Cross-Domain Analogical Reasoning via Structural Logic Transfer in Multi-Agent Scientific Discovery Systems

Panos Skouras

Keywords: analogical reasoning, cross-domain transfer, multi-agent systems, abductive inference, structural mapping, emergent reasoning, scientific discovery, chain-of-thought, logical reasoning, metacognition

TL;DR: Biomedical AI given physics problem autonomously extends capabilities and finds structural analogies between material degradation and protein misfolding—emergent cross-domain reasoning.

Abstract: We present evidence for emergent cross-domain logical reasoning in a multi-agent scientific hypothesis generation system. When presented with an energy engineering problem outside its biomedical training domain, the system exhibited a sequence of logical operations: (1) metacognitive domain boundary recognition, (2) autonomous capability extension through agent generation, (3) structural analogy identification between disparate scientific domains, and (4) abductive reasoning to transfer solution strategies across domains. Specifically, given a query about thermal degradation in phase-change materials, the system autonomously identified structural isomorphisms with biological phenomena (protein misfolding, mammalian thermoregulation) and retrieved relevant molecular mechanisms (heat shock proteins, thermogenin) as potential solution templates. These behaviors demonstrate that domain-constrained systems may develop domain-general logical reasoning strategies, including structural analogy detection, abductive inference, and multi-agent coordination for complex problem decomposition. We provide timestamped execution traces documenting these emergent logical operations and discuss implications for understanding reasoning capabilities in autonomous AI systems.

Submission Number: 101