[Submitted on 3 Nov 2026]
Title:From Tokens to Trajectories: Parameter-Efficient Continuous Adaptation of Unified Multimodal Models
View a PDF of the paper titled From Tokens to Trajectories: Parameter-Efficient Continuous Adaptation of Unified Multimodal Models, by Gayatri Deshmukh and 4 other authors
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Abstract:While discrete tokenization in Unified Multimodal Models (UMMs) establishes strong semantic reasoning, it introduces a strict quantization bottleneck that degrades fine-grained visual details, often resulting in structurally accurate but artifact-heavy images. We introduce FlowShift, a parameter-efficient post-training framework that bypasses this bottleneck by converting pre-trained discrete UMMs into continuous rectified-flow generators. Instead of discarding the backbone's semantic priors, FlowShift replaces discrete visual interfaces with continuous latent projections and applies LoRA to the attention layers. To efficiently boost visual quality without semantic drift, we propose Dual-Objective Velocity Vector Distillation (DOVD), which aligns the model with a high-fidelity continuous teacher and ground-truth continuous trajectories. This approach drastically improves perceptual quality while preserving prompt adherence and reasoning. When applied to leading discrete UMMs, FlowShift significantly improves COCO-30K FID and aesthetic scores, while retaining over 99.5% of multimodal understanding capabilities. Ablations confirm DOVD drives generation quality. Ultimately, FlowShift achieves high-fidelity continuous generation without sacrificing pre-trained semantic grounding.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2611.05451 [cs.CV]
(or arXiv:2611.05451v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.05451
arXiv-issued DOI via DataCite
Submission history
From: Onkar Susladkar [view email] [v1] Tue, 3 Nov 2026 18:31:48 UTC (3,170 KB)
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