AI 中文总结
研究大语言模型社交智能，提出Zijing框架，含测量基准SoMBench、训练方法Zing及推理架构Actio，通过实验证明社交智能大语言模型在评估、内化和落地方面需协同发展。
AI 中文摘要
随着大语言模型从孤立任务解决转向在人类环境中的长期服务，它们需要社交智能，即推断心理状态、跟踪社会关系、规范推理和在情境中调整行为的能力。本报告提出了Zijing，一个用于测量、内化和落地社交智能的集成框架。测量方面，引入SoMBench基准。内化方面，开发Zing训练方法。部署时落地方面，构建Actio推理架构。结果表明社交智能大语言模型需要在评估、参数内化和部署时落地方面协同推进。
英文摘要
As large language models move from isolated task solving toward long-term service in human environments, they require social intelligence: the ability to infer mental states, track social relations, reason over norms, and adapt behavior under context. This report presents ZenGen, an integrated framework for measuring, internalizing, and grounding social intelligence. For measurement, we introduce SoMBench, a psychology-grounded benchmark spanning 3 primary dimensions, 17 secondary dimensions, and 71 task paradigms. It controls question format, narrative perspective, and context length across 284 shared scenarios and 3,481 expert-verified instances. Evaluation of 20 representative LLMs reveals substantial headroom: the best model achieves only 72.08% overall accuracy, and none of the 17 secondary dimensions reaches the 90% near-ceiling band. For internalization, we develop ZenGen, a diagnosis-driven training recipe combining supervised fine-tuning, on-policy distillation, and rubric-based reinforcement learning. Across five social-cognition benchmarks, ZenGen consistently outperforms its base models, with ZenGen-27B-Stage2 achieving the best average score and ZenGen-32B-Stage2 remaining competitive with DeepSeek-V4-Pro. For deployment-time grounding, we build Actio, a harness-controlled inference architecture that routes four typed supports into reasoning: PRISM for procedural guidance, Starling for runtime mental-state representation, SAGE for reusable experience, and gated RAG for external social and normative knowledge. Across five base models and three benchmarks, the full harness improves 14 of 15 model-benchmark pairs and is best or tied for best in 8, demonstrating the effectiveness of typed runtime support. Together, these results show that socially intelligent LLMs require coordinated advances in evaluation, parametric internalization, and deployment-time grounding.
