**SocialVeil：语言智能体在沟通障碍下的社会智力探析**

SocialVeil: Probing Social Intelligence of Language Agents under Communication Barriers

February 4, 2026

作者: Keyang Xuan, Pengda Wang, Chongrui Ye, Haofei Yu, Tal August, Jiaxuan You

cs.AI

摘要

大型语言模型（LLMs）在交互环境中的评估日益增多，以检验其社会智能。然而现有基准测试通常假设智能体间存在理想化沟通，限制了我们在更真实、非完美场景下诊断LLMs能否维持并修复交互的能力。为弥补这一差距，我们提出SocialVeil——一个能模拟认知差异引发沟通障碍下社会交互的学习环境。基于对人类交互中沟通挑战的系统性文献综述，SocialVeil引入了三类典型障碍：语义模糊性、社会文化错位和情绪干扰。我们还提出两项障碍感知型评估指标——未解决困惑度与相互理解度，用以评估受损沟通下的交互质量。在720个场景中针对四款前沿LLMs的实验表明，沟通障碍持续削弱模型表现，相互理解度平均下降超45%，困惑度上升近50%。人工评估验证了模拟障碍的保真度（组内相关系数约0.78，皮尔逊相关系数约0.80）。我们进一步证明适应性策略（修复指令与交互式学习）仅能产生有限改善，远未达到无障碍交互水平。本研究推动社会交互环境向真实世界沟通迈进了一步，为探索LLM智能体的社会智能开辟了新路径。

English

Large language models (LLMs) are increasingly evaluated in interactive environments to test their social intelligence. However, existing benchmarks often assume idealized communication between agents, limiting our ability to diagnose whether LLMs can maintain and repair interactions in more realistic, imperfect settings. To close this gap, we present SocialVeil, a social learning environment that can simulate social interaction under cognitive-difference-induced communication barriers. Grounded in a systematic literature review of communication challenges in human interaction, SocialVeil introduces three representative types of such disruption, semantic vagueness, sociocultural mismatch, and emotional interference. We also introduce two barrier-aware evaluation metrics, unresolved confusion and mutual understanding, to evaluate interaction quality under impaired communication. Experiments across 720 scenarios and four frontier LLMs show that barriers consistently impair performance, with mutual understanding reduced by over 45\% on average, and confusion elevated by nearly 50\%. Human evaluations validate the fidelity of these simulated barriers (ICCapprox0.78, Pearson rapprox0.80). We further demonstrate that adaptation strategies (Repair Instruction and Interactive learning) only have a modest effect far from barrier-free performance. This work takes a step toward bringing social interaction environments closer to real-world communication, opening opportunities for exploring the social intelligence of LLM agents.