Do Unified Multimodal Models Think in One Space? A Lens Through Cross-Branch Steering
- URL: http://arxiv.org/abs/2607.26411v1
- Date: Wed, 29 Jul 2026 02:46:20 GMT
- Title: Do Unified Multimodal Models Think in One Space? A Lens Through Cross-Branch Steering
- Abstract summary: We introduce emphcross-branch semantic steering, an intervention-based framework that extracts semantic directions from one branch and applies them to the other.<n>We show that steering vectors learned from the understanding branch can transfer to generation, enabling controllable image synthesis and improved semantic faithfulness.<n>Our results reveal that architectural unification does not guarantee semantic alignment, and establish cross-branch steering as a practical tool for probing multimodal representations.
- Score: 7.62772056485722
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Unified multimodal models (UMMs) aim to integrate understanding and generation within a single architecture, yet it remains unclear whether these capabilities share a unified and transferable semantic space. This question is fundamentally challenging, as the two branches operate over heterogeneous representations (text tokens vs.\ visual latents) and distinct training objectives, making direct comparison difficult. To address this, we introduce \emph{cross-branch semantic steering}, an intervention-based framework that extracts semantic directions from one branch and applies them to the other. We show that steering vectors learned from the understanding branch can transfer to generation, enabling controllable image synthesis and improved semantic faithfulness. In contrast, the reverse direction consistently shows limited effectiveness. Our analysis suggests that this asymmetry may be related to a practical representational mismatch: understanding-derived vectors capture transferable, object-centric semantics, while generation-derived vectors primarily encode low-level appearance features. Our results reveal that architectural unification does not guarantee semantic alignment, and establish cross-branch steering as a practical tool for probing multimodal representations.
Related papers
- Uncovering Understanding-Generation Synergy in Native Unified Multimodal Models: From Representation, Task to System [90.64481646805855]
unified multimodal models (UMMs) jointly perform visual understanding and generation within a single model.<n>We investigate their relationship at the representation, task, and system levels in a controlled, structurally native setting.<n>We show that the value of UMMs extends beyond a unified interface: appropriate specialization, shared task knowledge, and end-to-end optimization can turn coexistence into synergy.
arXiv Detail & Related papers (2026-09-01T17:59:59Z) - SPAR: Semantic-Pixel Self-Alignment and Adaptive Routing for Unified Multimodal Models [31.77829987533264]
We propose a novel unified multimodal framework featuring textbfSemantic-textbfPixel self-alignment and textbfRouting (textbfSPAR)<n>To reconcile semantic perception with pixel-level reconstruction, we introduce an asymmetric dual-stream unified tokenizer. A lightweight semantic stream anchors discriminative features, while a Transformer-augmented pixel stream recovers fine-grained visual details into a unified compact latent space.
arXiv Detail & Related papers (2026-06-22T08:48:19Z) - Improving Relative Representations with Learned Anchors and Whitened Inner Products [0.0]
Independently trained neural models typically converge to incompatible latent representations.<n>Relative Representations (RR) address this by mapping absolute coordinates to a shared space defined by similarities to common anchor points.<n>Traditional implementations rely on randomly sampled anchors and cosine similarity.<n>We propose a robust framework for cross-model communication based on two improvements.
arXiv Detail & Related papers (2026-05-28T21:43:53Z) - Semantic Generative Tuning for Unified Multimodal Models [62.18894352635965]
Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.<n> prevailing training paradigms independently optimize understanding via sparse text signals and generation through dense pixel objectives.<n>This work presents the first systematic investigation into generative post-training, where we formulate hierarchical visual tasks as generative proxies to bridge the isolation in UMMs.
arXiv Detail & Related papers (2026-05-18T17:46:46Z) - UniAlignment: Semantic Alignment for Unified Image Generation, Understanding, Manipulation and Perception [54.53657134205492]
UniAlignment is a unified multimodal generation framework within a single diffusion transformer.<n>It incorporates both intrinsic-modal semantic alignment and cross-modal semantic alignment, thereby enhancing the model's cross-modal consistency and instruction-following robustness.<n>We present SemGen-Bench, a new benchmark specifically designed to evaluate multimodal semantic consistency under complex textual instructions.
arXiv Detail & Related papers (2025-09-28T09:11:30Z) - Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder [27.6980944443566]
This survey offers a novel perspective on latent space geometry through the lens of compositional semantics.<n>We review and compare three mainstream autoencoder architectures-Variational AutoEncoder (VAE), Vector Quantised VAE (VQVAE), and Sparse AutoEncoder (SAE)
arXiv Detail & Related papers (2025-06-25T01:48:18Z) - SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow [94.90853153808987]
Semantic segmentation and semantic image synthesis are representative tasks in visual perception and generation.
We propose a unified framework (SemFlow) and model them as a pair of reverse problems.
Experiments show that our SemFlow achieves competitive results on semantic segmentation and semantic image synthesis tasks.
arXiv Detail & Related papers (2024-05-30T17:34:40Z) - Spatial Semantic Recurrent Mining for Referring Image Segmentation [63.34997546393106]
We propose Stextsuperscript2RM to achieve high-quality cross-modality fusion.
It follows a working strategy of trilogy: distributing language feature, spatial semantic recurrent coparsing, and parsed-semantic balancing.
Our proposed method performs favorably against other state-of-the-art algorithms.
arXiv Detail & Related papers (2024-05-15T00:17:48Z) - Latent Space Translation via Semantic Alignment [29.2401314068038]
We show how representations learned from different neural modules can be translated between different pre-trained networks.
Our method directly estimates a transformation between two given latent spaces, thereby enabling effective stitching of encoders and decoders without additional training.
Notably, we show how it is possible to zero-shot stitch text encoders and vision decoders, or vice-versa, yielding surprisingly good classification performance in this multimodal setting.
arXiv Detail & Related papers (2023-11-01T17:12:00Z) - Making LLaMA SEE and Draw with SEED Tokenizer [69.1083058794092]
We introduce SEED, an elaborate image tokenizer that empowers Large Language Models with the ability to SEE and Draw.
With SEED tokens, LLM is able to perform scalable multimodal autoregression under its original training recipe.
SEED-LLaMA has exhibited compositional emergent abilities such as multi-turn in-context multimodal generation.
arXiv Detail & Related papers (2023-10-02T14:03:02Z) - Image-to-image Mapping with Many Domains by Sparse Attribute Transfer [71.28847881318013]
Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points.
Current convention is to approach this task with cycle-consistent GANs.
We propose an alternate approach that directly restricts the generator to performing a simple sparse transformation in a latent layer.
arXiv Detail & Related papers (2020-06-23T19:52:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.