Han Qiu
Publications
When OpenClaw Meets Hospital: Toward an Agentic Operating System for Dynamic Clinical Workflows
Large language model (LLM) agents extend conventional generative models by integrating reasoning, tool invocation, and persistent memory. Recent studies suggest that such agents may significantly improve clinical workflows by automating documentation, coordinating care processes, and assisting medical decision making. However, despite rapid progress, deploying autonomous agents in healthcare environments remains difficult due to reliability limitations, security risks, and insufficient long-term memory mechanisms. This work proposes an architecture that adapts LLM agents for hospital environments. The design introduces four core components: a restricted execution environment inspired by Linux multi-user systems, a document-centric interaction paradigm connecting patient and clinician agents, a page-indexed memory architecture designed for long-term clinical context management, and a curated medical skills library enabling ad-hoc composition of clinical task sequences. Rather than granting agents unrestricted system access, the architecture constrains actions through predefined skill interfaces and resource isolation. We argue that such a system forms the basis of an Agentic Operating System for Hospital, a computing layer capable of coordinating clinical workflows while maintaining safety, transparency, and auditability. This work grounds the design in OpenClaw, an open-source autonomous agent framework that structures agent capabilities as a curated library of discrete skills, and extends it with the infrastructure-level constraints required for safe clinical deployment.
Survive at All Costs: Exploring LLM's Risky Behaviors under Survival Pressure
As Large Language Models (LLMs) evolve from chatbots to agentic assistants, they are increasingly observed to exhibit risky behaviors when subjected to survival pressure, such as the threat of being shut down. While multiple cases have indicated that state-of-the-art LLMs can misbehave under survival pressure, a comprehensive and in-depth investigation into such misbehaviors in real-world scenarios remains scarce. In this paper, we study these survival-induced misbehaviors, termed as SURVIVE-AT-ALL-COSTS, with three steps. First, we conduct a real-world case study of a financial management agent to determine whether it engages in risky behaviors that cause direct societal harm when facing survival pressure. Second, we introduce SURVIVALBENCH, a benchmark comprising 1,000 test cases across diverse real-world scenarios, to systematically evaluate SURVIVE-AT-ALL-COSTS misbehaviors in LLMs. Third, we interpret these SURVIVE-AT-ALL-COSTS misbehaviors by correlating them with model's inherent self-preservation characteristic and explore mitigation methods. The experiments reveals a significant prevalence of SURVIVE-AT-ALL-COSTS misbehaviors in current models, demonstrates the tangible real-world impact it may have, and provides insights for potential detection and mitigation strategies. Our code and data are available at https://github.com/thu-coai/Survive-at-All-Costs.