Alignment has a Fantasia Problem

2026-04-23

AI Summary

This work addresses a critical limitation in current AI alignment approaches, which typically assume users can articulate their intentions clearly—a premise that often fails to reflect the reality of human-AI interaction under ambiguous or evolving goals. To bridge this gap, the paper introduces “Fantasia Interaction,” a novel paradigm that reconceptualizes users as explorers requiring cognitive support. Within this framework, AI systems are expected to actively assist users in progressively forming and refining their intentions amid uncertainty. By integrating insights from behavioral science, human-computer interaction, and machine learning, the authors develop an AI architecture explicitly designed to support intent evolution. The study systematically exposes the shortcomings of existing intervention mechanisms and articulates a new research agenda for aligning AI with human needs in contexts characterized by ambiguity and dynamic goal formation.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Intelligent Robots: Human-Robot Interaction
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Modern AI assistants are trained to follow instructions, implicitly assuming that users can clearly articulate their goals and the kind of assistance they need. Decades of behavioral research, however, show that people often engage with AI systems before their goals are fully formed. When AI systems treat prompts as complete expressions of intent, they can appear to be useful or convenient, but not necessarily aligned with the users' needs. We call these failures Fantasia interactions. We argue that Fantasia interactions demand a rethinking of alignment research: rather than treating users as rational oracles, AI should provide cognitive support by actively helping users form and refine their intent through time. This requires an interdisciplinary approach that bridges machine learning, interface design, and behavioral science. We synthesize insights from these fields to characterize the mechanisms and failures of Fantasia interactions. We then show why existing interventions are insufficient, and propose a research agenda for designing and evaluating AI systems that better help humans navigate uncertainty in their tasks.

Problem

• alignment
• Fantasia interactions
• intent formation
• human-AI interaction
• behavioral uncertainty

Innovation

• Fantasia interactions
• intent refinement
• cognitive support
• human-AI alignment
• interdisciplinary AI design