Zoe De Simone
Publications
Alignment has a Fantasia Problem
In accomplishing complex tasks, human cognition typically progresses from abstract to concrete (e.g., from brainstorming ideas to writing an essay). With the advent of highly capable AI assistants, people now offload various parts of their task to the system. However, instruction-tuned AI systems, even when designed to infer implicit intent, lack an understanding of humans' cognitive processes. When a user approaches AI while their goals and intentions are abstract, AI systems often short circuit their cognitive process through which those goals would be refined by jumping toward a final output (e.g., writing the essay entirely). Doing so takes away the user's agency in achieving the task: they may need to spend more time revising or, worse, settle on a suboptimal outcome. We call these failures Fantasia interactions after the famous Disney scene. We argue that Fantasia interactions demand a rethinking of alignment research, where AI systems optimize how cognitive responsibility is allocated within an interaction. We highlight gaps in state-of-the-art alignment methods, and outline a research agenda for training and evaluating models to achieve this vision.
Creo: From One-Shot Image Generation to Progressive, Co-Creative Ideation
Text-to-image (T2I) systems enable rapid generation of high-fidelity imagery but are misaligned with how visual ideas develop. T2I systems generate outputs that make implicit visual decisions on behalf of the user, often introduce fine-grained details that can anchor users prematurely and limit their ability to keep options open early on, and cause unintended changes during editing that are difficult to correct and reduce users' sense of control. To address these concerns, we present Creo, a multi-stage T2I system that scaffolds image generation by progressing from rough sketches to high-resolution outputs, exposing intermediary abstractions where users can make incremental changes. Sketch-like abstractions invite user editing and allow users to keep design options open when ideas are still forming due to their provisional nature. Each stage in Creo can be modified with manual changes and AI-assisted operations, enabling fine-grained, step-wise control through a locking mechanism that preserves prior decisions so subsequent edits affect only specified regions or attributes. Users remain in the loop, making and verifying decisions across stages, while the system applies diffs instead of regenerating full images, reducing drift as fidelity increases. A comparative study with a one-shot baseline shows that participants felt stronger ownership over Creo outputs, as they were able to trace their decisions in building up the image. Furthermore, embedding-based analysis indicates that Creo outputs are less homogeneous than one-shot results. These findings suggest that multi-stage generation, combined with intermediate control and decision locking, is a key design principle for improving controllability, user agency, creativity, and output diversity in generative systems.