Chinese demonstrates high semantic compactness and rich metaphorical expressiveness, enabling limited text to convey dense meanings while increasing the difficulty of generation and verification, particularly in short-form creative natural language generation (CNLG). In the real world, users often require personalized, fine-grained creative constraints, making reliable verification critical to guiding optimization. According to Brunswik's Lens Model from psychology, constraints' achievement can be inferred from sufficient observable cues. Existing studies are mainly outcome-oriented, implicitly assuming that the outcome itself provides adequate cues for verification. However, this assumption breaks down in Chinese short-form CNLG (e.g., naming or advertising) with diverse personalized constraints, where extremely brief outcomes inherently offer limited information. Explanations can naturally serve as extra cues. Nevertheless, under complex constraints, LLMs' explanations may suffer from hallucination, incompleteness, or ambiguity. To address these, we novelly formalize the Chinese short-form CNLG task as a heterogeneous multi-objective optimization (HMO) issue that needs to jointly optimize multiple personalized constraints and explanation reliability. We further propose MAGIC-HMO, a training-free multi-agent framework that optimizes these objectives through iterative generation and verification under an explanation-oriented multi-objective strategy. Experiments on \emph{Chinese Baby Naming}, a challenging benchmark, demonstrate that MAGIC-HMO significantly outperforms six strong baselines across various LLM backbones. Relevant data and codes are available at https://github.com/foolfun/MAGIC_HMO.

翻译：中文具有语义高度紧凑和丰富隐喻表达的特点，使得有限的文字能承载密集的语义，同时也增加了生成和验证的难度，尤其是在短文本创意自然语言生成（CNLG）任务中。在现实场景中，用户常提出个性化、细粒度的创意约束，这使得可靠的验证对指导优化至关重要。根据心理学中的Brunswik透镜模型，约束的达成度可通过充分的观测线索推断。现有研究主要面向结果导向，隐含假设输出结果本身能为验证提供足够线索。然而，这一假设在中文短文本CNLG（如命名或广告创作）中面临挑战——面对多样化的个性化约束时，极其简短的输出天然包含有限信息。解释可自然成为额外线索。但在复杂约束下，大型语言模型（LLM）生成的解释可能包含幻觉、不完整或歧义问题。为解决这一困境，我们创新性地将中文短文本CNLG任务建模为异构多目标优化（HMO）问题，需同时优化多个个性化约束与解释可靠性。进一步提出MAGIC-HMO——一种免训练的多智能体框架，通过基于解释导向的多目标策略，在迭代生成与验证机制中优化这些目标。在具有挑战性的基准《中文婴儿取名》上的实验表明，MAGIC-HMO在多种LLM基座下均显著优于六个强基线方法。相关数据与代码见https://github.com/foolfun/MAGIC_HMO。