{"total":30,"items":[{"citing_arxiv_id":"2606.27313","ref_index":9,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ViQ: Text-Aligned Visual Quantized Representations at Any Resolution","primary_cat":"cs.CV","submitted_at":"2026-06-25T17:29:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ViQ is a new two-stage text-aligned quantization method for visual features supporting arbitrary resolutions that claims competitive multimodal performance with efficiency gains of 20-70%.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.28398","ref_index":22,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Semantic-Aware Generative Image Transmission for Resource-Constrained Visual IoT Systems","primary_cat":"cs.CV","submitted_at":"2026-06-24T08:18:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A token-selection framework fuses semantic importance and recoverability estimates to transmit fewer bits while achieving competitive PSNR and better task preservation than baselines in IoT visual links.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23041","ref_index":29,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SPAR: Semantic-Pixel Self-Alignment and Adaptive Routing for Unified Multimodal Models","primary_cat":"cs.CV","submitted_at":"2026-06-22T08:48:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SPAR introduces a semantic-pixel self-alignment tokenizer and dynamic token routing to create a unified multimodal 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benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.26089","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Channel-wise Vector Quantization","primary_cat":"cs.CV","submitted_at":"2026-05-25T17:52:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CVQ replaces patch-wise vector quantization with channel-wise quantization of feature maps, enabling a next-channel autoregressive model that reports 100% codebook utilization and text-to-image scores of DPG 86.7 and GenEval 0.79.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.25012","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation","primary_cat":"cs.CV","submitted_at":"2026-05-24T11:32:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LEASE achieves state-of-the-art unified performance on ImageNet-1K by combining masked token reconstruction and codebook contrast losses in a one-time precomputed discrete token space.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18714","ref_index":41,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Semantic Generative Tuning for Unified Multimodal Models","primary_cat":"cs.CV","submitted_at":"2026-05-18T17:46:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Semantic Generative Tuning applies segmentation-based generative proxies during post-training to align and improve both understanding and generation in unified multimodal models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18115","ref_index":62,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens","primary_cat":"cs.CV","submitted_at":"2026-05-18T09:24:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"WinTok is a hybrid visual tokenizer that supplements pixel tokens with learnable semantic tokens distilled asymmetrically from foundation models to improve reconstruction, understanding, and generation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14333","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation","primary_cat":"cs.CV","submitted_at":"2026-05-14T03:57:25+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"InsightTok improves text and face fidelity in discrete image tokenization via content-aware perceptual losses, with gains transferring to autoregressive generation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13517","ref_index":4,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ArcVQ-VAE: A Spherical Vector Quantization Framework with ArcCosine Additive Margin","primary_cat":"cs.CV","submitted_at":"2026-05-13T13:35:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"ArcVQ-VAE adds spherical angular-margin regularization consisting of ball-bounded norms and arc-cosine margin loss to improve codebook utilization in VQ-VAE for image tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12500","ref_index":90,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture","primary_cat":"cs.CV","submitted_at":"2026-05-12T17:59:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SenseNova-U1 presents native unified multimodal models that match top understanding VLMs while delivering strong performance in image generation, infographics, and interleaved tasks via the NEO-unify architecture.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"arXiv:2507.12566, 2025. [89] Gen Luo, Xue Yang, Wenhan Dou, Zhaokai Wang, Jiawen Liu, Jifeng Dai, Yu Qiao, and Xizhou Zhu. Mono-internvl: Pushing the boundaries of monolithic multimodal large language models with endogenous visual pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24960-24971, 2025. [90] Chuofan Ma, Yi Jiang, Junfeng Wu, Jihan Yang, Xin Yu, Zehuan Yuan, Bingyue Peng, and Xiaojuan Qi. Unitok: A unified tokenizer for visual generation and understanding. arXiv preprint arXiv:2502.20321, 2025. [91] Wufei Ma, Haoyu Chen, Guofeng Zhang, Yu-Cheng Chou, Celso M de Melo, and Alan Yuille. 3dsrbench: A comprehensive 3d spatial reasoning benchmark."},{"citing_arxiv_id":"2605.07915","ref_index":55,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion","primary_cat":"cs.CV","submitted_at":"2026-05-08T15:52:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Prior-Aligned AutoEncoders shape latent manifolds with spatial coherence, local continuity, and global semantics to improve latent diffusion, achieving SOTA gFID 1.03 on ImageNet 256x256 with up to 13x faster convergence.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Deep generative models through the lens of the manifold hypothesis: A survey and new connections.arXiv preprint arXiv:2404.02954, 2024. [54] Chuofan Ma, Yi Jiang, Junfeng Wu, Jihan Yang, Xin Yu, Zehuan Yuan, Bingyue Peng, and Xiaojuan Qi. Unitok: A unified tokenizer for visual generation and understanding, 2025. URL https://arxiv.org/abs/2502.20321. [55] Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. InEuropean Conference on Computer Vision, pages 23-40. Springer, 2024. [56] Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov,"},{"citing_arxiv_id":"2605.05646","ref_index":127,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality","primary_cat":"cs.CV","submitted_at":"2026-05-07T03:53:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MUSE decouples reconstruction and semantic learning in visual tokenization via topological orthogonality, yielding SOTA generation quality and improved semantic performance over its teacher model.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25072","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Beyond Accuracy: Benchmarking Cross-Task Consistency in Unified Multimodal Models","primary_cat":"cs.CV","submitted_at":"2026-04-27T23:57:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"XTC-Bench reveals that strong performance on generation or understanding tasks in unified multimodal models does not guarantee cross-task semantic consistency, which instead depends on how tightly coupled the learning objectives are across modalities.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"representation-level misalignment, offering a concrete direction for ad- vancing unified multimodal modeling beyond isolated task performance. Project page:https: // weixingw. github. io/ xtc-bench/ 1 Introduction Unified Multimodal Models (uMMs) are designed to jointly support visual un- derstanding and visual generation within a shared architectural and representa- tional space [5,10,15,27,36-38,41]. By aligning vision and language into a shared latent representation, these models aim to enable seamless reasoning and synthe- sis across modalities. Recent systems demonstrate strong performance on both ⋆ Equal contribution arXiv:2604.25072v1 [cs.CV] 27 Apr 2026 2 Wang et al. Generation Understanding Generation Prompt: \"In this scene, Car_1 - a"},{"citing_arxiv_id":"2604.24885","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations","primary_cat":"cs.CV","submitted_at":"2026-04-27T18:08:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VibeToken enables autoregressive image generation at arbitrary resolutions using 64 tokens for 1024x1024 images with 3.94 gFID, constant 179G FLOPs, and better efficiency than diffusion or fixed AR baselines.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"formance, has seen limited deployment in production use cases. A key reason is flexibility: diffusion models gen- eralize naturally to arbitrary resolutions and aspect ratios, while AR models struggle to do so. As a result, most exist- ing AR literature targets fixed resolutions (e.g., 256 ×256, 512×512) and fails to adapt to arbitrary resolutions and as- pect ratios [25, 38, 39, 46, 47]. A common workaround is to append super-resolution modules [50], but this introduces additional training complexity and computational cost (e.g., SDXL/Flux-upscaler) [11, 30]. We trace these scalability issues back to the image tok- enizer [10, 24, 35, 37, 50, 54]. As shown in Figure 2, conven- tional 2D CNN-based tokenizers (e.g., VQGAN [10]) pro-"},{"citing_arxiv_id":"2604.24763","ref_index":27,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation","primary_cat":"cs.CV","submitted_at":"2026-04-27T17:59:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Tuna-2 shows that direct pixel embeddings can replace vision encoders in unified multimodal models, achieving competitive generation and stronger understanding at scale.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12145","ref_index":49,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Why Your Tokenizer Fails in Information Fusion: A Timing-Aware Pre-Quantization Fusion for Video-Enhanced Audio Tokenization","primary_cat":"eess.AS","submitted_at":"2026-04-13T23:49:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A timing-aware pre-quantization fusion approach integrates visual cues into audio tokenizers along the temporal axis, maintaining reconstruction quality while outperforming audio-only and prior multimodal baselines on downstream tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.20187","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MuSteerNet: Human Reaction Generation from Videos via Observation-Reaction Mutual Steering","primary_cat":"cs.CV","submitted_at":"2026-03-20T17:59:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MuSteerNet generates realistic 3D human reactions from videos by mutually steering visual observations and reaction motions to reduce content mismatch.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.01554","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"InfoTok: Information-Theoretic Regularization for Capacity-Constrained Shared Visual Tokenization in Unified MLLMs","primary_cat":"cs.LG","submitted_at":"2026-02-02T02:47:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"InfoTok uses mutual information constraints to regularize shared visual tokenization in unified MLLMs, improving both understanding and generation performance without extra training data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.22963","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary","primary_cat":"cs.RO","submitted_at":"2025-11-28T08:11:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Humanoid-LLA converts unconstrained natural language commands into stable whole-body motions for humanoid robots using a unified motion vocabulary and two-stage supervised-plus-reinforcement fine-tuning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.18457","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"VFM-VAE: Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models","primary_cat":"cs.CV","submitted_at":"2025-10-21T09:30:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VFM-VAE uses a frozen VFM directly as LDM tokenizer via a custom decoder, reaching gFID 2.22 in 80 epochs and 1.62 after 640 epochs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.22220","ref_index":53,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs","primary_cat":"cs.CL","submitted_at":"2025-09-26T11:32:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"StableToken introduces a multi-branch architecture with bit-wise voting to create noise-robust semantic speech tokens, achieving lower Unit Edit Distance and better SpeechLLM robustness than prior single-path tokenizers.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.15564","ref_index":77,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Show-o2: Improved Native Unified Multimodal Models","primary_cat":"cs.CV","submitted_at":"2025-06-18T15:39:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Show-o2 unifies text, image, and video understanding and generation in a single autoregressive-plus-flow-matching model built on 3D causal VAE representations.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Transfusion [147]✓ ✓AR + Diff.VILA-U [123]✓ ✓ ✓AREmu3 [114]✓ ✓ ✓ARMonoFormer [146]✓ ✓AR + Diff.Dual-Diffusion [63]✓ ✓Diff.SynerGen-VL [58]✓ ✓ARMMAR [134]✓ ✓AR + MARMUSE-VL [129]✓ ✓AROrthus [53]✓ ✓AR + Diff.Liquid [118]✓ ✓ARLlamaFusion [95]✓ ✓AR + Diff.UGen [99]✓ ✓ARUniDisc [98]✓ ✓Diff.UniToken [50]✓ ✓ARHarmon [122]✓ ✓AR+MARDualToken [96]✓ ✓ARUniTok [77]✓ ✓ARSelftok [110]✓ ✓ARMuddit [94]✓ ✓Diff.MMaDA [135]✓ ✓Diff.HaploOmni [124]✓ ✓ ✓AR + Diff.TokLIP [68]✓ ✓ARShow-o2 (Ours) ✓ ✓ ✓ AR + Diff. Janus-Series [26, 27, 79]✓ ✓AR (+Diff.)V ARGPT [148]✓ ✓ARUnidFluid [38]✓ ✓AR + MAROmniMamba [149]✓ ✓ARMogao [65]✓ ✓AR + Diff.BAGEL [32]✓ ✓ ✓AR + Diff.Fudoki [112]✓ ✓Diff.UniGen [104]✓ ✓AR + Diff. NExT-GPT [120]✓ ✓ ✓AR + Diff."},{"citing_arxiv_id":"2505.05472","ref_index":55,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation","primary_cat":"cs.CV","submitted_at":"2025-05-08T17:58:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Mogao presents a causal unified model with deep fusion, dual encoders, and interleaved position embeddings that achieves strong performance on multi-modal understanding, text-to-image generation, and coherent interleaved outputs including zero-shot editing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}