Contemporary large language models demonstrate strong performance on inductive reasoningtasks but apply statistical pattern generalisation uniformly across all query types. Ten reasoningcategories critical for enterprise decisions — causal intervention, counterfactual inference,abductive diagnosis, formal deduction, non-monotonic belief revision, analogical reasoning,probabilistic reasoning, temporal reasoning, inductive generalisation, and commonsenseinference — are systematically underserved by autoregressive architectures because theyrequire structural mechanisms that token prediction cannot maintain. We present EpistemicEigen, a system that enforces reasoning type as a hard architectural constraint through anepistemic routing layer, a formal logic verification bridge using the Z3 SMT solver, and aphysics-based commitment mechanism using the Ising model. The system provides completecoverage of all ten reasoning types, produces formally auditable decision records meetingregulatory requirements across financial, healthcare, pharmaceutical, and legal domains, andcompounds its structural advantages through physics-grounded domain memory. A 24-questionempirical benchmark against ChatGPT 5.2, scored on logical rigor rather than answercoherence, yields 87% accuracy for Epistemic Eigen versus 52% for ChatGPT 5.2. The35-point gap is compounded by a qualitative asymmetry in failure modes: Epistemic Eigen haltswith precise diagnostics when evidence is insufficient; ChatGPT 5.2 produces confidentanswers from unverified premises with no error signal
Madhava Bekkem (Mon,) studied this question.