让AI辩论更真实可信，从情感到逻辑全面评估并优化。

With the rapid advancements in large language models (LLMs), debating tasks, such as argument quality assessment and debate process simulation, have made significant progress. However, existing LLM-based debating systems focus on responding to specific arguments while neglecting objective assessments such as authenticity and logical validity. Furthermore, these systems lack a structured approach to optimize across various dimensions$-$including evaluation metrics, chain-of-thought (CoT) reasoning, and multi-turn debate refinement$-$thereby limiting their effectiveness. To address these interconnected challenges, we propose a dual-component framework: (1) $\textbf{InspireScore}$, a novel evaluation system that establishes a multi-dimensional assessment architecture incorporating four subjective criteria (emotional appeal, argument clarity, argument arrangement, and topic relevance) alongside two objective metrics (fact authenticity and logical validity); and (2) $\textbf{InspireDebate}$, an optimized debating framework employing a phased optimization approach through CoT reasoning enhancement, multi-dimensional Direct Preference Optimization (DPO), and real-time knowledge grounding via web-based Retrieval Augmented Generation (Web-RAG). Empirical evaluations demonstrate that $\textbf{InspireScore}$ achieves 44$\%$ higher correlation with expert judgments compared to existing methods, while $\textbf{InspireDebate}$ shows significant improvements, outperforming baseline models by 57$\%$. Source code is available at https://github.com/fywang12/InspireDebate.