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    <abstract>Current LLM-based multi-agent systems (MAS) periodically compress intermediate states to reduce inference-time token consumption, thereby attempting to incorporate more agents. However, naive scaling strategies face challenges. For example, in economic simulations, large-scale MAS typically discard semantically rich economic states, i.e., agent behavioral trajectories, which are key drivers of macroeconomic fluctuations. In this paper, we reveal a phenomenon in which agent heterogeneity gradually decreases during simulation, and propose Prospect-State Propagation for Multi-Agent Systems (PspMAS). Inspired by prospect theory, PspMAS decouples each agent’s micro state into a compact Prospect State and an expressive Semantic State. The former records psychological traces through a lightweight, parallelizable propagator and continuously injects heterogeneity into the system. The latter leverages the strong perception, reasoning, planning, and decision-making abilities of LLMs. These two components work complementarily, providing a scalable LLM-based multi-agent simulation solution.</abstract>
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%0 Conference Proceedings
%T Scaling Multi-Agent Systems with Prospect-State Propagation
%A Chen, Zhimei
%A Chen, Mu
%A Karray, Fakhri
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-scaling
%X Current LLM-based multi-agent systems (MAS) periodically compress intermediate states to reduce inference-time token consumption, thereby attempting to incorporate more agents. However, naive scaling strategies face challenges. For example, in economic simulations, large-scale MAS typically discard semantically rich economic states, i.e., agent behavioral trajectories, which are key drivers of macroeconomic fluctuations. In this paper, we reveal a phenomenon in which agent heterogeneity gradually decreases during simulation, and propose Prospect-State Propagation for Multi-Agent Systems (PspMAS). Inspired by prospect theory, PspMAS decouples each agent’s micro state into a compact Prospect State and an expressive Semantic State. The former records psychological traces through a lightweight, parallelizable propagator and continuously injects heterogeneity into the system. The latter leverages the strong perception, reasoning, planning, and decision-making abilities of LLMs. These two components work complementarily, providing a scalable LLM-based multi-agent simulation solution.
%U https://aclanthology.org/2026.findings-emnlp.1585/
%P 33754-33766

Markdown (Informal)

[Scaling Multi-Agent Systems with Prospect-State Propagation](https://aclanthology.org/2026.findings-emnlp.1585/) (Chen et al., Findings 2026)

• Scaling Multi-Agent Systems with Prospect-State Propagation (Chen et al., Findings 2026)

ACL

• Zhimei Chen, Mu Chen, and Fakhri Karray. 2026. Scaling Multi-Agent Systems with Prospect-State Propagation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 33754–33766, Budapest, Hungary. Association for Computational Linguistics.