Paper Citation Record · LEDGER

Representation Forcing for Bottleneck-Free Unified Multimodal Models

As of 9 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 3 inbound Pith citation observations for arXiv:2605.31604.

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

pith.paper-citation-record.v1
2605.31604 v4

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T15:31:57.426559Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T05:45:51.915796Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T23:59:06.123206Z

Reference resolution

65 of 65 outbound references displayed

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External citation measurements

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Outbound references

Observation ee98f9b5-ddb5-4680-8fd8-3fb7b7d0803a · outbound

This paper cites Latent forcing: Reordering the diffusion trajectory for pixel-space image generation.arXiv preprint arXiv:2602.11401, 2026.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Latent forcing: Reordering the diffusion trajectory for pixel-space image generation.arXiv preprint arXiv:2602.11401, 2026

Reference 1

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Observation cf14e559-e5a4-4189-b7be-946ff2e5f16e · outbound

This paper cites Improving image generation with better captions.OpenAI Technical Report,https: // cdn.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Improving image generation with better captions.OpenAI Technical Report,https: // cdn

Reference 2

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Observation cf610772-11ae-44d0-bbc5-116cb59b4169 · outbound

This paper cites FLUX.https://github.com/black-forest-labs/flux, 2024.

Representation Forcing for Bottleneck-Free Unified Multimodal Models FLUX.https://github.com/black-forest-labs/flux, 2024

Reference 3

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Observation 79f71b40-2ea1-4a39-8f84-a480ba44c8ef · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Unsupervised learning of visual features by contrasting cluster assignments

Reference 4

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Observation 56d802b2-668a-405c-ad12-93d97afc94b1 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 5

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Observation 25fa5aff-909c-428d-b74a-cb1e89301b66 · outbound

This paper cites BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset.

Representation Forcing for Bottleneck-Free Unified Multimodal Models BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 447b46f2-0f61-4bf4-98bd-b11066588b35 · outbound

This paper cites PixArt-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis.

Representation Forcing for Bottleneck-Free Unified Multimodal Models PixArt-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 5d53b2b8-b652-48ba-9a3d-45cb3819a60d · outbound

This paper cites PixelFlow: Pixel-Space Generative Models with Flow.

Representation Forcing for Bottleneck-Free Unified Multimodal Models PixelFlow: Pixel-Space Generative Models with Flow

Reference 8

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Observation 0db96706-1a79-4764-93cd-539fa656f5d0 · outbound

This paper cites Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation d335398d-8371-4596-bf76-40b3d80154dd · outbound

This paper cites Patch n’ Pack: NaViT, a vision transformer for any aspect ratio and resolution.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Patch n’ Pack: NaViT, a vision transformer for any aspect ratio and resolution

Reference 10

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Observation 30a5e4ab-0b1a-46a2-a73a-bc2569e16f6c · outbound

This paper cites Emerging Properties in Unified Multimodal Pretraining.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Emerging Properties in Unified Multimodal Pretraining

Reference 11

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Observation b4f94780-4672-4075-936e-3be7658574c4 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Imagenet: A large-scale hierarchical image database

Reference 12

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Observation d8b9f36a-4054-4cf4-b61f-51706c7d3227 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Diffusion models beat gans on image synthesis

Reference 13

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Observation eec1c095-5b55-49ab-8e4e-e111cafe4944 · outbound

This paper cites SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture.

Representation Forcing for Bottleneck-Free Unified Multimodal Models SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture

Reference 14

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Observation a21bc18c-463d-4e49-bcd4-b73badce8fc5 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Taming transformers for high-resolution image synthesis

Reference 15

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Observation de88a2e8-26ff-40b1-9ccf-c70a500b27e8 · outbound

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

Representation Forcing for Bottleneck-Free Unified Multimodal Models Scaling rectified flow transformers for high-resolution image synthesis

Reference 16

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Observation e14f7bf3-8f66-4a5b-85b2-2ce0bb697474 · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Representation Forcing for Bottleneck-Free Unified Multimodal Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 17

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Observation eb484af2-cfc6-42d6-9414-5fafe928d9c4 · outbound

This paper cites Smith, Wei-Chiu Ma, and Ranjay Krishna.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Smith, Wei-Chiu Ma, and Ranjay Krishna

Reference 18

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Observation 718affd4-fede-46e9-bdcc-0e395725a9a3 · outbound

This paper cites Seedream 3.0 Technical Report.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Seedream 3.0 Technical Report

Reference 19

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Observation ac8fcb53-ab2b-476c-bab2-ffa7d099b020 · outbound

This paper cites SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation.

Representation Forcing for Bottleneck-Free Unified Multimodal Models SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 6a601bf0-b870-4cfc-b0e4-8e260a6ddac7 · outbound

This paper cites GenEval: An object-focused framework for evaluating text-to-image alignment.

Representation Forcing for Bottleneck-Free Unified Multimodal Models GenEval: An object-focused framework for evaluating text-to-image alignment

Reference 21

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Observation a814cb94-4f9f-4d2f-b4ba-5aeac8e4843d · outbound

This paper cites HallusionBench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models.

Representation Forcing for Bottleneck-Free Unified Multimodal Models HallusionBench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models

Reference 22

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Observation 6a7d2f00-d00b-47bb-8e01-b8369cc96e3a · outbound

This paper cites Denoising diffusion probabilistic models.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Denoising diffusion probabilistic models

Reference 23

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Observation a4636529-06f0-4b30-82be-b768c435c7ca · outbound

This paper cites Simpler diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Simpler diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation c01d60d0-3499-421c-a9b4-401464a2dd2d · outbound

This paper cites ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.

Representation Forcing for Bottleneck-Free Unified Multimodal Models ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Reference 25

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Observation 9fb6db5a-f2d2-4a8f-89a8-d34761787717 · outbound

This paper cites A diagram is worth a dozen images.

Representation Forcing for Bottleneck-Free Unified Multimodal Models A diagram is worth a dozen images

Reference 26

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Observation 4811a82a-5758-450f-a856-b6b93d408c76 · outbound

This paper cites Auto-Encoding Variational Bayes.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Auto-Encoding Variational Bayes

Reference 27

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Observation 67c35343-616c-4e20-9071-2d5de573cb16 · outbound

This paper cites Back to Basics: Let Denoising Generative Models Denoise.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Back to Basics: Let Denoising Generative Models Denoise

Reference 28

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Observation 9a6fff01-7a55-423e-ac93-0548897990aa · outbound

This paper cites UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation.

Representation Forcing for Bottleneck-Free Unified Multimodal Models UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation

Reference 29

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Observation 9ba1cf97-7cc6-4897-9298-0e9016bd9db9 · outbound

This paper cites World Model on Million-Length Video And Language With Blockwise RingAttention.

Representation Forcing for Bottleneck-Free Unified Multimodal Models World Model on Million-Length Video And Language With Blockwise RingAttention

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 3fd2ec9a-f921-45db-a224-2f68be5cd6e7 · outbound

This paper cites Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 0e76227a-78c1-416b-82bc-04b4366fe459 · outbound

This paper cites Decoupled weight decay regularization.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Decoupled weight decay regularization

Reference 32

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Observation eeeff4de-3610-4171-b3a1-0c16591adb5a · outbound

This paper cites JanusFlow: Harmonizing autoregression and rectified flow for unified multimodal understanding and generation.

Representation Forcing for Bottleneck-Free Unified Multimodal Models JanusFlow: Harmonizing autoregression and rectified flow for unified multimodal understanding and generation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 91b641b2-8468-4a4b-a317-9af22353db9e · outbound

This paper cites ChartQA: A benchmark for question answering about charts with visual and logical reasoning.

Representation Forcing for Bottleneck-Free Unified Multimodal Models ChartQA: A benchmark for question answering about charts with visual and logical reasoning

Reference 34

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Observation 4c9a5c00-a365-4c3e-89fc-85f4d9e504a0 · outbound

This paper cites DocVQA: A Dataset for VQA on Document Images.

Representation Forcing for Bottleneck-Free Unified Multimodal Models DocVQA: A Dataset for VQA on Document Images

Reference 35

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Observation 37c5734d-1b23-4b88-aa57-a2b6e51cc4ac · outbound

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Representation Forcing for Bottleneck-Free Unified Multimodal Models Unresolved cited work

Reference 36

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Observation 7ea7e89a-6461-42b2-a2ef-8686c26b6ca3 · outbound

This paper cites Transfer between Modalities with MetaQueries.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Transfer between Modalities with MetaQueries

Reference 37

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Observation 67dcfd7d-4c5d-4302-a93e-d42d9bc1a14d · outbound

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

Representation Forcing for Bottleneck-Free Unified Multimodal Models SDXL: Improving latent diffusion models for high-resolution image synthesis

Reference 38

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Observation b2952c8b-a3dd-46e8-8661-f7d6ecac24fd · outbound

This paper cites Du, Zehuan Yuan, and Xinglong Wu.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Du, Zehuan Yuan, and Xinglong Wu

Reference 39

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Observation 7c2ba7e0-ee72-4f80-b79f-6154a7ac1ace · outbound

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

Representation Forcing for Bottleneck-Free Unified Multimodal Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 40

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Observation 3a187d1e-824f-4c04-b8ac-fee29126b089 · outbound

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

Representation Forcing for Bottleneck-Free Unified Multimodal Models High-resolution image synthesis with latent diffusion models

Reference 41

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Observation d6f43eeb-99ce-4e5a-8ad5-2266efe89ae6 · outbound

This paper cites Latent diffusion model without variational autoencoder.arxiv: 2510.15301, 2025.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Latent diffusion model without variational autoencoder.arxiv: 2510.15301, 2025

Reference 42

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Observation b414f980-526f-4589-83dc-c9fd71210dbb · outbound

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Representation Forcing for Bottleneck-Free Unified Multimodal Models DINOv3

Reference 43

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Observation 0c96917a-63ba-45b4-b2bb-433680bbc4f1 · outbound

This paper cites Generative multimodal models are in-context learners.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Generative multimodal models are in-context learners

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 5109de35-9c1f-482e-88e6-7ff8e1a1b3da · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Representation Forcing for Bottleneck-Free Unified Multimodal Models SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 6bf52a3e-9c07-4463-ad59-885d32286853 · outbound

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Representation Forcing for Bottleneck-Free Unified Multimodal Models Neural discrete representation learning

Reference 46

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This paper cites ILLUME: Illuminating your LLMs to see, draw, and self-enhance.

Representation Forcing for Bottleneck-Free Unified Multimodal Models ILLUME: Illuminating your LLMs to see, draw, and self-enhance

Reference 47

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Observation 3963e05a-db2e-43bd-9c65-33860daaead3 · outbound

This paper cites PixNerd: Pixel Neural Field Diffusion.

Representation Forcing for Bottleneck-Free Unified Multimodal Models PixNerd: Pixel Neural Field Diffusion

Reference 48

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Representation Forcing for Bottleneck-Free Unified Multimodal Models Emu3: Next-Token Prediction is All You Need

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 1f76f380-4e7a-4895-b126-660d9eb3c36a · outbound

This paper cites Cubic discrete diffusion: Discrete visual generation on high-dimensional representation tokens.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Cubic discrete diffusion: Discrete visual generation on high-dimensional representation tokens

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-12T15:31:57.426559Z

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Observation 1a48579d-3f14-4131-9451-fec5fa3acfdc · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Wan: Open and Advanced Large-Scale Video Generative Models

Reference 51

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Observation df4b6c76-3515-4b4c-9fa5-74e03582d2c2 · outbound

This paper cites Qwen-Image Technical Report.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Qwen-Image Technical Report

Reference 52

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Observation ae0397fa-b5f3-465a-a96c-f989e40ee16d · outbound

This paper cites Janus: Decoupling visual encoding for unified multimodal understanding and generation.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Janus: Decoupling visual encoding for unified multimodal understanding and generation

Reference 53

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Observation bead864d-e8cf-4f43-97e8-96de1d0b857a · outbound

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

Representation Forcing for Bottleneck-Free Unified Multimodal Models OmniGen2: Towards Instruction-Aligned Multimodal Generation

Reference 54

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Observation a8d0f5fe-c403-4420-bd67-7b0e4a4102be · outbound

This paper cites RealWorldQA.https://huggingface.co/datasets/xai-org/RealworldQA, 2024.

Representation Forcing for Bottleneck-Free Unified Multimodal Models RealWorldQA.https://huggingface.co/datasets/xai-org/RealworldQA, 2024

Reference 55

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Observation 1302f11a-3c2e-4776-9e08-48ca82a1c58b · outbound

This paper cites Show-o: One single transformer to unify multimodal understanding and generation.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Show-o: One single transformer to unify multimodal understanding and generation

Reference 56

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Observation d952adf4-9e74-4cae-865f-abd790f17f38 · outbound

This paper cites Show-o2: Improved native unified multimodal models.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Show-o2: Improved native unified multimodal models

Reference 57

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This paper cites Qwen3 Technical Report.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Qwen3 Technical Report

Reference 58

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This paper cites Context Unrolling in Omni Models.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Context Unrolling in Omni Models

Reference 59

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This paper cites Representation alignment for generation: Training diffusion transformers is easier than you think.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Representation alignment for generation: Training diffusion transformers is easier than you think

Reference 60

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This paper cites MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert AGI.

Representation Forcing for Bottleneck-Free Unified Multimodal Models MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert AGI

Reference 61

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This paper cites Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

Reference 62

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

Representation Forcing for Bottleneck-Free Unified Multimodal Models Sigmoid loss for language image pre-training

Reference 63

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Representation Forcing for Bottleneck-Free Unified Multimodal Models Diffusion Transformers with Representation Autoencoders

Reference 64

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This paper cites Transfusion: Predict the next token and diffuse images with one multi-modal model.

Representation Forcing for Bottleneck-Free Unified Multimodal Models Transfusion: Predict the next token and diffuse images with one multi-modal model

Reference 65

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Pith citing papers

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LLM can Read Spectrogram: Encoder-free Speech-Language Modeling cites this paper.

LLM can Read Spectrogram: Encoder-free Speech-Language Modeling Representation Forcing for Bottleneck-Free Unified Multimodal Models

Reference 25

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Observation 921868c2-f87e-478e-92ee-4b377756fa90 · inbound

LLM can Read Spectrogram: Encoder-free Speech-Language Modeling cites this paper.

LLM can Read Spectrogram: Encoder-free Speech-Language Modeling Representation Forcing for Bottleneck-Free Unified Multimodal Models

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-03T23:59:06.125682Z

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dRAE: Representation Autoencoder with Hyper-Spherical Codes cites this paper.

dRAE: Representation Autoencoder with Hyper-Spherical Codes Representation Forcing for Bottleneck-Free Unified Multimodal Models

Reference 59

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