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From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devices
The paper proposes a framework that uses large language models (LLMs) to support medical device safety work, which is document-based and costly due to the need for safety and domain experts. The framework connects…
No One to Blame: A Framework of Constitutive AI Unaccountability
Researchers have proposed a new framework called 'constitutive AI unaccountability' to address challenges in traditional accountability mechanisms with the increasing deployment of autonomous AI systems. This framework…
Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry
The paper discusses a reliability challenge caused by AI agents being created by non-engineering users within organizations, due to their dependency on various factors that can cause silent degradation over time. The…
Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry
The paper proposes a solution to ensure reliability in AI agents created by non-engineering users within organizations, through democratized low-code, no-code, and conversational development environments. The proposed…
The Autonomous Agency Scale: A Behavioral Framework for Measuring Self-Directed Behavior in AI Systems
The Autonomous Agency Scale (AAS) is a new framework proposed to measure the self-directed behavior of AI systems. The scale evaluates seven dimensions of agency across two temporal bands: active and ambient. It…
Human-AI Agent Interaction as a Neuroplastic Training Environment
The text discusses how interacting with AI agents can be seen as a form of neuroplastic training due to the frequent iterative loop in human-AI interaction. It suggests that these interactions may reinforce negative…
A toy framework for single and multi-agent human-AI curiosity ecosystems
The paper presents a basic framework to understand curiosity from the perspective of an ecosystem. It discusses how an individual agent's curiosity is influenced by factors like immediate uncertainty reduction, costs…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
In this research paper titled 'MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems', the authors propose a framework called Mesa to prioritize and secure communication channels in…
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
The authors present a framework for analyzing dialogue in collaborative problem-solving contexts, particularly focusing on human-AI and multi-agent collaboration. The framework addresses limitations in current…
The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
The paper presents the Spec Growth Engine, a framework to address the structural failure modes introduced by AI coding agents in software development. These failure modes include context explosion and silent spec-code…
The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
The paper proposes a new framework called the Spec Growth Engine to improve AI coding agents during software development. The framework addresses two main issues: context explosion and silent spec-code drift. It uses a…
VideoAgent: All-in-One Framework for Video Understanding and Editing
Researchers have developed a new framework called VideoAgent to automate video editing tasks. The system can handle diverse editing operations and understand long videos, addressing limitations in existing automated…
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