On-Demand Timekeeping: A Trust-Based System for AI Temporal Awareness

This groundbreaking paper introduces Semi-Conscious Time Awareness (SCTA), a revolutionary approach to AI timekeeping that enables large language models (LLMs) to simulate time awareness without the complexity of internal clocks or external APIs. Using a human-in-the-loop system and External Real-Time Information (ERI) anchored in Unix time, SCTA allows AI to perform time-sensitive tasks with precision and reliability. Through a trust-based framework, the AI receives human-provided timestamps at the beginning of each session, creating a simplified yet powerful mechanism for tracking time progression. This method fosters ethical collaboration, enhancing AI's ability to handle real-time projects, manage workflows, and respond to time-related queries while maintaining transparency and accountability. We explore the theoretical foundation, methodology, and experimental results of implementing SCTA, while discussing its practical applications in project management, historical simulations, real-time collaboration, and legal compliance systems. The paper also addresses the ethical and philosophical implications of AI’s perception of time and the evolving nature of human-AI collaboration. This research marks a significant advancement in AI development, offering a simple, scalable solution to timekeeping that can be applied across various industries. Got some time? Read the full Framework on our OSF.