We propose a formally grounded framework for epistemic regulation, including monitoring and alignment, in multi-agent systems comprised of generative reasoning agents (e.g. Large Reasoning Models) that interact in a shared environment, modeled as a co-adaptive game between reasoning agents and an uncertain, possibly adversarial/non-stationary environment. By including diverse reasoning agents acting as higher-level players which serve as meta‐agents or critics, the aim is to collectively probe and expand the limits of a shared-knowledge space. At the core of this setup lies a multi-objective optimization problem: to simultaneously minimize epistemic uncertainty, maximize agreement with a verifiable knowledge base, and avoid collapse into undecidable, divergent, or non-halting inference chains. The AI agents are heterogeneous and specialised by design, interact through strategic critique and synthesis, and converge (under bounded computability constraints) to an epistemic equilibrium, a state of stabilized, self-consistent belief formation.
Internally, we model the AI inference loop as a stratified meta-reasoning architecture: queries falling within a formally decidable domain are resolved with provable certainty, while queries outside this domain (e.g., out-of-distribution or ill-posed queries) are flagged and approximated with full epistemic transparency. This design echoes the halting problem and aligns with recent ideas from co-evolutionary cognition and edge-of-chaos dynamics in AI systems. In particular, we analyze the conditions under which hallucinations (overconfident errors) propagate or dissipate in recursive AI agent networks, how strategic heterogeneity suppresses epistemic collapse, and what edge-of-chaos and co-evolutionary dynamics are at play.
The work is directly applicable to LLM red-teaming, adversarial prompting, RAG pipelines, generative agents in specific technical domains, and self-auditing AI systems. Through its technical contributions, this model serves as a tool for understanding how generative AI systems co-evolve with human epistemic norms, and offers a path towards next‑generation autonomous cognitive systems that can safely self‑extend their reasoning reach and reliably integrate knowledge.
