From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913948620554240 |
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| author | Ghisellini, Renato Pareschi, Remo Pedroni, Marco Raggi, Giovanni Battista |
| author_facet | Ghisellini, Renato Pareschi, Remo Pedroni, Marco Raggi, Giovanni Battista |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13768 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoning Ghisellini, Renato Pareschi, Remo Pedroni, Marco Raggi, Giovanni Battista Artificial Intelligence I.2.7 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. |
| title | From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoning |
| topic | Artificial Intelligence I.2.7 |
| url | https://arxiv.org/abs/2507.13768 |