Title: Layered Contextual Alignment: Multi-Agent Coordination for Web Automation Through Hierarchical Preference Learning
Primary area: reinforcement learning
Keywords: multi-agent systems, contextual alignment, coordination theory, distributed systems, hierarchical alignment, lateral alignment

Abstract: We present Layered Contextual Alignment (LCA), a hierarchical coordination framework that enables efficient multi-agent web automation through preference-based alignment without explicit communication. While existing approaches suffer from either prohibitive communication overhead or poor coordination quality, LCA introduces a three-layer alignment mechanism that captures global objectives, shared session states, and individual agent observations, enabling emergent coordination behaviors. Through comprehensive experiments on 25 diverse web automation tasks, we demonstrate that LCA achieves 97.8\% task success rate with 4.21$\times$ speedup over sequential processing. Our theoretical analysis establishes convergence guarantees with $O(n \log n)$ communication complexity and identifies a critical phase transition at alignment threshold $\tau = 0.65$, which we validate empirically through extensive ablation studies. Statistical validation across 1000 runs confirms significant improvements over 18 baseline systems ($p < 0.001$), with large effect sizes (Cohen's $d > 0.8$) for key comparisons. The framework demonstrates practical utility through production deployment, processing 10,000+ pages daily within reasonable resource constraints. Our work establishes that lightweight hierarchical coordination, rather than complex communication protocols or massive parallelization, provides the optimal balance between efficiency and quality for multi-agent web automation. The identification of universal phase transition behavior in coordination systems provides theoretical insights applicable to broader multi-agent coordination challenges.