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
Assessing the impacts of social policy changes is a widely acknowledged challenge for policymakers. Econometric methods can be unreliable when extrapolating to hypothetical scenarios, while field pilot programs are highly costly. In this paper, we propose using large language models (LLMs) as policy-assessment tools adapted from general-purpose models. We present FlexPension-LLM, the first domain-specialized large language model for a hierarchical pension-enrollment prediction task among flexible workers in China, and introduce DKI-RDistill, which injects policy-grounded cues into the prompt, including Probit-derived marginal effects and hukou-province pension rules. The method then uses LoRA/SFT to distill rationale-augmented supervision into an open-weight MoE student, with teacher errors corrected by regenerating those cases under ground-truth labels. On a CHFS 2019 blind split, FlexPension-LLM achieves 0.9316 Composite F1, surpassing its Claude Sonnet 4.5 teacher and 15 of 17 baselines, and is statistically indistinguishable from Claude Opus 4.6. Across four external surveys, it averages 0.7549 Composite F1 and shows the narrowest performance range among the strongest systems. Component analysis shows that gains come mainly from policy-grounded cue injection and error-filtered supervision, while rationales provide decision traces that can be checked against policy rules.
Yu-Miao Li, Pei-Xin Liu, Donglin Di et al.· 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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
