Scaling Multi-Agent Systems with Prospect-State Propagation

Zhimei Chen; Mu Chen; Fakhri Karray

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arXiv · 2609.08033

Scaling Multi-Agent Systems with Prospect-State Propagation

Zhimei Chen·Mu Chen·Fakhri Karray

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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.

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Zhimei Chen, Mu Chen, Fakhri Karray. 2026-09-07. Scaling Multi-Agent Systems with Prospect-State Propagation. https://arxiv.org/abs/2609.08033

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