NanoMA: How Does Agent ReAct Enables Flexible Multi-Agent Topologies?

Long-horizon tasks continually reveal new dependencies and evidence, requiring multi-agent collaboration to adapt during execution. Central planning concentrates coordination in one controller, while recursive delegation distributes task decomposition but offers limited support for revising existing cross-branch collaboration. We introduce NanoMA, a multi-agent ReAct (Reasoning + Acting) framework that treats collaboration structure as an executable and dynamically revisable component of problem solving. Within each agent's ReAct loop, execution actions are combined with four topology actions—spawn, kill, query, and send—for creating, terminating, observing, and contacting agents. Execution traces show agents combining these actions in response to task feedback to form diverse collaboration structures, rather than following predefined topology templates. On RepoZero-Py2JS Hard with GPT-5.6 Luna High, NanoMA achieves a 66.91% all-pass rate, exceeding the best-performing baseline in our comparison by 13.67 percentage points. On EdgeBench, it obtains the highest overall score among the evaluated frameworks with both GPT-5.6 Luna and Qwen3.7-Plus. Communication ablations show higher task accuracy with cross-branch query access and better performance with targeted messages than with broadcasting. Code is available anonymously at https://anonymous.4open.science/r/NanoMA-private-2CC6/.