reference 18

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Observation 577e19f4-1940-4fee-a6a1-8bead0521160 · outbound

This paper cites Multi-view user preference modeling for personalized text-to-image generation,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Multi-view user preference modeling for personalized text-to-image generation,

Reference 19

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Observation 6718d5ab-2250-4dd3-9243-47ead66dcf01 · outbound

This paper cites Continual diffusion: Continual customization of text-to-image diffusion with c-lora,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Continual diffusion: Continual customization of text-to-image diffusion with c-lora,

Reference 20

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Observation 565acb88-8ab6-4342-b298-24c919a44879 · outbound

This paper cites Create your world: Lifelong text-to-image diffusion,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Create your world: Lifelong text-to-image diffusion,

Reference 21

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Observation 47b98731-c0f7-4db3-ba0a-2f8cb5b9f6dc · outbound

This paper cites How to continually adapt text-to-image diffusion models for flexible customization?.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning How to continually adapt text-to-image diffusion models for flexible customization?

Reference 22

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Observation e22e4c87-679d-4669-ad4f-b146cc357b9a · outbound

This paper cites Conceptguard: Continual personalized text-to-image generation with forgetting and confusion mitigation,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Conceptguard: Continual personalized text-to-image generation with forgetting and confusion mitigation,

Reference 23

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Observation d88e396e-7d1d-4a69-98e9-2cdaca5e63f8 · outbound

This paper cites Catastrophic interference in connec- tionist networks: The sequential learning problem,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Catastrophic interference in connec- tionist networks: The sequential learning problem,

Reference 24

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R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning GPT-4 Technical Report

Reference 25

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R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Segment Anything

Reference 26

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This paper cites InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning

Reference 27

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Observation fac782d8-3cf9-47cd-9b3f-78ee983422f0 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 28

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Observation c87ec436-1f80-42a4-ad8a-e40d023b51fa · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning LoRA: Low-Rank Adaptation of Large Language Models

Reference 29

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Observation 1b2a4d17-1b92-4580-9c59-ecbd968ef6f3 · outbound

This paper cites Adaptive mixtures of local experts,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Adaptive mixtures of local experts,

Reference 30

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This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 31

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This paper cites Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models

Reference 32

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Observation de67dcb1-6d43-4968-a2ae-9aa076c3f821 · outbound

This paper cites Mixture of LoRA Experts.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Mixture of LoRA Experts

Reference 33

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Observation 76e6caa0-1fe9-49d7-9264-5fbbddffad19 · outbound

This paper cites Expert gate: Lifelong learning with a network of experts,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Expert gate: Lifelong learning with a network of experts,

Reference 34

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Observation ed757949-a0aa-42d0-87e0-a517f5d15e1b · outbound

This paper cites Lifelong language pretraining with distribution-specialized experts,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Lifelong language pretraining with distribution-specialized experts,

Reference 35

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Observation 3e83d980-cd01-47ca-965d-07fe1c03d77c · outbound

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

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Boosting continual learning of vision-language models via mixture-of-experts adapters,

Reference 36

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Observation 93f26c23-fe13-4185-9b29-0135d4cc8c0c · outbound

This paper cites Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts

Reference 37

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This paper cites Coin: A benchmark of continual instruction tuning for multimodel large language models,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Coin: A benchmark of continual instruction tuning for multimodel large language models,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:35:25.548718Z

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This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 39

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This paper cites An image is worth multiple words: Multi-attribute inversion for constrained text-to- image synthesis,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning An image is worth multiple words: Multi-attribute inversion for constrained text-to- image synthesis,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:35:25.384586Z

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Observation ebf47871-a821-4aee-91f1-9c128cab861d · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Overcoming catastrophic forgetting in neural networks,

Reference 41

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Observation 55b0dca0-d1fb-4fe6-87e2-f227003be9df · outbound

This paper cites Learning without forgetting,.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Learning without forgetting,

Reference 42

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This paper cites Classifier-Free Diffusion Guidance.

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning Classifier-Free Diffusion Guidance

Reference 43

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Pith citing papers

Observation 1574b754-e60f-46d0-87f4-e33f27a47af2 · inbound

Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States cites this paper.

Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning

Reference 13

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