constraint formulation

本文提出CIT-CAD框架，通过构建约束意图树来指导CAD代码生成及验证，解决现有方法忽视设计结构与关系错误的问题。

In parametric CAD, conventional sketch constraint generation often misaligns with design intent, leading to over-constrained systems or geometric distortions. To address this, we propose “Design Alignment”—a novel paradigm that introduces large language model (LLM) alignment techniques to CAD constraint generation for the first time. Our method establishes a solver-feedback-driven alignment training framework, integrating reasoning-capable LLMs’ semantic understanding with classical geometric constraint modeling to jointly optimize constraint completeness and geometric fidelity. The approach is compatible with existing generative models and achieves a full-constraint satisfaction rate of 93%, substantially outperforming supervised fine-tuning baselines (34%) and non-aligned methods (8.9%). This advancement significantly enhances the editability, robustness, and design-intent consistency of parametric CAD models.

SMT solvers exhibit poor efficiency on quantified formulas arising from real-world applications—especially when formulas are easily encodable yet computationally expensive to solve. This paper introduces a novel quantification mechanism based on set-bounded quantifiers, where variable domains are restricted to finite sets, and integrates quantifier elimination with filtering operators from finite relational theory. Our contributions are threefold: (1) We define a decidable fragment of constraints wherein bounded quantification is realized via constrained set derivation; (2) we identify the fundamental cause of undecidability in unrestricted filtering operations; and (3) we establish a formal framework unifying quantifier-free logic with filtering operators. Experiments demonstrate that our approach significantly outperforms state-of-the-art quantification techniques on the satisfiable SLEEC benchmark, while matching the performance of the specialized solver LEGOS on unsatisfiable benchmarks.

This study addresses the high cost and error-proneness of model refactoring caused by paradigm disparities among constraint solvers. We propose a modular automated translation framework based on CPMpy that employs a layered waterfall architecture to uniformly handle sub-expression negation and auxiliary variable generation while optimizing linearization strategies for nonlinear operators. This approach enables seamless translation from high-level models to low-level paradigms, including CP, SMT, and ILP. Experimental results demonstrate that the framework effectively eliminates manual rewriting and that its optimized linearization significantly enhances ILP and PB solving performance. Consequently, this work provides an efficient, flexible, and standardized solution for the automatic benchmarking of multi-paradigm solvers in combinatorial optimization.

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 lack of systematic methodologies in model optimization, which often relies on heuristic choices and struggles to accommodate diverse deployment constraints. It formalizes model compression and acceleration as a constraint-aware multi-objective engineering decision problem, establishing a unified and actionable framework grounded in five key dimensions: data availability, latency, memory footprint, accuracy tolerance, and retraining budget. By integrating techniques such as quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and inference optimization, the study proposes tailored optimization pipelines for four representative industrial scenarios, delivering a reproducible and quantifiable guide for technology selection.

本文提出了一种形式化的语义块模型和执行评判基准来独立评估规范质量，通过结构化表示和机器可验证条件解决规范确定性问题。

This study addresses the prevalent issue of semantic domain constraint violations in synthetic tabular data by proposing a unified, tool-based repair framework leveraging LLM agents. The method automatically discovers inter-column constraints through machine-executable hypotheses and full-table validation interfaces, integrating counterexample-guided revision with a generator-agnostic post-processing mechanism to achieve zero-violation correction for equations, inequalities, and logical dependencies. Experimental results demonstrate that this framework significantly enhances the detection and repair of complex constraints. Furthermore, it ensures semantic consistency while effectively preserving univariate distributional characteristics and improving utility for downstream tasks, offering a robust solution for high-fidelity synthetic data generation.

This study addresses the challenge in axiomatic design of accurately translating customer needs and constraints into a minimal and independent set of primary functional requirements (FRs). Focusing on the problem definition phase, it systematically elucidates the nature, invariance, and formulation principles of primary FRs. Building upon Nam P. Suh’s theoretical framework and integrating insights from complexity theory and requirements engineering, the work establishes—for the first time—the objectivity and uniqueness of primary FRs, clarifies common misconceptions, and critically examines the applicability boundaries of large language models in this context. The research provides designers with a clear, actionable methodology for constructing primary FRs, thereby significantly enhancing the rigor of problem definition and the likelihood of successful design outcomes.

This work addresses the challenge of irreproducibility in data analysis scripts, which often stems from implicit assumptions—such as specific package versions, expected data formats, or undocumented manual interventions. The paper proposes a static analysis approach tailored to data analysis workflows that, for the first time, unifies diverse implicit assumptions into inferable constraint models. By leveraging customized program analysis and example-driven modeling, the authors develop a prototype system capable of automatically identifying these hidden assumptions, extracting executable preconditions, and generating verifiable constraints. The resulting framework supports runtime validation and automatic documentation generation, substantially enhancing script executability, reproducibility, and interpretability.