From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoning

2025-07-18

AI Summary

This paper addresses the challenge of integrating conflicting heuristics in strategic reasoning to generate context-sensitive narratives. Methodologically, it proposes a hybrid architecture combining heuristic extraction, semantic activation, and compositional synthesis—moving beyond conventional single-rule selection paradigms. It introduces a quantum-cognition-inspired mechanism of semantic interdependence and rhetorical framing to enable dynamic entanglement and meaning generation across heterogeneous strategic rules, alongside a semantic interference metric for adaptive cross-domain knowledge integration. Evaluated on Meta and FTC case studies, the framework produces logically coherent, context-adapted strategic reasoning narratives. Preliminary semantic metrics demonstrate significant improvements in coherence and contextual sensitivity. The core contribution is the first systematic application of quantum cognition principles to strategic heuristic fusion, establishing a novel paradigm for modeling complex decision-making under uncertainty.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Develop hybrid architecture for agent-augmented strategic reasoning
• Fuse conflicting heuristics into context-sensitive narratives
• Validate framework via case study and semantic metrics

Innovation

• Hybrid architecture combining heuristic extraction and synthesis
• Semantic interdependence inspired by quantum cognition
• Fuses conflicting heuristics into context-sensitive narratives