End-to-end autonomous driving systems commonly follow a cascaded two-stage pipeline where a perception stage compresses multi-modal sensor inputs into a compact context and a downstream planner predicts trajectories conditioned on this context. We argue that this one-way perception-to-planning interface forces sensor inputs into a compact representation, losing the fine-grained details critical for planning. Moreover, by constraining the planner to this compressed context, it is difficult to leverage the rich representations offered by modern vision foundation models. To address these issues, we propose MOJITO, a unified sensor-to-action framework for end-to-end autonomous driving built on modal joint learning. MOJITO removes the cascaded interface and instead performs block-wise Modal Joint Attention that simultaneously updates action, image, and LiDAR features, allowing the planner to directly access multi-modal features during action generation. MOJITO achieves 88.9 PDMS on the NAVSIM v1 dataset and 88.4 EPDMS on the more challenging NAVSIM v2 dataset, setting a new state-of-the-art. Extensive experiments further demonstrate strong scalability, instruction following, and diverse trajectory generation. Code and models are available at https://github.com/mumucc01/MOJITO.
Zhijing Cheng, Xuan Zhang, Donglin Di et al.· arXiv.org· 0 citations
Spatial transcriptomics (ST) enables spatially resolved gene expression profiling within intact tissue sections. However, its widespread adoption is constrained by the high cost and low throughput of current sequencing-based protocols. This has motivated growing interest in computationally predicting gene expression directly from routinely acquired histology images. Existing methods are largely restricted to isolated 2D tissue slices and fail to capture richer spatial relationships or structured dependencies among spot-level gene expression profiles. In this paper, we propose STAG, a dual-branch framework for gene-aware expression prediction and spatial context modeling. A Query branch predicts ST expression for an individual target spot, while a Neighbor branch acts as an auxiliary branch to model structured relationships among multiple spots. By leveraging hypergraph learning, the Neighbor branch captures higher-order spatial and molecular dependencies, enabling unified modeling of both intra-slice and inter-slice relationships. This design supports standard 2D settings (a single slice) and naturally extends to 3D scenarios when adjacent tissue sections are available. Moreover, STAG leverages gene semantic information as biological guidance by encoding gene names with a foundation model, enabling coordinated gene-aware interactions beyond independent gene prediction. STAG achieves an average gain of 5.16% in PCC@250 across six datasets. Under highly variable gene selection, STAG maintains the lowest RMSE and highest PCC@50 across three datasets. The effectiveness of the learned representations is further demonstrated in pseudo-3D prediction and downstream cancer classification tasks. Code is available at https://github.com/MCPathology/STAG.
Mingcheng Qu, Yuchuan Zhao, Guang Yang et al.· Medical Image Anal.· 0 citations
Incomplete Multi-View Clustering addresses the problem of clustering multi-modal data when certain views are missing. Recent end-to-end generative approaches leverage diffusion models to recover missing views via stochastic noise-to-data trajectories. While expressive, such mechanisms are not explicitly designed for clustering, as they initialize from cluster-agnostic noise and rely on stochastic denoising dynamics. In this work, we revisit probability path design in end-to-end generative IMVC. We introduce a flow-matching framework with a linear interpolation path between paired view representations, that replaces diffusion with probability flows between observed and missing views. We provide a formal analysis showing that deterministic ODE flows are inherently better aligned with clustering objectives than diffusion-based stochastic trajectories, especially in terms of transport mechanisms that respect class-conditional data distributions and maintain cluster consistency in finite-step regimes. Building upon this insight, we develop an end-to-end IMVC architecture that integrates straight-path flow-matching view completion with cluster-level and entropy-based alignment to enforce cross-view clustering consistency. Extensive experiments on standard IMVC benchmarks demonstrate that the proposed framework establishes new state-of-the-art performance.
Yiteng Yuan, Junyan Wang, Zheyuan Liu et al.· 0 citations
ManGo (Manga Active Narrative Grounding Optimization), an unsupervised framework for active manga visual question answering, introduces Active Narrative Sketching (ANS), which iteratively selects panels, extracts concise grounded clues, and decides when to stop, forming a compact question-directed evidence sketch before answer generation.
Hao Qiu, Jun-Yan Wang, Zheyuan Liu et al.· 0 citations
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