Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Systems
State Compression
Agent Heterogeneity
Scaling Multi-Agent Systems with Prospect-State Propagation
🤖 AI Summary
本文针对多智能体系统中智能体异质性减少的问题，提出了一种基于前景理论的前景状态传播方法（PspMAS），通过分离前景状态和语义状态来提高系统的可扩展性。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Prospect-State Propagation
Multi-Agent Systems
Scalability
Heterogeneity Injection
Large Language Models
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
