Skip to main content
Search

# Unified Multimodal Models

4 related articles

Subscribe to updates
  1. arXiv

    Möbius moves denoising inside the language model: no motion VAE, and it leads HumanML3D on generation and captioning

    Möbius places the entire denoising process of motion generation inside the same multimodal representation space used for motion understanding, so clean motion, noisy motion and text share attention without a discrete tokenizer or a pretrained motion VAE. On HumanML3D, the 0.6B variant outperforms diffusion-based, language-model-based and unified motion-language baselines on both text-to-motion generation and motion captioning; scaling to 1.7B parameters and 1,700 hours of motion data improves all motion-language tasks further.

    Tencent (China)Hong Kong University of Science and TechnologyMassachusetts Institute of Technology
    Jiazhi Shu（First author）
    MöbiusText-to-Motion GenerationMixture-of-TransformersUnified Multimodal Models
  2. arXiv

    Forgetting isn't unified in unified multimodal models: editing only the image path leaves 24% recognition forgetting, while UniForget reaches 80% joint forgetting

    Unified multimodal models generate images and understand them in one model, yet existing single-pathway unlearning methods struggle to make a target concept disappear from both pathways. UUBench pairs generation and understanding tasks around 30 target concepts and 60 same-category neighbors, and UniForget merges generation-side weight editing with understanding-side low-rank adapter updates into one checkpoint, reaching 80.0% joint forgetting on BAGEL-7B-MoT and 92.7% on Janus-Pro-7B while exposing a trade-off between forgetting and capability preservation.

    Zhongkai Xue（First author）
    UniForgetUUBenchMachine UnlearningUnified Multimodal ModelsLoRA
  3. arXiv

    UniEvo-VL lets a multimodal model teach itself with its own critique, lifting GenEval from 0.747 to 0.808

    UniEvo-VL introduces an on-policy self-distillation (OPSD) recipe in which one multimodal model acts as both a student seeing only the vanilla prompt and a teacher conditioned on a critique-derived revised prompt, minimizing per-state divergence between their denoising distributions along the student's own trajectories; on Qwen-Image-2512 it raises GenEval from 0.747 to 0.808 and GenEval2 Soft-TIFA from 32.97 to 35.53, while stronger external critics such as GPT5.6-Luna indicate a higher self-evolving ceiling and text-rendering gains remain uneven.

    Fang Wu（First author）
    UniEvo-VLOn-Policy Self-DistillationUnified Multimodal ModelsQwen-Image-2512Image Generation
  4. arXiv

    OmniTaskonomy maps 19 generation tasks against 25 understanding capabilities: I2I-then-I2T training improves understanding, and gradient alignment tracks the gains

    Using controlled paired image-to-image (I2I) generation and image-to-text (I2T) understanding tasks on the unified multimodal model BAGEL, the authors find that a recipe of I2I training that updates shared parameters followed by I2T finetuning improves understanding with gains that grow as I2I data increases, build OmniTaskonomy, a taxonomy spanning 19 I2I tasks and 25 understanding capabilities, produce a selective task-dependent generation-to-understanding transfer map, and find that stronger gradient alignment between generation and understanding is associated with larger downstream transfer gains.

    Jiaxin Ge（First author）
    OmniTaskonomyCross-Modal TransferGradient AlignmentBAGELUnified Multimodal Models

You’re all caught up