cross-surface integration

This work addresses the lack of formal guarantees for global consistency among heterogeneous views—such as electrical, thermal, mechanical, and software—in multi-view systems engineering. It introduces sheaf theory into model-based systems engineering for the first time, constructing a topological space (an architectural site) where interfaces serve as points and engineering views as open sets. A design presheaf is defined to assign local design spaces to these opens. Using restriction maps and limit-preserving functors from category theory, the paper proves that this presheaf satisfies the sheaf condition if and only if all pairwise interfaces are compatible, thereby reducing global consistency to local compatibility and ensuring a unique global design amalgamation. The approach is machine-verified in Lean 4 with Mathlib for a three-view case study, yielding a formally checkable chain of consistency proofs.

Current foundation models generate 3D content that, while visually plausible, lacks operability and thus struggles to support downstream tasks such as CAD or robotics. This work proposes Hylos, a novel system architecture that introduces contract-based design into spatial intelligence for the first time. Hylos employs a “spatial transaction” mechanism to maintain scene-level operational states, ensuring object recognizability, constraint satisfaction, and action feasibility. By integrating scene graph dependency tracking, constraint solving, and capability gap detection, the system guarantees verifiability and rollback capability for spatial modifications at transaction boundaries, while enabling upstream repairs grounded in causal dependencies. Experiments demonstrate that Hylos effectively identifies and corrects structural errors, transforming generated 3D content into a reliable foundation suitable for engineering applications and interactive world construction.

Understanding the interaction between modular CMA-ES algorithm configurations and problem characteristics remains challenging, as performance varies significantly across optimization problems. Method: Leveraging the BBOB benchmark suite (5D/30D), we propose an “algorithm footprint” modeling framework that quantitatively characterizes how configuration performance responds to landscape features—including condition number, non-convexity, and anisotropy. Contribution/Results: By analyzing footprints across 24 benchmark functions, we identify both universal behavioral patterns and configuration-specific response mechanisms. This work establishes, for the first time, an interpretable, systematic mapping between problem features and configuration preferences. The resulting framework enhances transparency and reliability in black-box optimization—particularly for algorithm selection and adaptive configuration—by grounding empirical performance in explainable landscape-aware principles.

Traditional robot design and control are typically decoupled, leading to morphologies poorly aligned with task requirements. This paper proposes a simulation-driven co-optimization framework for morphology and control, breaking the conventional “design-then-control” paradigm to enable task-oriented, end-to-end joint search. Our method employs gradient-free optimization to simultaneously evolve structural parameters and controller policies within a URDF-based multi-task reinforcement learning simulation environment. Key contributions include: (1) demonstrating that controller retraining significantly improves performance, yielding an average gain of 37%; and (2) revealing an inverse correlation between morphological complexity and controller training budget—providing theoretical justification for structural simplification under resource constraints. We validate the framework across four public simulation benchmarks, showing that co-optimization consistently yields more compact, robust, and task-adapted robot morphologies compared to sequential approaches.

In software design, paradigm-implied semantic expectations—such as data abstraction consistency and feedback-control closed-loop behavior—are often left implicit, leading to design deviations and verification challenges. To address this, we introduce the concept of *design obligations*: explicit, logically formalizable, and verifiable specifications that codify such implicit constraints inherent to design paradigms. Leveraging formal modeling and paradigm semantics analysis, we establish two obligation frameworks—one for data-abstraction-based systems and another for feedback-driven adaptive systems—precisely capturing their core semantic requirements. We demonstrate that common design flaws stem from obligation violations and show how these obligations enable rigorous compliance verification and pedagogical application. This work bridges the semantic gap between design intent and implementation, providing both theoretical foundations and a methodological framework for paradigm-driven design assurance.

This work addresses the limitations of large language models (LLMs) in modern network system architecture design, where they often fail due to overlooked constraints and erroneous assumptions. To overcome these issues, the authors propose Kepler, a framework that formulates architecture design as an interpretable constrained optimization problem. Kepler encodes critical properties of systems, hardware, and workloads at an abstract level through expert-driven, structured specifications and leverages SMT solvers for rigorous reasoning. By circumventing the unreliable generative process of LLMs, Kepler efficiently synthesizes feasible architectures that balance multiple objectives, uncovers cross-layer interactions missed by LLMs, and provides traceable, explainable design decisions.

This study addresses the challenge of spatial layout optimization for interconnected systems within non-convex design spaces by extending the SPI2 framework. It introduces, for the first time, a geometric representation based on Maximal Disjoint Ball Decomposition (MDBD) combined with differentiable inside-outside tests, enabling component placement under arbitrary non-convex boundaries. The method integrates computations of centroid and moment of inertia and establishes an end-to-end CAD workflow that supports automatic assembly reconstruction. By simultaneously satisfying geometric constraints, routing requirements, and physical performance objectives, the approach guarantees geometric feasibility within numerical precision. The efficacy and practicality of the proposed method are demonstrated through a multi-system co-layout case study of a synthetic aircraft auxiliary unit.

This work addresses the suboptimal performance and limited interpretability inherent in conventional robot design, where structural topology, material distribution, and control policies are typically developed in isolation. To overcome this limitation, the authors propose the first end-to-end, gradient-based co-design framework that simultaneously optimizes the topology of truss-lattice robots, their heterogeneous material assignment, and neural network controllers. By integrating continuous modeling, hybrid variable embedding, and differentiable physics simulation—augmented with automatic differentiation and constrained optimization to navigate highly non-convex design spaces—the method automatically generates diverse, high-performance locomotion behaviors across multiple tasks. Experimental results demonstrate that the co-designed systems consistently outperform those produced by sequential design approaches, while also elucidating the individual and synergistic contributions of each design dimension, thereby exhibiting strong generalization and task adaptability.