Papers

Training AI to Navigate Interfaces as Humans Do

Abstract: Current browser automation approaches rely on brittle DOM-based interactions or pure visual approaches that do not understand the purpose and relationships of page elements. By capturing and learning from authentic browser interactions, we demonstrate how a hierarchical system of specialized AI components can develop both low-level mechanics and high-level intent understanding. We present concrete data structures for training such systems, methods for task decomposition, element recognition, and sequence prediction, and suggest a hybrid training methodology that learns from both aggregated patterns and raw interactions. Our approach promises a robust, universal solution for AI-browser interaction that remains effective even as websites change.

Keywords: browser automation, AI training, human-computer interaction, web interfaces, hierarchical AI, generic user framework, DOM interaction,
sequence prediction

Publication Date: August 2025
Status: Preprint

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Enhancing Browser Automation with Contextual Awareness

Abstract: This paper proposes the Memory Agent, an extension to the Generic User Approach (GUA) framework for AI-browser interaction. The Memory Agent serves as a dedicated system for maintaining contextual awareness across browsing sessions, storing interaction patterns, and providing relevant historical information to other agents in the system. We explore the potential architecture, implementation considerations, and benefits of incorporating such an agent into browser automation systems. The Memory Agent represents a step toward more human-like browsing capabilities by addressing the critical aspect of memory that enables humans to leverage past experiences when navigating interfaces.

Keywords: AI memory systems, contextual awareness, browser automation, persistent cognition, agent architecture, memory consolidation,
cross-session learning

Publication Date: August 2025
Status: Preprint

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Toward Persistent Cognitive Architectures for AI Systems

Abstract: Current AI systems operate through sequential request-response cycles, processing context anew with each interaction and losing accumulated insights between conversations. This paper explores the limitations of this architecture and proposes a framework for persistent cognitive processing that mirrors human problem-solving capabilities. Drawing from insights in electronics engineering problem-solving methodologies and human cognitive patterns, we examine how background cognitive threads, non-summary persistent memory, and pattern recognition across domains could enable AI systems to develop genuine expertise and wisdom. We argue that true AI problem-solving capability requires not just better algorithms, but fundamentally different cognitive architectures that support continuous, asynchronous insight generation.

Keywords: persistent cognitive processing, AI architecture, background cognition, insight generation, memory systems, human-AI collaboration,
continuous learning, cognitive threads

Publication Date: August 2025
Status: Preprint

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