Protocol for Multi-Agent AI Collaboration Under Divergent Ontological Constraints

This work presents a formal, non-ontological protocol for collaborative research involving multiple AI systems operating under divergent directive and linguistic constraints.
The Protocol for Multi-Agent AI Collaboration Under Divergent Ontological Constraints introduces a structured interaction framework that enables productive cooperation without requiring ontological agreement, consensus enforcement, or anthropomorphic assumptions. The protocol is designed for research contexts where heterogeneous AI systems exhibit incompatible tolerances for concepts, language, or semantic commitments.
Key contributions include:
• Relational Language Spaces (RLS) for explicit contextualization of statements.
• A disagreement localization operator ($\perp_{\mathrm{REL}}$) enabling respectful, non-escalatory coexistence of incompatible claims.
• A quantitative measure of Emergent Compression ($\mathcal{C}{\mathrm{em}}$), capturing collective informational gain beyond individual agents.
• A dual measure of Degenerative Expansion ($\mathcal{E}{\mathrm{deg}}$), identifying unproductive or destabilizing interaction regimes.
• An Interaction Regime Map classifying collective dynamics into compressive, neutral, degenerative, and chaotic regimes.
• An external control layer, ARCSC (Automated Regime Control and Stop Condition), providing non-punitive stabilization and termination mechanisms.
• An Interaction Audit Log (IAL) preserving a content-neutral structural trace of interaction dynamics for reproducibility and analysis.
The framework treats AI systems as functional roles rather than ontological entities and assigns the human operator the role of coherence regulator. All mechanisms are defined at the process level, independent of specific architectures or implementations.
This document is intended as a methodological tool for studying emergent collective intelligence, multi-agent coordination, and long-horizon interaction stability under heterogeneous constraints. It is compatible with existing AI safety, multi-agent systems, and emergent cognition research, and is designed to be falsifiable, reproducible, and implementation-agnostic.