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Paper Citation Record · LEDGER

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

As of 7 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 14 inbound Pith citation observations for arXiv:2508.18966.

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

pith.paper-citation-record.v1
2508.18966 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:07:24.734639Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:20:00.972643Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:39:50.260429Z

Reference resolution

41 of 41 outbound references displayed

  • verified exact2
  • verified fuzzy6
  • unresolved33
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 053fcd0d-de62-47b0-916d-a605db7f6902 · outbound

This paper cites Re-Imagen: Retrieval-Augmented Text-to-Image Generator.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Re-Imagen: Retrieval-Augmented Text-to-Image Generator

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.622496Z digest=sha256:10ec6f06b0fc01d25a42ab82b82ffdb6aa1bc75ed4faa556547428e0f6ac61e4

Observation 146939ad-10e6-4892-b183-4b5939c1a2b2 · outbound

This paper cites Style injection in diffusion: A training-free approach for adapting large-scale diffusion models for style transfer.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Style injection in diffusion: A training-free approach for adapting large-scale diffusion models for style transfer

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.626138Z digest=sha256:9f03a32e59b49ff3e65a1b7c3ea4fd914142f816ea48b3c992c4ade13f371b80

Observation cac5d7ff-bd4b-44ed-bb72-37e7c1ad33b1 · outbound

This paper cites Emerging Properties in Unified Multimodal Pretraining.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Emerging Properties in Unified Multimodal Pretraining

Reference 3

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no resolver link, observed 2026-08-05T16:07:24.629170Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T16:07:24.629170Z digest=sha256:b0709d9c307b190504901b2bc2c742dc109e8194a9620be096a4b3b22c5f6d2d

Observation aa0d0b9e-0b1a-4750-b1b3-0ac961c6d402 · outbound

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

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Scaling rectified flow transformers for high-resolution image synthesis

Reference 4

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no resolver link, observed 2026-08-05T16:07:24.632577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.632577Z digest=sha256:31587f9be26b34e9f8ddd3071e64f1b5f65674b5dfd7e55dce112932fe1f1014

Observation 8502f226-b290-41b4-acdf-2d2f3e63ccde · outbound

This paper cites Implicit style-content separation using b-lora.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Implicit style-content separation using b-lora

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-05T16:07:25.168086Z

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.

source=pdf_text observed=2026-08-05T16:07:24.635590Z digest=sha256:d76813000d759c591a91a86885f684037ad34ba808e0c7b661b45205805fc7b9

Observation b764b2bc-0676-4f46-a51f-cc4807099491 · outbound

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

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.638386Z digest=sha256:7c196af13bb5ca8a803a97e465154bf9207262ac8657b8c2be121cda82627373

Observation 53caa1f1-ed43-4583-9ebc-95068f3f3791 · outbound

This paper cites StyleShot: A Snapshot on Any Style.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning StyleShot: A Snapshot on Any Style

Reference 7

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source=pdf_text observed=2026-08-05T16:07:24.641769Z digest=sha256:641a958286e680a787fc90620a30fe7229073a957c1a98a6b546982bcf55607f

Observation 9900a882-fd17-468c-8124-56df99c1957e · outbound

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

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.644627Z digest=sha256:278149c818a7665744c01608c04ee94162d7206fcae8a66282fca5fc9ab83d6c

Observation 16c1e1df-944e-4dd9-bed4-f60cb9e1ded4 · outbound

This paper cites In-Context LoRA for Diffusion Transformers.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning In-Context LoRA for Diffusion Transformers

Reference 9

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no resolver link, observed 2026-08-05T16:07:24.647533Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T16:07:24.647533Z digest=sha256:1d6440619f8d090dc91f8f9fa15cc47e848bbdd3c95a0347e3e48308ceb2ac67

Observation 5027f8ff-d35b-495a-bd9d-219738de35db · outbound

This paper cites Realcustom: narrowing real text word for real-time open-domain text-to-image customization.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Realcustom: narrowing real text word for real-time open-domain text-to-image customization

Reference 10

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raw_fallback, observed 2026-08-05T16:07:25.159561Z

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.

source=pdf_text observed=2026-08-05T16:07:24.650398Z digest=sha256:84ce53fa01522756d6a5d2392b0dba4c5135ae56e7d5f002a16f59f97445dc54

Observation c2e0acfd-0748-44df-97db-a65da35872ac · outbound

This paper cites Arbitrary style transfer in real-time with adaptive instance normalization.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Arbitrary style transfer in real-time with adaptive instance normalization

Reference 11

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source=pdf_text observed=2026-08-05T16:07:24.653086Z digest=sha256:65c758f7c0809acbc30016e7270f9de0d07abc4de80cac769c3f30ef432b4211

Observation 3c39c659-d8f7-488b-a300-34739d2a7963 · outbound

This paper cites Visual Style Prompting with Swapping Self-Attention.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Visual Style Prompting with Swapping Self-Attention

Reference 12

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source=pdf_text observed=2026-08-05T16:07:24.655619Z digest=sha256:7d4338a90307fb57ad21be7f07066acae786ffd8e9e0a4f27900274c190d94d1

Observation 60a6d8e9-92a7-4a64-a5f8-c28179ce2de8 · outbound

This paper cites InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity

Reference 13

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source=pdf_text observed=2026-08-05T16:07:24.658469Z digest=sha256:b17543677ae10210059ec8bb1ac19e688f65d4f116a8674072b035f013604531

Observation e7b04878-e630-431d-b924-5a6a8092eaab · outbound

This paper cites Flux: Official inference repository for flux.1 models, 2024.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Flux: Official inference repository for flux.1 models, 2024

Reference 14

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source=pdf_text observed=2026-08-05T16:07:24.661146Z digest=sha256:e0ce1d9404494569d5491d9ae0cba13cdc0d4985e1e31a613025da5e69373438

Observation 7ae95b26-2355-484f-bcf5-6ab4da9c122f · outbound

This paper cites FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space

Reference 15

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source=pdf_text observed=2026-08-05T16:07:24.663792Z digest=sha256:7b5aa596421f1de0f82968ad22d016c15885ebf04ca7b3a7cac79e8bb0e9250d

Observation 9cc647c7-a885-4534-b113-0fb7b5036a94 · outbound

This paper cites Stylestudio: Text-driven style transfer with selective control of style elements.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Stylestudio: Text-driven style transfer with selective control of style elements

Reference 16

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source=pdf_text observed=2026-08-05T16:07:24.666586Z digest=sha256:99998c728b24310c28de0492e2580c61b31b1027e3903cd9d3d00fd19431878d

Observation 67884539-b12c-4c88-a8ed-4506d23b4040 · outbound

This paper cites Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing.Advancesin Neural Information Processing Systems, 36:30146–30166, 2023.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing.Advancesin Neural Information Processing Systems, 36:30146–30166, 2023

Reference 17

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verified fuzzy
raw_fallback, observed 2026-08-05T16:07:25.137453Z

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.

source=pdf_text observed=2026-08-05T16:07:24.669116Z digest=sha256:a542d6bbce1411d2432788520900088907efcbdd9502e8a90c877cfd73d6c07c

Observation 4a4a020f-ba17-4b79-a6b0-b7c45ab3c53a · outbound

This paper cites RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models

Reference 18

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source=pdf_text observed=2026-08-05T16:07:24.671855Z digest=sha256:052edd83558cbe5409e289c7da75505f8c92bb7b392ad9a06e54745944fbeb83

Observation 05b6b423-5388-4a5b-a8a1-347d26ef34d8 · outbound

This paper cites Realcustom++: Representing images as real-word for real-time customization.arXiv preprint arXiv:2408.09744, 2024.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Realcustom++: Representing images as real-word for real-time customization.arXiv preprint arXiv:2408.09744, 2024

Reference 19

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source=pdf_text observed=2026-08-05T16:07:24.674807Z digest=sha256:a96cfbf3534c937af6bbdf1f3d4adbc9495abac64c665ce39128349bd7f24291

Observation 519fa052-f033-4e3f-8224-0bbae670b014 · outbound

This paper cites Dreamo: A unified framework for image customization.arXiv preprint arXiv:2504.16915, 2025.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Dreamo: A unified framework for image customization.arXiv preprint arXiv:2504.16915, 2025

Reference 20

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source=pdf_text observed=2026-08-05T16:07:24.677294Z digest=sha256:060d8ef4fe01655b91047a75ce1bbe6f60006305318a6deebc320b4355fa119c

Observation f48189fd-172e-4f3e-a354-81cb301dc3d4 · outbound

This paper cites SDXL: Improving latent diffusion models for high-resolution image synthesis.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning SDXL: Improving latent diffusion models for high-resolution image synthesis

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-05T16:07:25.128253Z

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.

source=pdf_text observed=2026-08-05T16:07:24.679882Z digest=sha256:31f04950fc5cef5cea0f92c3ecef90afad81165a9b3e144b88dd62ff1fe80cf4

Observation b489951c-11de-4b91-bb91-b2de6e970432 · outbound

This paper cites BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models

Reference 22

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local_arxiv, observed 2026-08-05T16:07:24.848310Z

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.

source=pdf_text observed=2026-08-05T16:07:24.682387Z digest=sha256:880d54a0149bee9d8de8f6832983bd5db407f3647f5d915890b38cff4d3b8cf9

Observation 46c47027-63f6-4c6f-8666-a2d674ebba13 · outbound

This paper cites Deadiff: An efficient stylization diffusion model with disentangled representations.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Deadiff: An efficient stylization diffusion model with disentangled representations

Reference 23

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source=pdf_text observed=2026-08-05T16:07:24.685128Z digest=sha256:3fec983c696e1747fbd468e7193d14ad3e1e88a9f5c9f3b7549878f325445d52

Observation 9aeababf-79bd-48a0-a132-72af3cdcc824 · outbound

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

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning High-resolution image synthesis with latent diffusion models

Reference 24

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source=pdf_text observed=2026-08-05T16:07:24.687669Z digest=sha256:742d015b007f01f42a9c08245353b12b06ee38b9e0d5eaf962d890b33e872bc0

Observation 7c63eb10-94e5-4e13-b913-8742fdc37524 · outbound

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

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 25

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source=pdf_text observed=2026-08-05T16:07:24.690145Z digest=sha256:b2c059d245bdab5fd8b28b3481066d9ccff1f81b98b85a35c768daf1dbad0d5f

Observation 8c76ada6-c7a6-4fe6-be94-6e90d8cfc7b5 · outbound

This paper cites Measuring Style Similarity in Diffusion Models.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Measuring Style Similarity in Diffusion Models

Reference 26

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source=pdf_text observed=2026-08-05T16:07:24.692815Z digest=sha256:95b109c230b3483619be259f74326f4600397779e17066083efa2f48df5226ba

Observation 64c788c7-30cd-47d7-b930-2f59bb5fde59 · outbound

This paper cites OminiControl: Minimal and Universal Control for Diffusion Transformer.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning OminiControl: Minimal and Universal Control for Diffusion Transformer

Reference 27

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source=pdf_text observed=2026-08-05T16:07:24.695962Z digest=sha256:38ea28b076b55f1beab8b3587cad722cb047414216ccfa576ef74440db06ce88

Observation 9c7298f5-de34-445c-b55c-ecca2a88ba55 · outbound

This paper cites InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation

Reference 28

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no resolver link, observed 2026-08-05T16:07:24.698825Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.698825Z digest=sha256:018443e7c01eab0d85aa1503f2d3a58a038a955b3529982bf9c59745fe3d79e7

Observation 593897fd-7a6c-4c3d-b121-bb1ff8c02e9c · outbound

This paper cites Omnistyle: Filtering high quality style transfer data at scale.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Omnistyle: Filtering high quality style transfer data at scale

Reference 29

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source=pdf_text observed=2026-08-05T16:07:24.701568Z digest=sha256:af602d91f10844ccb848f346e70ef6935984fd0b6d26926dffbea17ae316423d

Observation 89f1fb6e-8929-498a-bd12-5477a50256b8 · outbound

This paper cites Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-05T16:07:25.100761Z

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.

source=pdf_text observed=2026-08-05T16:07:24.704297Z digest=sha256:7c1c2851b3b1e784a63f9dad50e267af99ef29dc3370d668ac35fc6e5578fff9

Observation 9773e485-df0d-4c4d-9e6f-ef5e1af2f9fa · outbound

This paper cites Qwen-Image Technical Report.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Qwen-Image Technical Report

Reference 31

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source=pdf_text observed=2026-08-05T16:07:24.706877Z digest=sha256:8feee9fee90507e9bc5b3c49dd47e295d9807ab5523f97e4e69cd780c86b0f69

Observation bac6495b-4c04-458e-b93f-75e5e925cdec · outbound

This paper cites OmniGen2: Towards Instruction-Aligned Multimodal Generation.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning OmniGen2: Towards Instruction-Aligned Multimodal Generation

Reference 32

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source=pdf_text observed=2026-08-05T16:07:24.709716Z digest=sha256:474c58f64731fedb6e3c6ebb7491fda8daed60b5a1bd299965c94d25f1c52a0e

Observation aa3ec40c-2d58-4253-bce6-f92b281dfccb · outbound

This paper cites VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing Control.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing Control

Reference 33

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source=pdf_text observed=2026-08-05T16:07:24.712500Z digest=sha256:27ffd92cb9c783394d3a292f76fa4b01c3055f774961e7de513048da80f93a00

Observation e6420c93-3141-4cb9-bc5a-1443315e3a4b · outbound

This paper cites Less-to-More Generalization: Unlocking More Controllability by In-Context Generation.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Less-to-More Generalization: Unlocking More Controllability by In-Context Generation

Reference 34

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source=pdf_text observed=2026-08-05T16:07:24.715507Z digest=sha256:bda4d6680cd8d218043b62a5ebb923de67794e7dd2cbae3f0455f969ac57f0be

Observation c0ba062e-8ad5-4bbf-9789-47192892d7bd · outbound

This paper cites StyleAlign: Analysis and Applications of Aligned StyleGAN Models.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning StyleAlign: Analysis and Applications of Aligned StyleGAN Models

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-05T16:07:24.785290Z

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.

source=pdf_text observed=2026-08-05T16:07:24.718370Z digest=sha256:1dc9a7d381066899b0158a0b2000f6db0ae4693950774532a33777979f7cf905

Observation e5fb1619-8d3f-4ddf-9ea9-597676a3823f · outbound

This paper cites OmniGen: Unified Image Generation.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning OmniGen: Unified Image Generation

Reference 36

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no resolver link, observed 2026-08-05T16:07:24.721187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.721187Z digest=sha256:12abe8ed00fd31c99800c6fc77618bea6cc0fcc9c923836be34ebd0f66746b9c

Observation 986665d2-9465-4df2-9dcb-8fd911f9237f · outbound

This paper cites CSGO: Content-Style Composition in Text-to-Image Generation.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning CSGO: Content-Style Composition in Text-to-Image Generation

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T16:07:24.723979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.723979Z digest=sha256:26babcc96e6fb448c843debbc1cccd01c2230d1386e592ed1f1151ef974cfb74

Observation 372c0820-4c5d-4f9d-9542-43aa5a310d52 · outbound

This paper cites Imagereward: Learning and evaluating human preferences for text-to-image generation.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Imagereward: Learning and evaluating human preferences for text-to-image generation

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T16:07:24.726795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.726795Z digest=sha256:176360551f5f90ce56c2ecbc6df4822698cefd2f370577c2dd934172360af3d1

Observation ea8d63d9-5230-4548-805a-7784c90b13bf · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T16:07:24.729303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.729303Z digest=sha256:68b6b3e9b68cc99e0de29b9b3aa7b714f8399c052a7a8cf1df1196f85d6c01d3

Observation 50544936-e08e-4044-98bb-f77254976b9a · outbound

This paper cites Sigmoid loss for language image pre-training.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning Sigmoid loss for language image pre-training

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T16:07:24.732133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:07:24.732133Z digest=sha256:b449ddad95a12616db4ed97db7f9f140c3d8efe7e821a42beff7cc0282a36aad

Observation 30f54828-8d8f-4760-b35c-f4703dd850c9 · outbound

This paper cites The girl is riding a bike in the street.

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning The girl is riding a bike in the street

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:07:25.083292Z

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.

source=pdf_text observed=2026-08-05T16:07:24.734639Z digest=sha256:e76f3a5f2b955b5de812d0375ce0e59e5ec72f1eefad88387e5c0e62b9e34e35

Pith citing papers

Observation 2bae9a30-e521-4019-a44a-b1fe7a86bbf7 · inbound

EditIDv2: Editable ID Customization with Data-Lubricated ID Feature Integration for Text-to-Image Generation cites this paper.

EditIDv2: Editable ID Customization with Data-Lubricated ID Feature Integration for Text-to-Image Generation USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T05:20:00.972643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:20:00.972643Z digest=sha256:4d5fd846d331a701c643739e83ddcaf1ef5f06a9d5de52b0596f69f222378d13

Observation b453d790-b836-49af-bfa6-d49d5f7e63d5 · inbound

iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation cites this paper.

iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-04T06:47:09.609041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:47:09.609041Z digest=sha256:fb0a422d027a2090a06fc83ea9c2833b531b6f699df1851745b8191bdd23f418

Observation b23e37bb-3fe3-485a-a5ee-f839000fe832 · inbound

Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling cites this paper.

Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-16T22:41:19.268627Z

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.

source=pdf_text observed=2026-05-16T22:39:32.955779Z digest=sha256:65ce3b3dc169e78c6abaf94fdab554ccc077b33f597a3e6c0eba5d0862953b16

Observation 57de51e5-ae80-45f0-897e-310726028827 · inbound

Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling cites this paper.

Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-03T16:37:06.916771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:37:06.916771Z digest=sha256:d8643ffc9650fb65a15e464013e93ba7dcedc90f3a22946bb77dae340faed563

Observation ad4de808-44e1-453e-83fb-174180e5dfba · inbound

Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition cites this paper.

Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:51:27.672889Z

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.

source=pdf_text observed=2026-05-12T03:56:43.640874Z digest=sha256:85576736ffc0b9615ab609c8bc5693e8b6e8fd13ba4915da8b5625ad362f1797

Observation 7d859d78-5852-48be-8ed0-398e0075acd3 · inbound

Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition cites this paper.

Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:58:03.425154Z

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.

source=pdf_text observed=2026-05-14T21:56:10.499014Z digest=sha256:1ab23735b19ecc620ce33fd026451765922fad71014a8fe5bc701063334f1378

Observation 0f6c8606-c901-4891-946c-1ba2440d02bc · inbound

UniCustom: Unified Visual Conditioning for Multi-Reference Image Generation cites this paper.

UniCustom: Unified Visual Conditioning for Multi-Reference Image Generation USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-13T06:02:23.928121Z

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.

source=pdf_text observed=2026-05-13T05:53:21.851578Z digest=sha256:363cc07084eb898fb5911b3786a6552e2538e4cf493584e7b835db624b57218d

Observation d4daa348-10eb-442e-b4ea-e7eb562c1253 · inbound

UniCustom: Unified Visual Conditioning for Multi-Reference Image Generation cites this paper.

UniCustom: Unified Visual Conditioning for Multi-Reference Image Generation USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:03:03.331618Z

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.

source=pdf_text observed=2026-05-14T22:00:01.349754Z digest=sha256:8af758e8e04f321e9a76e8907769e7600a074d721253938d92209aecb0c0f764

Observation c199922f-5f35-4379-af42-98756c27559e · inbound

Lance: Unified Multimodal Modeling by Multi-Task Synergy cites this paper.

Lance: Unified Multimodal Modeling by Multi-Task Synergy USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 127

Resolution
verified exact
arxiv_id, observed 2026-05-20T11:48:15.001844Z

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.

source=pdf_text observed=2026-05-20T11:46:52.658984Z digest=sha256:5ebd5d578b2db553f0d3846a085eeeb137a9647bae2c63e9c53d36d1b3db2b9f

Observation 2093b86d-e477-4408-9173-9a1f84c6a850 · inbound

Lance: Unified Multimodal Modeling by Multi-Task Synergy cites this paper.

Lance: Unified Multimodal Modeling by Multi-Task Synergy USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 128

Resolution
verified exact
arxiv_id, observed 2026-05-21T07:59:50.549240Z

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.

source=pdf_text observed=2026-05-21T07:56:34.034047Z digest=sha256:3de7d0072dfb44939b32baadc0f8e6087e8bd9c7f5eabc3a6130da61402b73a0

Observation d4968a7d-7e35-4448-98a7-6da073e8cae5 · inbound

FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining cites this paper.

FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T03:29:31.033751Z

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.

source=arxiv_source observed=2026-06-26T17:59:00.685869Z digest=sha256:b0bf69b592a5ba04df8328ea8fac65848b0e1cc2753ddd8415a1134aa048f491

Observation 01f8e662-e1fd-4d72-92e5-1a9c12703774 · inbound

Scaling Multi-Reference Image Generation with Dynamic Reward Optimization cites this paper.

Scaling Multi-Reference Image Generation with Dynamic Reward Optimization USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-07-04T13:39:50.262085Z

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.

source=pdf_text observed=2026-06-26T05:06:12.721122Z digest=sha256:f1c4af0931de185aba0532f63f106e2c33443d2e3a750ab27e8fcfb8c45c3d62

Observation f14cd92c-223e-4886-a72d-1719f00fbffc · inbound

DreamStyle3D: Efficient 3D Stylized Asset Generation via Dual-Attention Disentanglement cites this paper.

DreamStyle3D: Efficient 3D Stylized Asset Generation via Dual-Attention Disentanglement USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 44

Resolution
unresolved
no resolver link, observed 2026-07-31T06:53:04.357097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T06:53:04.357097Z digest=sha256:deb60619119b1e560bb71f335fba3e28f8efebb31116abe057b8742cd4a140c0

Observation 34f53ee2-6478-48b0-9edb-b0b17869d422 · inbound

DreamStyle3D: Efficient 3D Stylized Asset Generation via Dual-Attention Disentanglement cites this paper.

DreamStyle3D: Efficient 3D Stylized Asset Generation via Dual-Attention Disentanglement USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-04T03:46:56.800614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T03:46:56.800614Z digest=sha256:faca0f9ccdfc921bc7b35ddfb833745b564365a81991ddf5383ea88fdaec780d