Transfer of AgentsNet-Measured Performance to Asynchronous or Dynamic Communication Settings

Ascertain how well performance of large language model-based multi-agent systems measured under synchronous message-passing in the LOCAL model transfers to asynchronous or dynamically constrained communication protocols commonly encountered in real-world multi-agent settings.

Background

AgentsNet adopts a synchronous message-passing protocol inspired by the LOCAL model from distributed computing to enable controlled evaluation of decentralized coordination and communication.

The authors note that many real-world multi-agent systems operate asynchronously or under dynamic communication constraints, raising uncertainty about the extent to which benchmarked performance under synchronous assumptions generalizes to these settings.

References

Many real-world multi-agent systems operate asynchronously or under dynamic communication constraints, and it remains unclear how well performance would transfer under such conditions.

— AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs  (2507.08616 - Grötschla et al., 11 Jul 2025) in Appendix: Limitations

Whether this locality ultimately yields lower communication cost, higher acceptance, stronger privacy, or better decentralized performance remains a research question rather than an assumption of the present experiments.

— Atomic Common-Day Invoice Clearing under Causal Daily Scheduling: Path-Enabled and Bounded-Cycle Policies  (2608.23060 - Rosa et al., 24 Aug 2026) in Section 1, Introduction; Section 6.3, “Path-enabled clearing as an agentic research agenda”

Whether decentralized message-passing can remain correct and stable under realistic conditions (e.g., asynchrony, heterogeneity, partial participation, or justify its complexity over simpler alternatives, remains uncertain~\citep{scardapane2020distributed,blumenkamp2022framework}, and further research is needed to understand performance bounds and practical benefits.

— From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning  (2609.02984 - Bourgerie et al., 2 Sep 2026) in Section 6.1, paragraph “Open challenges”