Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems

Summary

Position paper arguing the field should shift to topology-aware multi-agent systems that explicitly model and dynamically optimize how agents interact, decomposing a system into agents, communication links, and topology and linking these to adaptability, efficiency, robustness, and fairness. The field-level statement that structure is a first-class design variable rather than an implementation detail, which is the premise the paper's experiment operationalizes. It also flags evaluation difficulty in MAS, supporting the argument that benchmark accuracy alone is an inadequate outcome measure.

Contribution

Queued — not yet read.

Method

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Relevance to RISE

Surfaced by the 2026-08-29 literature sweep as a medium-priority candidate for the Architecture as Epistemology (ISR) paper (angle: MAS topology / structure vs. aggregation rule). Verified against: Opened https://arxiv.org/abs/2505.22467 (arXiv abstract page, v3 17 Oct 2025)

Critique / open questions

Queued — not yet assessed.

Key quotes

Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. (abstract)

However, the topology of these systems--how agents in MASs should be configured, connected, and coordinated--remains largely unexplored. (abstract)