THEIA: Learning Complete Kleene Three-Valued Logic in a Pure-Neural Modular Architecture

We present THEIA, a modular neural architecture that learns complete Kleene three-valued logic (K3) end-to-end without any external symbolic solver, and investigate what architectural prior enables compositional generalization under uncertainty. THEIA processes four mathematical domains (arithmetic, order, set membership, propositional logic) through dedicated engines that converge in a final logic module. Trained on a 2M-sample dataset with input space ~3.4x10^13, it achieves 12/12 Kleene K3 rule coverage across 5 seeds in 9.2 +/- 3.5 minutes (5.6x faster than a parameter-comparable Transformer under matched settings). A mod-3 sequential composition experiment generalizes from 5-step training to 500-step evaluation at 99.97% +/- 0.02% -- a result that critically depends on structured inductive bias: replacing the four-engine backbone with a flat MLP collapses length generalization to chance by 50 steps regardless of capacity (both 0.80M and parameter-matched 2.75M variants fail), while a pre-LN TF8LTuned Transformer baseline (3,582,147 params) trained under the identical protocol reaches 99.24% at 500 steps (Appendix D). Mechanistic probing reveals that modularity induces a delayed verdict: upstream engines encode domain-specific variables without committing to the final truth value (probe accuracy <= 74% uncertainty-only ceiling), with the verdict emerging only at the Logic Engine boundary -- causally confirmed by activation patching (100% flip rate on 986 matched pairs, replicated across n=5 seeds; 100.0% aggregate). The Transformer baseline reaches equivalent correctness through a qualitatively different representational trajectory (contraction then expansion), suggesting that modular and monolithic architectures implement distinct compositional strategies.