23 citations · 27 across the 15 of their papers we have counts for
17 papers
Learning to Clarify Underspecified Intents Under Limited Interaction
Pranav M R, Manuel Cherep, Pattie Maes +1
AI assistants receive requests that leave out information needed for a good outcome, for example about users' preferences or goals. They must then either speculate or ask for more…
Probing Perceptual Priors of MLLMs via Gibbs Sampling with Interpretable Generative Controls
Manuel Cherep, Pattie Maes, Nikhil Singh
A model's behavior on a task is jointly determined by the input it receives and the prior it brings in, i.e. the distribution over stimuli it implicitly expects. Interpretability r…
Before You Say It: Anticipating Verbal Behavior from Longitudinal Everyday Conversations with LLMs
Yasith Samaradivakara, Valdemar Danry, Paul Liang +1
Knowing someone deeply means not just understanding what they say or do but also how they will likely think, react, and engage across situations. Such predictions could eventually…
Position: Behavioral Systems Require Behavioral Tests
Manuel Cherep, Nikhil Singh, Pattie Maes
Artificial agentic systems increasingly operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time. Yet, current evaluation meth…
Feeling the Facts: Real-time Wearable Fact-checkers Can Use Nudges to Reduce User Belief in False Information
Chitralekha Gupta, Nadia Victoria Aritonang, Dixon Prem Daniel Rajendran +3
Misinformation can spread rapidly in everyday conversation, where pausing to verify is not always possible. We envision a wearable system that bridges the timing gap between hearin…
Visual Persuasion: What Influences Decisions of Vision-Language Models?
Manuel Cherep, Pranav M R, Pattie Maes +1
The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs). These agents make visual decisio…
A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments
Manuel Cherep, Chengtian Ma, Abigail Xu +3
Environments built for people are increasingly operated by a new class of economic actors: LLM-powered software agents making decisions on our behalf. These decisions range from ou…
EmoSign: A Multimodal Dataset for Understanding Emotions in American Sign Language
Phoebe Chua, Cathy Mengying Fang, Takehiko Ohkawa +3
Unlike spoken languages where the use of prosodic features to convey emotion is well studied, indicators of emotion in sign language remain poorly understood, creating communicatio…
LLM Agents Are Hypersensitive to Nudges
Manuel Cherep, Pattie Maes, Nikhil Singh
LLMs are being set loose in complex, real-world environments involving sequential decision-making and tool use. Often, this involves making choices on behalf of human users. Howeve…
Perspectives on Capturing Emotional Expressiveness in Sign Language
Phoebe Chua, Cathy Mengying Fang, Yasith Samaradivakara +2
Significant advances have been made in our ability to understand and generate emotionally expressive content such as text and speech, yet comparable progress in sign language techn…
Discovering and Steering Interpretable Concepts in Large Generative Music Models
Nikhil Singh, Manuel Cherep, Pattie Maes
The fidelity with which neural networks can now generate content such as music presents a scientific opportunity: these systems appear to have learned implicit theories of such con…
The Goldilocks Time Window for Proactive Interventions in Wearable AI Systems
Cathy Mengying Fang, Wazeer Zulfikar, Yasith Samaradivakara +2
As AI systems become increasingly integrated into our daily lives and into wearable form factors, there's a fundamental tension between their potential to proactively assist us and…
Can dialogues with AI systems help humans better discern visual misinformation?
Anku Rani, Valdemar Danry, Andy Lippman +1
The widespread emergence of manipulated news media content poses significant challenges to online information integrity. This study investigates whether dialogues with AI about AI-…
Mirai: A Wearable Proactive AI "Inner-Voice" for Contextual Nudging
Cathy Mengying Fang, Yasith Samaradivakara, Pattie Maes +1
People often find it difficult to turn their intentions into real actions -- a challenge that affects both personal growth and mental well-being. While established methods like cog…
Leveraging AI-Generated Emotional Self-Voice to Nudge People towards their Ideal Selves
Cathy Mengying Fang, Phoebe Chua, Samantha Chan +3
Emotions, shaped by past experiences, significantly influence decision-making and goal pursuit. Traditional cognitive-behavioral techniques for personal development rely on mental…
Purrfect Pitch: Exploring Musical Interval Learning through Multisensory Interfaces
Sam Chin, Cathy Mengying Fang, Nikhil Singh +3
We introduce Purrfect Pitch, a system consisting of a wearable haptic device and a custom-designed learning interface for musical ear training. We focus on the ability to identify…
PhysioLLM: Supporting Personalized Health Insights with Wearables and Large Language Models
Cathy Mengying Fang, Valdemar Danry, Nathan Whitmore +4
We present PhysioLLM, an interactive system that leverages large language models (LLMs) to provide personalized health understanding and exploration by integrating physiological da…