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Towards Pragmatic Temporal Alignment in Stateful Generative AI Systems: A Configurable Approach

Year: 2024

Type: conference-paper

Abstract: Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or ``state'' across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a position framework for temporal alignment with configurable toggles. We present five alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world application scenarios. This paper discusses the potential benefits and challenges of each alignment method and positions the importance of a configurable system in advancing progress in stateful generative AI systems.

Source: Proceedings of the AAAI Symposium Series

DOI: 10.1609/aaaiss.v4i1.31821

Authors: Kaushik Roy, Yuxin Zi, Amit Sheth

Institution: University of South Carolina

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Foundation

Award: EAGER: Knowledge-guided neurosymbolic AI with guardrails for safe virtual health assistants