Current evaluations of large language models (LLMs) mainly rely on dataset-based generation accuracy. However, generative correctness does not guarantee the discriminative capability required to verify solutions, frequently masking an inability to distinguish valid reasoning from plausible errors. While multi-agent debate inherently entails judgment, we show that uncontrolled context growth and convergence to majority voting introduce significant noise, obscuring intrinsic model judgment. To address these limitations, we propose a progressive argumentation-mining diagnostic framework designed to explicitly control context and isolate discriminative behaviors. Instead of indiscriminate aggregation, our approach distills and retains only the single most well-supported rationale per answer, preventing context dilution while enforcing strict quality-based selection. Applying this framework reveals a fundamental cognitive divergence: models exhibit structural susceptibility to plausible misinformation in knowledge tasks, whereas in reasoning tasks they demonstrate latent discriminative potential that remains fragile under pressure. These findings underscore the fragility of discriminative capabilities, advocating for diagnostic methodologies that prioritize judgment stability over simple generation performance.

Large Language Models (LLMs) are increasingly adopted in macroeconomic agent-based modeling(ABM). However, existing research focuses on replicating macro-level stylized facts while often neglecting verification of micro-level decision-making. We investigate this gap by comparing LLM agents to human responses from the Survey of Consumer Expectations (SCE) dataset. Our empirical analysis identifies specific limitations: weak trend responsiveness, mode collapse, and a potential data leakage. We propose the Heterogeneous Shock-Response Causal Transmission Framework to tackle these issues. To ensure theoretical consistency, we use LLMs to build a literature-verified causal graph in which macroeconomic shocks influence decisions via generated mediator nodes, while agent profiles serve as edge moderators. Building on this, during inference, we perform a path search to retrieve relevant causal chains and inject them as an explicit Chain-of-Thought(CoT), prioritizing mechanistic logic over statistical pattern matching. To evaluate the effectiveness of our inference approach, we validate it via a two-stage process that combines micro-level dataset testing and macro-level simulation in the EconAgent system. Results from these experiments indicate that our framework improves alignment with human trends and effectively captures behavioral heterogeneity. Overall, this work contributes to the development of reliable and grounded economic simulations.

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) 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) 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 InspireScore achieves 44% higher correlation with expert judgments compared to existing methods, while InspireDebate shows significant improvements, outperforming baseline models by 57%. Source code is available at https://github.com/fywang12/InspireDebate.