Causal Signatures for Cross-Domain Reasoning - Enabling Functional Analogy Through WHAT/WHY/HOW Alignment

Description Structural Reasoning, Functional Analogy, and Cross-Domain Intelligence This paper introduces Causal Signatures as a core mechanism enabling cross-domain reasoning within the Semantic Topology Reasoning Architecture (STRA). Rather than relying on surface similarity, symbolic overlap, or statistical correlation, the framework defines analogy as structural equivalence in causal function, expressed through aligned WHAT / WHY / HOW relationships. A causal signature captures how an entity behaves, why it behaves that way, and what functional role it fulfills—independent of domain, representation, or vocabulary. This allows reasoning systems to identify deep functional analogies (e.g., biological, mechanical, cognitive, or social systems) even when no shared symbols or features exist. Within STRA, causal signatures operate as invariant reasoning anchors across semantic topologies. They enable analogical transfer, abstraction, and generalization without requiring token-level embeddings, vector similarity, or gradient-trained representations. This paper formalizes: The definition and structure of causal signatures The WHAT / WHY / HOW triadic alignment model How causal signatures are discovered, compared, and reused across domains Why functional analogy requires causal structure rather than representational similarity How this mechanism avoids hallucination and false analogy common in large language models The work positions causal signatures as a foundational bridge between symbolic reasoning, conceptual abstraction, and structure-centric intelligence. Abstract Cross-domain reasoning requires identifying functional equivalence across systems that share no surface similarity. Current AI systems rely on statistical correlation, embedding proximity, or symbolic overlap, which limits their ability to form robust analogies across domains. This paper introduces Causal Signatures, a structure-centric representation that captures functional invariants through aligned WHAT / WHY / HOW relationships. A causal signature encodes what a system does, why it exists, and how it operates, independent of domain-specific representation. Integrated within the Semantic Topology Reasoning Architecture (STRA), causal signatures enable analogical reasoning, abstraction, and transfer without token-level processing or gradient-based learning. The framework allows reasoning systems to identify deep functional similarity across biology, engineering, cognition, and social systems, even in the absence of shared symbols. We demonstrate how causal signatures function as cross-domain reasoning anchors, enabling robust analogy formation while reducing spurious similarity, hallucination, and representational bias. This work establishes causal structure—not representation—as the primary substrate of general intelligence. Background and Context This paper is part of the Semantic Topology Reasoning Architecture (STRA) research program, which proposes a shift from parameter-centric AI models to structure-centric reasoning systems. It directly complements and extends the following works: Semantic Topology Reasoning Architecture (STRA): From Parameter-Centric Models to Structure-Centric Reasoning(10.5281/zenodo.18207533) Activation Arrays as Working Memory in Semantic Reasoning Systems(10.5281/zenodo.18207539) Selection Pressure & Natural Selection of Thought – Evolutionary Dynamics in Semantic Reasoning Systems(10.5281/zenodo.18207552) Transform Learning: Procedural Reasoning Without Programs in Semantic Topology Architectures(10.5281/zenodo.18207558) The Semantic Abacus: Quantitative, Symbolic, and Skill-Based Reasoning in Semantic Topology Architectures(10.5281/zenodo.18207560) Together, these papers define a unified architecture for reasoning without tokens, embeddings, or opaque parameter tuning. Key Contributions Causal Signature FrameworkDefines a domain-independent representation of functional causality. WHAT / WHY / HOW Alignment ModelFormalizes functional reasoning as triadic causal alignment rather than surface similarity. Cross-Domain Analogy MechanismEnables deep analogical transfer across unrelated domains without shared symbols. Structure-Centric ReasoningDemonstrates how causal structure replaces embeddings and statistical similarity. Hallucination ResistanceReduces false analogy and spurious inference by requiring causal coherence. Integration with STRAShows how causal signatures operate within semantic topologies and activation-based reasoning. Research Impact This work contributes to artificial intelligence, cognitive science, and knowledge representation by: Reframing analogy as causal structure alignment Enabling cross-domain reasoning without representation-level similarity Providing a transparent alternative to embedding-based analogy Supporting interpretable, auditable reasoning processes Advancing structure-centric AI architectures beyond token-based models Access and Documentation ORCID: https://orcid.org/0009-0003-4876-9273 GitHub: https://github.com/Neuron-Soul-AI/Neuron-Soul-AI Academia.edu: https://independent.academia.edu/MarceloTeixeira214 LinkedIn: https://www.linkedin.com/in/marcelo-emanuel-paradela-teixeira-702082382/ Email: marcelo.soul.ai@gmail.com License: Creative Commons BY‑NC 4.0 © Marcelo Emanuel Paradela Teixeira 2026