Title: Stochastic Self-Organization in Multi-Agent Systems
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: multi-agent systems, contribution estimation

Abstract: Large Language Models (LLMs) have enabled multi-agent systems (MAS) where agents collaborate to solve tasks beyond the reach of a single model. Yet most existing approaches rely on fixed topologies, pretrained graph generators, optimization over edges, or external LLM judges, thereby adding complexity. We introduce a response-conditioned framework that adapts communication on-the-fly. Agents independently generate answers and assess peer contributions using a Shapley~value-inspired approximation. A directed acyclic communication graph (DAG) is then constructed to route information from high-contribution agents to others, ensuring stable and efficient message passing without additional supervision or training. We provide a theoretical analysis showing that multiple agents increase the chance of correctness and that the correct answers naturally dominate information flow. Experiments with both strong and weak LLM backends demonstrate robust performance, with significant gains in the weak regime where prior methods collapse.