Deliberate Topology Selection as a First-Class Design Variable: How Multi-Agent Systems Learn When to Reconfigure, Prune, and Restructure Communication Graphs

Version 2 — revised in response to an external structural review and an automated critique pass. See "Response to Review" appendix in the PDF for the change log.

Multi-agent systems (MAS) built on large language models (LLMs) have converged on a common assumption: that the communication topology connecting agents is either fixed at design time or left entirely to unconstrained dynamic negotiation. This paper argues, through a heuristic reading of seven recent arXiv preprints spanning cs.MA, cs.DC, and cs.NI, that this binary is a false dichotomy — and that the most effective systems in the corpus share a structural pattern we call *deliberate topology selection*: the explicit, policy-guided choice of which edges to activate, suppress, or reconfigure at runtime, conditioned on task state, budget, and agent confidence. This is a heuristic reading, not a derivation from a shared formalism; the papers do not share a unified mathematical framework, but they converge on a common engineering diagnosis. The synthesis draws on findings about: training-free communication pruning via Theory-of-Mind confidence signals [corpus:arxiv:2605.29612]; task-conditioned structural priors that guide posterior orchestration [corpus:arxiv:2605.25746]; nucleus-electron hierarchies that separate stable backbones from dynamically activated agents [corpus:arxiv:2605.26178]; game-theoretic minimax method selection under scenario uncertainty [corpus:arxiv:2606.02383]; decentralized event-triggered synchronization under censored feedback [corpus:arxiv:2605.27076]; federated market-based allocation over tree-structured service DAGs [corpus:arxiv:2605.27106]; and infrastructure-layer topology proposals for V2X safety communication [corpus:arxiv:2605.25431]. The central falsification path is concrete: if a static, task-agnostic topology trained once on a broad corpus consistently matches or outperforms deliberate topology selection across coordination-heavy benchmarks, the thesis collapses. The abstract reports no such result in any corpus paper; the claim remains a candidate structural pattern worth investigating, not an established law. ---

Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename.

Cited arXiv preprints: 2605.25431, 2605.25746, 2605.26178, 2605.26448, 2605.27076, 2605.27106, 2605.27466, 2605.27787, 2605.28984, 2605.29511, 2605.29612, 2605.30102, 2605.30802, 2606.02383

AI disclosure. This work was produced with an agentic AI research apparatus operated by Saluca Labs. The apparatus drafted, searched and analysed under direction. Cristian Ruvalcaba is the human author and is accountable for the content. No AI system is listed as an author or contributor, because authorship entails accountability that a model cannot hold; this disclosure is the credit, and it is deliberately the whole of it.