# HELENA:Hierarchical Sparse Coordination over a Union of Complementary Topologies for MAS

arXiv: https://arxiv.org/abs/2608.04634 · HTML: https://mlsanity.com/paper/2608.04634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- First posted on arXiv 5 Aug 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Abstract (arXiv)

LLM-based multi-agent systems (MAS) typically optimize a single topology, restricting reasoning to a narrow trajectory and limiting comprehensive analytical capacity. Naively merging multiple topologies into a composite graph introduces redundant noise propagation across irrelevant connections, degrading solution quality. To address this dilemma, we propose Hierarchical Sparse Coordination over a Union of Complementary Topologies for MAS (HELENA), a multi-agent framework that balances diverse reasoning paths with sparse task-dependent execution. helena constructs a union MAS graph from complementary candidate topologies selected via Monte Carlo Tree Search and Determinantal Point Process, broadening the reasoning trajectory for comprehensive analysis of complex problems. A Hierarchical Sparse Coordination module then activates only a sparse subgraph at each step while agents exchange compressed latent briefs to suppress redundant noise propagation. Finally, a Local Self-Refinement stage identifies decision units with discrepancy evidence and rewrites them only when contrastive evidence simultaneously confirms a reliable solution-side failure and a challenger-side improvement. Experiments across eight benchmarks show that helena achieves state-of-the-art results on all benchmarks, with an average gain of pctup3.47 over the strongest baseline and up to pctup10.34 on MMLU-Pro, achieving larger improvements on harder benchmarks at a reasonable additional cost.