SPASM：面向多轮对话生成的稳定人格驱动代理模拟
SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation
AI总结：
本文提出SPASM框架，通过人格创建、对话生成和终止检测模块，提升多轮对话生成的稳定性，采用ECP方法减少人格漂移和回声现象。
摘要：
大型语言模型越来越多地用于教学、支持和咨询等多轮场景，其可靠性依赖于在长周期内保持一致的角色、人格和目标。当LLM用于生成合成对话用于训练和评估时，LLM-LLM对话可能积累身份相关故障，如人格漂移、角色混淆和
英文摘要：
Large language models are increasingly deployed in multi-turn settings such as tutoring, support, and counseling, where reliability depends on preserving consistent roles, personas, and goals across long horizons. This requirement becomes critical when LLMs are used to generate synthetic dialogues for training and evaluation, since LLM--LLM conversations can accumulate identity-related failures such as persona drift, role confusion, and "echoing", where one agent gradually mirrors its partner. We introduce SPASM (Stable Persona-driven Agent Simulation for Multi-turn dialogue generation), a modular, stability-first framework that decomposes simulation into (i) persona creation via schema sampling, plausibility validation, and natural-language persona crafting, (ii) Client--Responder dialogue generation, and (iii) termination detection for coherent stopping. To improve long-horizon stability without changing model weights, we propose Egocentric Context Projection (ECP): dialogue history is stored in a perspective-agnostic representation and deterministically projected into each agent's egocentric view before generation. Across three LLM backbones (GPT-4o-mini, DeepSeek-V3.2, Qwen-Plus) and nine Client--Responder pairings, we construct a dataset of 4,500 personas and 45,000 conversations (500 personas X 10 conversations per pairing). Ablations show ECP substantially reduces persona drift and, under human validation, eliminates echoing; embedding analyses recover persona structure and reveal strong responder-driven interaction geometry. Our code is available at https://github.com/lhannnn/SPASM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
