Recent advancements in Large Language Models (LLMs) have shown remarkable capabilities in reasoning, prompting a surge in research aimed at developing trustworthy LLMs. The emergence of LLM-based agents has garnered considerable attention, yet their trustworthiness remains an under-explored area. As agents can directly interact with the physical environment in everyday human activities, their reliability and safety is critical. This paper presents an Agent-Constitution-based agent framework, TrustAgent, an initial investigation into improving the safety dimension of trustworthiness in LLM-based agents. This framework consists of threefold strategies – pre-planning strategy which injects safety knowledge to the model prior to plan generation, in-planning strategy which bolsters safety during plan generation, and post-planning strategy which ensures safety by post-planning inspection. Through experimental analysis, we demonstrate how these approaches can effectively elevate an LLM agent’s safety by identifying and preventing potential challenges. Furthermore, we explore the intricate relationships between safety and helpfulness, and model’s reasoning ability and its efficacy as a safe agent. We argue that a robust reasoning ability is a fundamental prerequisite for an LLM to function safely as an agent. This paper underscores the imperative of integrating safety awareness and trustworthiness into the design and deployment of LLM-based agents, not only to enhance their performance but also to ensure their responsible integration into human-centric environments. Data and code are publicly available at \url{https://github.com/agiresearch/TrustAgent}.

Current approaches of knowledge editing struggle to effectively propagate updates to interconnected facts. In this work, we delve into the barriers that hinder the appropriate propagation of updated knowledge within these models for accurate reasoning. To support our analysis, we introduce a novel reasoning-based benchmark – ReCoE (Reasoning-based Counterfactual Editing dataset) – which covers six common reasoning schemes in real world. We conduct a thorough analysis of existing knowledge editing techniques, including input augmentation, finetuning, and locate-and-edit. We found that all model editing methods show notably low performance on this dataset, especially in certain reasoning schemes. Our analysis over the chain-of-thought generation of edited models further uncover key reasons behind the inadequacy of existing knowledge editing methods from a reasoning standpoint, involving aspects on fact-wise editing, fact recall ability, and coherence in generation. We will make our benchmark publicly available.

This paper envisions a revolutionary AIOS-Agent ecosystem, where Large Language Model (LLM) serves as the (Artificial) Intelligent Operating System (IOS, or AIOS)–an operating system “with soul”. Upon this foundation, a diverse range of LLM-based AI Agent Applications (Agents, or AAPs) are developed, enriching the AIOS-Agent ecosystem and signaling a paradigm shift from the traditional OS-APP ecosystem. We envision that LLMs impact will not be limited to the AI application level, instead, it will in turn revolutionize the design and implementation of computer system, architecture, software, and programming language, featured by several main concepts. LLM as OS (system-level), Agents as Applications (application-level), Natural Language as Programming Interface (user-level), and Tools as Devices/Libraries (hardware/middleware-level). In this paper, we begin by introducing the architecture and historical evolution of traditional Operating Systems (OS). Then we formalize a conceptual framework for AIOS through “LLM as OS (LLMAO)”, drawing analogies between AIOS components and traditional OS elements. LLM is likened to OS kernel, context window to memory, external storage to file system, hardware tools to peripheral devices, software tools to programming libraries, and user prompts to user commands. Subsequently, we introduce the new AIOS-Agent Ecosystem, where users and developers can easily program Agent Applications (AAPs) using natural language, democratizing the development of and the access to computer software, which is different from the traditional OS-APP ecosystem, where desktop or mobile applications (APPs) have to be programmed by well-trained software developers using professional programming languages. Following this, we explore the diverse scope of Agent Applications. These agents can autonomously perform diverse tasks, showcasing intelligent task-solving ability in various scenarios. We delve into both single agent systems and multi-agent systems, as well as human-agent interaction. Lastly, we posit that the AIOS-Agent ecosystem can gain invaluable insights from the development trajectory of the traditional OS-APP ecosystem. Drawing on these insights, we propose a strategic roadmap for the evolution of the AIOS-Agent ecosystem. This roadmap is designed to guide the future research and development, suggesting systematic progresses of AIOS and its Agent applications.