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Paper Citation Record · LEDGER

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

As of 18 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 7 inbound Pith citation observations for arXiv:2509.06818.

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

pith.paper-citation-record.v1
2509.06818 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:06:09.138816Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:47:03.874746Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:39:50.192540Z

Reference resolution

41 of 41 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8bb42d78-3ac3-4b41-95d1-3971164f7f5b · outbound

This paper cites Improving image generation with better captions.Computer Science.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Improving image generation with better captions.Computer Science

Reference 1

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source=pdf_text observed=2026-08-04T23:06:09.023163Z digest=sha256:34d2f6baa59d9fa8941a93e45dad9f421e049fe4032a388089852c935b9ea94f

Observation 9a5ccbde-ceb7-489c-80be-9d4cdb4d35f2 · outbound

This paper cites Training diffusion models with reinforcement learning.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Training diffusion models with reinforcement learning

Reference 2

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source=pdf_text observed=2026-08-04T23:06:09.026835Z digest=sha256:69d55fba8e100ebaa28582d9251fca639abc0fe993bb13afde49ddb52ada2c46

Observation 1718ae73-4146-4f05-b6c1-ffb29316b3db · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward End-to-end object detection with transformers

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-04T23:06:09.029898Z digest=sha256:154d656cc546567cca767807bb406e5d969883bdeda2beb36c1203278ae6a140

Observation 65abac06-99d6-4a96-99e5-8bbec1b5019b · outbound

This paper cites XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation

Reference 4

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source=pdf_text observed=2026-08-04T23:06:09.033237Z digest=sha256:92e0b6d602716240017308b8cab4ce7bb2ae74a120f38e7b22dcd53b123b8f01

Observation 65e9867e-d57f-4ac7-bf4b-459c078fbebf · outbound

This paper cites UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics

Reference 5

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source=pdf_text observed=2026-08-04T23:06:09.036965Z digest=sha256:453eb2e6e57d43a55bef8006029b101c39cea070b82faa7ef87ac3da78ed41d3

Observation c831902c-e129-4fd5-a795-d93b65da2481 · outbound

This paper cites Emerging Properties in Unified Multimodal Pretraining.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Emerging Properties in Unified Multimodal Pretraining

Reference 6

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source=pdf_text observed=2026-08-04T23:06:09.040097Z digest=sha256:e738747b8f5ab3589a17ef771bb5f06219c0299414c18a3fe718b8503673206c

Observation 12133458-848c-4791-8327-74d54e51aaa0 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Scaling rectified flow transformers for high-resolution image synthesis

Reference 7

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source=pdf_text observed=2026-08-04T23:06:09.043501Z digest=sha256:c1fe2ad220814741fbb83b73568fcd96deed4a6520066db33c4fdc52d2cec3ef

Observation 6286da42-7eaf-4728-98e0-a52c43962975 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 8

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source=pdf_text observed=2026-08-04T23:06:09.046504Z digest=sha256:d547dbe4511ff59167d99ca0b100b136a6510a5b5725f6dcbc804ec1bd6eec09

Observation f352d55f-fe98-45ca-9c92-4c47774aa00d · outbound

This paper cites Pulid: Pure and lightning id customization via contrastive alignment.Advances in neural information processing systems, 37:36777–36804, 2024.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Pulid: Pure and lightning id customization via contrastive alignment.Advances in neural information processing systems, 37:36777–36804, 2024

Reference 9

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source=pdf_text observed=2026-08-04T23:06:09.049592Z digest=sha256:aa1b39e46408d96fafef08785774c84c09728e77e51f49a6ff87d7d84365b968

Observation 0b98db35-c4d9-4430-8fc0-30e88357ab27 · outbound

This paper cites Denoising diffusion probabilistic models.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Denoising diffusion probabilistic models

Reference 10

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source=pdf_text observed=2026-08-04T23:06:09.052869Z digest=sha256:5ac7085f5561e96646c83a7f6e1b9c773a0431f0f80457aba8de9c45fa54665f

Observation 5361965b-7b2c-4344-9075-c67647c21130 · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 11

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source=pdf_text observed=2026-08-04T23:06:09.055389Z digest=sha256:01fa2678ebbdb87d117f62de68da68445e3a82e2b98f27b263a60b38511210da

Observation b293daad-ff91-4de7-9806-ffa71b36f20f · outbound

This paper cites In-Context LoRA for Diffusion Transformers.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward In-Context LoRA for Diffusion Transformers

Reference 12

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source=pdf_text observed=2026-08-04T23:06:09.058285Z digest=sha256:29e426a21a4ddedca689c0fa1629da3fcfa5944c17981023924d1432020d20b9

Observation 95dfd15b-75e6-4a61-a7ad-f62aa03724fb · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Realcustom: Narrowing real text word for real-time open-domain text-to-image customization

Reference 13

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source=pdf_text observed=2026-08-04T23:06:09.061323Z digest=sha256:cb7158f494d9177241ec357e3657a405123e1c985460525a9e9e4be0d86d0ceb

Observation 641df5cb-16e5-4f72-b1ab-a54cd2f9fa0e · outbound

This paper cites Resolving multi-condition confusion for finetuning-free personalized image generation.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Resolving multi-condition confusion for finetuning-free personalized image generation

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-04T23:06:09.064045Z digest=sha256:6117b99819e06815b67ac1c50d3b95ece370d084511cbd97bc251abd4e6c857b

Observation 94800da2-0f82-4007-b5e0-f1cc9a4d2891 · outbound

This paper cites The hungarian method for the assignment problem.Naval research logistics quarterly, 2(1-2): 83–97, 1955.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward The hungarian method for the assignment problem.Naval research logistics quarterly, 2(1-2): 83–97, 1955

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-04T23:06:09.066630Z digest=sha256:89a7d3d7a81416a52cee1d15841f7de51c13e83ff2e04ce6845f4e75acb67a57

Observation 2a034d08-f13c-4c81-9e00-e5792f477f0a · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Flux: Official inference repository for flux.1 models, 2024

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-04T23:06:09.069168Z digest=sha256:78d7c33c077e288d9feaa429827383ee523983ec90d825dccb9d0c9e763dfeb9

Observation 7e226394-a749-4320-b3a0-9d8a23c70f46 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Realcustom++: Representing images as real-word for real-time customization.arXiv preprint arXiv:2408.09744, 2024

Reference 17

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source=pdf_text observed=2026-08-04T23:06:09.071731Z digest=sha256:c5297c391b5c3ab2e5c7dfb3a0380603e8ef44856e9aa8ddcc94cf042c7cc580

Observation 26afccf9-4b73-4cbf-b94d-0ac6a2965846 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Dreamo: A unified framework for image customization.arXiv preprint arXiv:2504.16915, 2025

Reference 18

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source=pdf_text observed=2026-08-04T23:06:09.074483Z digest=sha256:ae1e9e13d541ac252c17516ead945e3ae255a698234a364de130544e4aac31f3

Observation d26079d3-f9d5-4ed8-9fbf-d59a939c5b37 · outbound

This paper cites Introducing gpt-4.1 in the api, 2025.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Introducing gpt-4.1 in the api, 2025

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-04T23:06:09.077338Z digest=sha256:f92604e866e60d5bf02f27051d747e5a22631e5be8acbe8c08449e9c0125c74c

Observation 69bcd6c6-866c-4914-a8f7-32e9ccaaeee4 · outbound

This paper cites Training language models to follow instructions with human feedback.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Training language models to follow instructions with human feedback

Reference 20

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source=pdf_text observed=2026-08-04T23:06:09.080162Z digest=sha256:fbf46e3264184054005e64c6b4b426f3776448171c1ef684cf3e2c5497be385c

Observation 36cd3815-324e-4705-a20e-088d278cc7e6 · outbound

This paper cites Scalable diffusion models with transformers.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Scalable diffusion models with transformers

Reference 21

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source=pdf_text observed=2026-08-04T23:06:09.083028Z digest=sha256:4212f3610ff718076c0b188ad83c2f56072e26deb4074ba7cf72167ab04fcbc7

Observation 7a8fc27c-11e4-4ee8-9ee1-99be135d6113 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 22

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source=pdf_text observed=2026-08-04T23:06:09.085713Z digest=sha256:9879c0cbf1d8a6c6dabc0ff633ae74dafb6c502d9fd5910fc2466d281e06e009

Observation 931b262e-7efa-478f-8884-de43bff657ef · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Movie Gen: A Cast of Media Foundation Models

Reference 23

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source=pdf_text observed=2026-08-04T23:06:09.088428Z digest=sha256:2040d5f1f43daf5e917c70331fd1750766b7d625de2d8711469df0e9d21ee82e

Observation c95e369d-6562-4937-ba5a-6e389df70538 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward High-resolution image synthesis with latent diffusion models

Reference 24

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source=pdf_text observed=2026-08-04T23:06:09.091259Z digest=sha256:d9a581122e2c539071e7313d075b24ab0bfe170a735a9cff49917213ae247600

Observation 072bb970-dd9a-4cb6-b205-a2ca85c1a9e1 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 25

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source=pdf_text observed=2026-08-04T23:06:09.093917Z digest=sha256:d8dc5da70803c2859dd8981de0107d0dcd8df393ef5cad1a1751cb418011c196

Observation 0dc78ab7-fb0a-43bd-a2e0-280f84241b76 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022

Reference 26

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source=pdf_text observed=2026-08-04T23:06:09.096779Z digest=sha256:3e0450762052ea5e0589cc9243106faa0bd16e97ac1e6eca8a9be8e54cf3a03e

Observation 8ce41981-4812-4cd1-b89b-0eee9f239940 · outbound

This paper cites Diffusion model alignment using direct preference optimization.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Diffusion model alignment using direct preference optimization

Reference 27

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source=pdf_text observed=2026-08-04T23:06:09.099278Z digest=sha256:a528ea2b6c0400735db592cee509cd4b5d50cde389722417da35306dfa27c12e

Observation 16e8e5ff-39dc-427e-b9ce-4e42749b3b49 · outbound

This paper cites InstantID: Zero-shot Identity-Preserving Generation in Seconds.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward InstantID: Zero-shot Identity-Preserving Generation in Seconds

Reference 28

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source=pdf_text observed=2026-08-04T23:06:09.102025Z digest=sha256:938da6eaad5aefdab27b133dc7609b3bf2463f7137ba47c04e44f37aab2bb3d9

Observation e1af5e4c-63e2-45e3-bca6-ec8d16b5a907 · outbound

This paper cites MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 29

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source=pdf_text observed=2026-08-04T23:06:09.104752Z digest=sha256:663ab28151d96eac5427926e7520e81a83cb4f7b5fdcc631cffb54b0d4213067

Observation 5858f1fe-5da0-436c-8821-c3abac382b09 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation

Reference 30

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source=pdf_text observed=2026-08-04T23:06:09.107593Z digest=sha256:2c67430056f5b298f72d283346620b15e880047c40736fa4b86040dfbd603da5

Observation 5be15ec2-2c39-4bb6-ad8c-3a652241a015 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward OmniGen2: Towards Instruction-Aligned Multimodal Generation

Reference 31

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source=pdf_text observed=2026-08-04T23:06:09.110105Z digest=sha256:3da2f2ef87623600a317df06a8610af191dd06dfc85f042c1ee19a1f57840914

Observation e7b1079e-987c-4055-8827-e5e0b9820761 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Less-to-More Generalization: Unlocking More Controllability by In-Context Generation

Reference 32

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source=pdf_text observed=2026-08-04T23:06:09.112747Z digest=sha256:9e93465a7560d81c8f9d359fc185858c5dfbf6f55e1a737b5b7ae614239a5e34

Observation 70c6ba23-ccbb-4df0-aefc-c881ccd61ebc · outbound

This paper cites Omnigen: Unified image generation.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Omnigen: Unified image generation

Reference 33

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source=pdf_text observed=2026-08-04T23:06:09.115689Z digest=sha256:dd1739b3a30e6a7521e5a48a6037afcab07bee5609372911a3f14a071816b7b2

Observation 62ce7771-0f17-45a7-9d2e-f1e89915e9a3 · outbound

This paper cites OmniControl: Control Any Joint at Any Time for Human Motion Generation.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward OmniControl: Control Any Joint at Any Time for Human Motion Generation

Reference 34

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source=pdf_text observed=2026-08-04T23:06:09.118576Z digest=sha256:f491f2ba658f2b781b0a6663618e95c6efff60a699a43c85a1718686714ede96

Observation 5acf5283-1bd3-4560-ba02-c833488962fe · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Imagereward: Learning and evaluating human preferences for text-to-image generation

Reference 35

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source=pdf_text observed=2026-08-04T23:06:09.121638Z digest=sha256:3cd148983cc7f90b0bd081785a02ee09fd656d299dc3082345c1a9179911fa11

Observation 90c78021-d30b-476e-b4c8-42f86399d1f1 · outbound

This paper cites DanceGRPO: Unleashing GRPO on Visual Generation.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward DanceGRPO: Unleashing GRPO on Visual Generation

Reference 36

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unresolved
no resolver link, observed 2026-08-04T23:06:09.124619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:06:09.124619Z digest=sha256:904448dece4e403310d6fed719fe314a55ed6746f045434b9ef849e7a87462f1

Observation 64a04c11-9483-436d-b96d-5ea58a5234c5 · outbound

This paper cites GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation

Reference 37

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unresolved
no resolver link, observed 2026-08-04T23:06:09.127777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:06:09.127777Z digest=sha256:15af5e57ee6542796b8e71001d23c1d4bc0dd38112828b580414c27c304f35d7

Observation dc5c31ff-c6d3-44d2-8219-3010a432dbc1 · outbound

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

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 38

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unresolved
no resolver link, observed 2026-08-04T23:06:09.130481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:06:09.130481Z digest=sha256:61689f200f61fe6a261a17020dc5b4b45928ee0183d66ff3741589334efca98f

Observation f5a39c72-7dd8-4120-80f1-1724756caa7c · outbound

This paper cites Openstory: A large-scale open-domain dataset for subject-driven visual storytelling.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Openstory: A large-scale open-domain dataset for subject-driven visual storytelling

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T23:06:09.344918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-04T23:06:09.133485Z digest=sha256:4af00f6bc0b2f52a470b49f7f6fa71363d46d631fdcffdf593251e031604c694

Observation 27df826f-af33-4af3-83dd-4712351c8187 · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 40

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unresolved
no resolver link, observed 2026-08-04T23:06:09.136031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:06:09.136031Z digest=sha256:7dce94ead78155922b4cd55ffd649021198bae913b29d6cd09394cc402eaa453

Observation b9e7ac10-0b52-4b96-bd78-3571a39e0535 · outbound

This paper cites UXO Team.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward UXO Team

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T23:06:09.333887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-04T23:06:09.138816Z digest=sha256:3e0ecb2c649b7ccca6de1bb4279db299eba757aa9d69c12504ec6bbd3836a60b

Pith citing papers

Observation c491ff43-79cd-40bf-9599-455153a9b709 · inbound

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

iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

Reference 11

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unresolved
no resolver link, observed 2026-08-04T06:47:03.874746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:47:03.874746Z digest=sha256:13f7ff54f264fa86f4990ba366494e7b17fe336a5b8e06687e24e5a49c6eb28b

Observation 0e42f994-a136-4e66-a044-aeac2ad86291 · inbound

Hierarchical Concept-to-Appearance Guidance for Multi-Subject Image Generation cites this paper.

Hierarchical Concept-to-Appearance Guidance for Multi-Subject Image Generation UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-03T05:01:41.850013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T05:01:41.850013Z digest=sha256:12c181acbfa54b91b8950beb4830cf7784ae467af79bb19ac856e7c06e9da06e

Observation e2b20178-ce30-47d7-9a1e-a5e6594d7e3d · inbound

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

UniCustom: Unified Visual Conditioning for Multi-Reference Image Generation UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-13T06:02:23.962361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-13T05:53:21.851578Z digest=sha256:4b5e6df4d213e2fa4d3d2082ee2a6cb7a5a78d5b26649c52444922da87b05edb

Observation 433458d2-c72e-4a81-9044-3195a855a411 · inbound

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

UniCustom: Unified Visual Conditioning for Multi-Reference Image Generation UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:03:03.413078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-14T22:00:01.349754Z digest=sha256:6e180c88aaa60bb2784420d97bb74230519a3c34f3b1fa930a93d2cc38932439

Observation b115e978-f589-445b-bd6d-5d0034f62953 · inbound

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

Lance: Unified Multimodal Modeling by Multi-Task Synergy UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-20T11:48:14.874327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-20T11:46:52.658984Z digest=sha256:ed6c6813bd808763fd44584d269a15757e29b7affe46bf3e527975f00df868be

Observation 94e83882-5ab2-49c1-a065-6d4a07dc09ec · inbound

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

Lance: Unified Multimodal Modeling by Multi-Task Synergy UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-21T07:59:50.448844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T07:56:34.034047Z digest=sha256:ec844786e6350934730ca0fca8ad7f27c15efd05e077ea1a76361f95f0918ef8

Observation 2642bbd9-f34c-4338-a05e-ea57dd7fc408 · inbound

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

Scaling Multi-Reference Image Generation with Dynamic Reward Optimization UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-07-04T13:39:50.194247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-26T05:06:12.721122Z digest=sha256:311c54004afdbe2d529cd6699dab2070879a39c02f300b29b5d6d2c9dfdba62b