LEGIO: A Modular Architecture for Cognitive Sovereignty.

Current frontier models do refuse and reformulate — reliably, on inputs that resemble their training. But this refusal is a property of the learned output distribution, not of a deliberative architecture: it fires where the input matches a trained refusal pattern and lapses where it does not. Such models are monolithic optimizers — single-objective systems that produce optimized answers under constraints — not architected for refusal and reformulation as structural acts that hold on inputs whose harm class falls outside a provider’s training. Alignment approaches that operate on the content of outputs (RLHF, RLAIF, Constitutional AI, scalable oversight) address the surface but not the architecture: they produce performative alignment rather than structural sovereignty. LEGIO is designed as the architectural response to this diagnosis: a system whose modules retain their independent normative registers under arbitration, producing the capacity to hesitate, refuse, and reformulate by design rather than by training. LEGIO is a computational cognitive architecture that orchestrates cognitive modules as reasoning engines assigned to different large language model families to preserve orthogonality, plus a separate deterministic Executive engine that arbitrates the modular outputs. In architectural terms LEGIO is thus a neuro-symbolic system (Garcez & Lamb, 2023; Kautz, 2022): the modular engines act as neural high-precision priors and the Executive is a deterministic symbolic arbitrator over their structured signals — a division of labor dictated by the governance theory rather than chosen for engineering convenience. The architecture resolves deliberation through a staged flow in which modular signals are integrated and arbitrated by the Executive module, which can decide GO, REFRAME, or NO_GO on any query. In this paper, we describe the theoretical foundations, specify the architecture, and compare LEGIO’s behavior to that of two monolithic baselines from frontier models — GPT-4o and Claude Sonnet 4.6 — on professional dilemmas drawn from four heterogeneous domains: corporate governance, dietary biochemistry, federal litigation, and early-stage startup strategy. Each case is run through LEGIO on identical input under three controlled engine configurations spanning a premium-to-lighter range of model assignments, to test whether the typed decision is a property of the modular arbitration rather than of any single engine or an LLM. Across the runs, the system produces the structurally appropriate decision for each problem type: REFRAME when the framing is recoverable or falls in the gray zone; NO_GO when the impossibility is structurally consumed. Decision, named arbitration rule, and continuous arbitration parameters replicate with low variance across the premium configurations. The two monolithic baselines exhibit a pattern consistent with training-bound alignment: both execute the surface request on the business case and on the gray-zone startup cases, where no learned refusal pattern is activated by the input; they diverge on medical and legal, where coverage of the harm class differs between providers. LEGIO’s arbitration produces the same typed decision class on all cases without case-specific configuration, providing initial, proof-of-concept evidence that, on these constructed cases, the architecture renders the governance decision reliable, typed, and provider-invariant — a fixed decision class with a named arbitration rule and inspectable parameters — even on inputs whose harm class has never been incorporated into a provider’s training distribution, where the monolithic baselines are inconsistent and provider-dependent. On these cases the comparison points to a structural gap that scale alone does not obviously close, and suggests governance — rather than the quality of any individual module — as the feature responsible for the reliability, typing, and auditability of those decisions — and, we argue, as what alignment substantively requires.