Scalable Oversight in Multi-Agent Systems: Provable Alignment via Delegated Debate and Hierarchical Verification

Michael Bronikowski

Keywords: multi-agent systems, scalable oversight, hierarchical debate, delegated verification, AI alignment, P AC-Bayesian risk bounds, cost-aware routing, collusion resistance, retrieval-augmented verification

TL;DR: We introduce HDO, a hierarchical delegated oversight framework that provably reduces misalignment risk and improves efficiency in multi‑agent AI via cost‑aware routing to specialized verifiers

Abstract: As AI agents proliferate in collaborative ecosystems, ensuring alignment across multi-agent interactions poses a profound challenge: oversight scales sublinearly with agent count, amplifying risks of collusion, deception, or value drift in long-horizon tasks. We introduce Hierarchical Delegated Oversight (HDO), a scalable framework where weak overseer agents delegate verification to specialized sub-agents via structured debates, achieving provable alignment guarantees under bounded communication budgets. HDO formalizes oversight as a hierarchical tree of entailment checks, deriving PAC-Bayesian bounds on misalignment risk that tighten with delegation depth. Our policy routes disputes to cost-minimal verifiers (e.g., cross-model NLI or synthetic data probes), enabling 3–5× efficiency over flat debate baselines. Empirically, on WebArena and AgentBench suites, HDO reduces collective hallucination rates by 28% while maintaining 95% oversight accuracy at 2× lower tokens than human-in-the-loop methods. Ablations reveal robustness to adversarial collusion, with failure modes taxonomized by delegation granularity. By bridging theoretical oversight [Irving et al., 2018, Christiano et al., 2018] with agentic scalability [Park et al., 2023], HDO paves the way for safe multi-agent deployment in 2026’s agentic paradigms.

Submission Number: 325