Constrained Pinching Antenna Array Design for Sum-Rate Maximization in Multi-User PASS

Pinching antenna systems (PASS) have recently emerged as a promising architecture for flexible indoor wireless communications. However, most existing pinching antenna (PA) array designs for multi-user PASS either offer limited beam adaptation accuracy or require prohibitively high deployment cost. In this paper, we investigate a more practical constrained pinching antenna array (C-PAA)-assisted downlink PASS, where multiple PAs are grouped into a movable array and can be finely adjusted within the array at the wavelength scale. To improve the system spectral efficiency, a sum-rate maximization problem is formulated by jointly considering the array-center position and the fine-grained antenna distribution within the C-PAA. First, the structural properties of the C-PAA are characterized, and an explicit upper bound on the array aperture is derived. Then, tractable approximations for the effective channel gain and the achievable user rate are developed. Furthermore, the optimization problem of the multi-user sum-rate is analyzed, where the system sum-rate function is shown to exhibit a favorable unimodal behavior under practically relevant conditions, which enables an efficient one-dimensional search for the optimal C-PAA position. To further reduce the computational complexity, a closed-form approximate solution for the near-optimal array-center position is derived. Numerical results verify the accuracy of the developed analysis and demonstrate that the proposed C-PAA scheme closely approaches the ideal upper bound and significantly outperforms conventional fixed-spacing and existing PA array benchmarks.

Beyond Dense Futures: World Models as Structured Planners for Robotic Manipulation

Recent world-model-based Vision-Language-Action (VLA) architectures have improved robotic manipulation through predictive visual foresight. However, dense future prediction introduces visual redundancy and accumulates errors, causing long-horizon plan drift. Meanwhile, recent sparse methods typically represent visual foresight using high-level semantic subtasks or implicit latent states. These representations often lack explicit kinematic grounding, weakening the alignment between planning and low-level execution. To address this, we propose StructVLA, which reformulates a generative world model into an explicit structured planner for reliable control. Instead of dense rollouts or semantic goals, StructVLA predicts sparse, physically meaningful structured frames. Derived from intrinsic kinematic cues (e.g., gripper transitions and kinematic turning points), these frames capture spatiotemporal milestones closely aligned with task progress. We implement this approach through a two-stage training paradigm with a unified discrete token vocabulary: the world model is first trained to predict structured frames and subsequently optimized to map the structured foresight into low-level actions. This approach provides clear physical guidance and bridges visual planning and motion control. In our experiments, StructVLA achieves strong average success rates of 75.0% on SimplerEnv-WidowX and 94.8% on LIBERO. Real-world deployments further demonstrate reliable task completion and robust generalization across both basic pick-and-place and complex long-horizon tasks.