arXiv subjects

Alexander J. Fiannaca

Publications and source records attributed to Alexander J. Fiannaca.

4 recordsLinked to original sources

arXiv2026

Compass vs Railway Tracks: Unpacking User Mental Models for Communicating Long-Horizon Work to Humans vs. AI

As AI systems grow increasingly capable of operating for hours or days at a time, users' prompts are transforming into elaborate specifications for the AI to autonomously work on. While prompting for bounded, single-turn tasks has been extensively studied, less is known about how people communicate specifications for long-horizon tasks. We conducted a qualitative study in which 16 professionals drafted specifications for both a human colleague and an AI, revealing a core divergence: participants treated human delegation as a "compass", offering high-level intent to encourage flexible exploration. In contrast, communication with AI resembled painstakingly laying down "railway tracks": rigid, exhaustive instructions to minimize ambiguity and deviation. This reflected a perception that current AI struggles to infer intent, prioritize, and make judgments on its own. When envisioning an ideal AI collaborator, users desired a hybrid: a collaborator blending AI's efficiency and large context window with the critical thinking and agency of a human colleague. We discuss design implications for future AI systems, proposing that they align on outcomes through generated rough drafts, verify feasibility via end-to-end "test runs," and monitor execution through intelligent check-ins -- ultimately transforming AI from a passive instruction-follower into a reliable collaborator for ambiguous, long-horizon tasks.

cs.HC↗

arXiv2025

Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning

Multimodal large language models (MLLMs), with their expansive world knowledge and reasoning capabilities, present a unique opportunity for end-users to create personalized AI sensors capable of reasoning about complex situations. A user could describe a desired sensing task in natural language (e.g., "alert if my toddler is getting into mischief"), with the MLLM analyzing the camera feed and responding within seconds. In a formative study, we found that users saw substantial value in defining their own sensors, yet struggled to articulate their unique personal requirements and debug the sensors through prompting alone. To address these challenges, we developed Gensors, a system that empowers users to define customized sensors supported by the reasoning capabilities of MLLMs. Gensors 1) assists users in eliciting requirements through both automatically-generated and manually created sensor criteria, 2) facilitates debugging by allowing users to isolate and test individual criteria in parallel, 3) suggests additional criteria based on user-provided images, and 4) proposes test cases to help users "stress test" sensors on potentially unforeseen scenarios. In a user study, participants reported significantly greater sense of control, understanding, and ease of communication when defining sensors using Gensors. Beyond addressing model limitations, Gensors supported users in debugging, eliciting requirements, and expressing unique personal requirements to the sensor through criteria-based reasoning; it also helped uncover users' "blind spots" by exposing overlooked criteria and revealing unanticipated failure modes. Finally, we discuss how unique characteristics of MLLMs--such as hallucinations and inconsistent responses--can impact the sensor-creation process. These findings contribute to the design of future intelligent sensing systems that are intuitive and customizable by everyday users.

cs.HC↗

arXiv2024

In Situ AI Prototyping: Infusing Multimodal Prompts into Mobile Settings with MobileMaker

Recent advances in multimodal large language models (LLMs) have made it easier to rapidly prototype AI-powered features, especially for mobile use cases. However, gathering early, mobile-situated user feedback on these AI prototypes remains challenging. The broad scope and flexibility of LLMs means that, for a given use-case-specific prototype, there is a crucial need to understand the wide range of in-the-wild input users are likely to provide and their in-context expectations for the AI's behavior. To explore the concept of in situ AI prototyping and testing, we created MobileMaker: a platform that enables designers to rapidly create and test mobile AI prototypes directly on devices. This tool also enables testers to make on-device, in-the-field revisions of prototypes using natural language. In an exploratory study with 16 participants, we explored how user feedback on prototypes created with MobileMaker compares to that of existing prototyping tools (e.g., Figma, prompt editors). Our findings suggest that MobileMaker prototypes enabled more serendipitous discovery of: model input edge cases, discrepancies between AI's and user's in-context interpretation of the task, and contextual signals missed by the AI. Furthermore, we learned that while the ability to make in-the-wild revisions led users to feel more fulfilled as active participants in the design process, it might also constrain their feedback to the subset of changes perceived as more actionable or implementable by the prototyping tool.

cs.HC↗

arXiv2024

"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output

Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. In this work, we surveyed 51 experienced industry professionals to understand the range of scenarios and motivations driving the need for output constraints from a user-centered perspective. We identified 134 concrete use cases for constraints at two levels: low-level, which ensures the output adhere to a structured format and an appropriate length, and high-level, which requires the output to follow semantic and stylistic guidelines without hallucination. Critically, applying output constraints could not only streamline the currently repetitive process of developing, testing, and integrating LLM prompts for developers, but also enhance the user experience of LLM-powered features and applications. We conclude with a discussion on user preferences and needs towards articulating intended constraints for LLMs, alongside an initial design for a constraint prototyping tool.

cs.HC↗