Internal Structural Mechanisms and Dialogue Stability in Multi-Agent Language Systems: An Ablation Study

This work investigates how internal structural mechanisms influence conversational stability in multi-agent language systems. While most agent architectures emphasize external tools and prompting strategies, this study examines dialogue dynamics emerging from internal regulation processes. Through controlled ablation experiments, four system configurations were evaluated: baseline dialogue, structured dialogue seeding, observer-based interventions, and internal state modulation. Dialogue behavior was measured using circularity rate, progress rate, and intervention utility metrics. Results indicate that structured dialogue seeding significantly reduces conversational circularity while maximizing semantic progression. Observer interventions provide measurable corrective effects, and internal state modulation contributes to moderate stabilization. The findings suggest that dialogue coherence emerges primarily from interaction structure rather than language model capability alone.