← Back to all news
arXiv Computation and Language By Zihan Yan, Yaohong Xiang, Yun Huang

Social Life Simulation for Non-Cognitive Skills Learning

Read the original on arXiv Computation and Language →

The paper introduces Simulife++, an interactive platform powered by a large language model that lets users create stories with AI characters in social scenarios. It expands human-AI interaction to include a Sage Agent, a bystander AI that offers guidance and perspectives to promote reflection on non‑cognitive skills. A within‑subject study shows that the Sage Agent significantly boosts users’ reflection on motivation, self‑perceptions, resilience, and narrative transportation, while also enhancing social communication and decision‑making.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv AI
Oct 9

MIMESIS: Learning User Simulators as Training Environments for Interactive Agents

MIMESIS is a user simulator designed to train interactive language agents by mimicking real user behavior. It is trained on human conversations with explicit reasoning supervision and incorporates 13 realistic behavioral patterns. Experiments show that agents trained with MIMESIS outperform those trained with GPT-5.5 across multiple unseen simulators, and the proposed Coached On-Policy Self-Distillation further improves agent performance by providing dense, token-level supervision.

By Hoang Phan, Dat Huynh, Andrey Zhmoginov, Qi Zeng, Wancen Mu, Yue Cao, Shengjie Bi, Yun He, Changdae Oh, Deren Lei
llmsagentsreinforcement-learningefficiency
More like this →