Emergent Communication Topologies from Local Reasoning Refinements in Multi-Agent Systems

Effective collaboration in Large Language Model (LLM)-based multi-agent systems (MAS) requires communication that adapts to how agents can improve one another's evolving reasoning. However, existing approaches based on task or query information or overall agent assessments do not directly identify which reasoning states can provide useful corrections to a particular peer. We introduce refinement-specific communication and instantiate it in LoRIT, a training-free framework that organizes communication around directional, recipient-specific opportunities for reasoning improvement. Agents assess how their current reasoning states can correct or complete one another, and these local relations induce a global communication structure that accommodates mutual refinement and evolves with reasoning. This couples reasoning updates with topology self-adaptation without imposing a global agent ranking or optimizing a separate graph-level objective. Experiments across diverse reasoning and coding tasks demonstrate broad improvements over single-agent and multi-agent baselines with both weak and strong LLM backbones. With stronger refinement assessment, LoRIT enables groups of smaller models to achieve collective performance comparable to or exceeding that of larger single-agent models. These results highlight the potential of refinement-specific communication to turn complementary information in imperfect individual solutions into stronger collective reasoning.