What question did this study set out to answer?

The aim is to address the limitations of current AI systems in handling long-term tasks by preserving human judgment.

June 17, 2026Open Access

A Study on Universal AI Environment Design for Long-Term Tasks and Projects with Preservation of Human Judgment Authority – Centered on Time Reference, Resource Saturation Visualization, and Reversible Forgetting Mechanism –

Key Points

The aim is to address the limitations of current AI systems in handling long-term tasks by preserving human judgment.
Analyzed existing conversational AI systems and their structural limitations for long-term tasks.
Proposed an AI architecture incorporating time references, resource visualization, and reversible forgetting mechanisms.
Ensured the framework's compatibility with various AI models for gradual integration.
The proposed architecture facilitates better resource efficiency and task continuity.
Human judgment authority is maintained, improving user trust in long-term AI-driven projects.
The system can adapt to existing frameworks, enhancing its practical applicability.

Abstract

This paper analyzes the structural limitations of existing conversational AI systems in long-term tasks and complex project environments, and proposes a universal AI architecture that preserves human judgment authority while achieving both resource efficiency and contextual continuity. The proposed framework incorporates explicit time references, task unit separation, resource saturation visualization, a reversible forgetting mechanism based on dummy data, and a session-project transition structure as core elements. The architecture is not dependent on any specific AI model or platform and can be partially and progressively integrated into existing systems. This study argues that AI environments should support and preserve human judgment rather than replace it, especially in long-term work and project contexts.

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