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New framework MASC enhances AI client role-playing for counseling

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MASC, a Multi-Agent Self-Calibration framework designed to improve the consistency of AI-simulated clients in psychological counseling. This framework addresses issues like persona drift and unrealistic emotional states by incorporating construct-guided generation, collaborative refinement, consistency verification, and memory-based revision. To evaluate these simulations, a new benchmark called CRPC-Bench has been introduced, which includes client profiles, personality traits, and turn-level psychological dynamics. AI

IMPACT This research could lead to more reliable AI-driven tools for training mental health professionals and conducting psychological research.

RANK_REASON The cluster contains a research paper detailing a new framework and benchmark for AI simulation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework MASC enhances AI client role-playing for counseling

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The cluster contains a research paper detailing a new framework and benchmark for AI simulation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shixin Peng, Kun Jiang, Jiaxing Zheng, Qihao Yang, Jingying Chen ·

    MASC: A Multi-Agent Self-Calibration Framework with Latent Construct Alignment for Consistent Client Role-Playing in Psychological Counseling

    arXiv:2610.08250v1 Announce Type: new Abstract: Large language models are increasingly used to simulate clients for counselor training and psychological counseling research, but reliable simulation requires clients to remain psychologically coherent across extended interactions. …

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