融合多策略生成情境化决策，突破传统选最优规则的局限
From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoning
- 通过语义关联激活多种策略，实现冲突规则的协同合成
- 在美墨贸易战案例中生成连贯且情境敏感的推理叙事
- 适合需要跨领域战略分析的决策支持场景
我们提出一种混合架构，用于代理增强型战略推理，结合启发式提取、语义激活与组合合成。基于从古典军事理论到现代企业战略的多元来源，模型通过受量子认知研究启发的语义相互作用过程，激活并组合多个启发式策略。不同于传统决策引擎仅选择最优规则，本系统将冲突启发式融合为连贯且上下文敏感的推理叙事，由语义交互建模与修辞框架引导。通过Meta vs. FTC案例研究验证框架可行性，初步采用语义度量进行评估。讨论了局限性及扩展方向（如动态干扰调优）。
原文摘要 · Abstract (English)
We present a hybrid architecture for agent-augmented strategic reasoning, combining heuristic extraction, semantic activation, and compositional synthesis. Drawing on sources ranging from classical military theory to contemporary corporate strategy, our model activates and composes multiple heuristics through a process of semantic interdependence inspired by research in quantum cognition. Unlike traditional decision engines that select the best rule, our system fuses conflicting heuristics into coherent and context-sensitive narratives, guided by semantic interaction modeling and rhetorical framing. We demonstrate the framework via a Meta vs. FTC case study, with preliminary validation through semantic metrics. Limitations and extensions (e.g., dynamic interference tuning) are discussed.
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