This paper proposes a smarter way for LLMs to reason by splitting work across agents that share one workspace.

The problem is that even strong reasoning models still break on harder multi-step tasks because they do not carry out logic reliably all the way through.

The system, called BIGMAS, builds a small graph of specialist agents for each problem, rather than using one fixed chain every time.

Every agent reads and writes through a shared workspace, while a separate controller sees the whole state and picks the next useful step.

The authors tested it on 3 puzzle tasks across 6 frontier models, covering arithmetic expression search and multi-step planning.

It improved results on every model and task, with examples like 12% to 30% on Six Fives and 57% to 93% on Tower of London.

What matters is that the paper shows reasoning can improve from better system structure, not only from making a single model think longer.

----

Paper Link – arxiv. org/abs/2603.15371

Paper Title: "Brain-Inspired Graph Multi-Agent Systems for LLM Reasoning"