Advancements in Large language models (LLMs) have enabled a variety of downstream applications like story and interview script generation.However, recent research raised concerns about culture-related fairness issues in LLM-generated content.In this work, we identify and systematically investigate LLMs' insider-outsider bias, a phenomenon where models position themselves as "insiders" of mainstream cultures during generation while externalizing less dominant cultures.We propose the INSIDEOUT benchmark with 4,000 generation prompts and three evaluation metrics to quantify this bias through a culturally situated interview script generation task, in which an LLM is positioned as a reporter interviewing local people across 10 diverse cultures.Empirical evaluation on 5 state-of-the-art LLMs reveals that while models adopt insider tones in over 88% US-contexted scripts on average, they disproportionately default to "outsider" stances for non-Western cultures.To mitigate these biases, we propose 2 inference-time methods: a baseline prompt-based Fairness Intervention Pillars (FIP) method, and a structured Mitigation via Fairness Agents (MFA) framework consisting of a Single-Agent (MFA-SA), a Hierarchical-Agent (MFA-HA), and an autonomous Agentic Planning (MFA-Plan) pipeline.Empirical results demonstrate that agent-based MFA methods achieve outstanding and robust performance in mitigating the insider-outsider bias: For instance, on the Cultural Alignment Gap (CAG) metric, MFA-SA reduces bias in Llama model by 89.70 % and MFA-HA mitigates bias in Qwen by 82.54%.These findings showcase the effectiveness of agent-based methods as a promising direction for mitigating biases in generative LLMs. * Equal contribution.

Dynamic reconfiguration support in application servers is a solution to meet the demands for flexible and adaptive component-based applications. However, when an application is reconfigured, its fault-tolerant mechanism should be reconfigured either. This is one of the crucial problems we have to solve before a fault-tolerant application is dynamically reconfigured at runtime. This paper proposes a fault-tolerant sandbox to support the reconfigurable fault-tolerant mechanisms on application servers. We present how the sandbox integrates multiple error detection and recovery mechanisms, and how to reconfigure these mechanisms at runtime, especially for coordinated recovery mechanisms. We implement a prototype and perform a set of controlled experiments to demonstrate the sandbox’s capabilities.