Recursive Adversarial Engine: A Stability-Oriented Architecture for Reliable Large Language Model Multi-Agent Systems

Large language model (LLM) based multi-agent systems are rapidly migrating from laboratory prototypes to production deployments in finance, scientific research, industrial digital twins, and urban scheduling. As deployment scales, however, a family of systemic pathologies persists: decision oscillation driven by zero-sum agent interactions, cascade propagation of hallucinated content, long-horizon behavioral drift, value misalignment, and unresolved inter-agent conflict. Existing orchestration paradigms, which rely on instruction dispatch, greedy optimization, or static rules, address only shallow coordination and provide no principled mechanism for convergence. This paper proposes the Recursive Adversarial Engine (RAE), a runtime architecture for multi-agent systems organized around three propositions: bounded adversarial interaction supplies the error-detection signal, recursive correction supplies the error-elimination mechanism, and dynamic checks and balances supply the stability guarantee. We specify a four-layer reference architecture comprising adversarial verification, recursive correction, checks-and-balances scheduling, and steady-state output, and we characterize its convergence behavior through a Lyapunov-style stability argument. A qualitative comparison with self-play, multi-agent debate, alignment, and consensus frameworks delineates the paradigm difference. Engineering implications for high-stakes domains, long-horizon robustness, auditability, and middleware integration are analyzed, and open challenges are identified.