activity
20242026
most citedSimulating Human Strategic Behavior: Comparing Single and Multi-agent LLMs

11 citations · 18 across the 23 of their papers we have counts for

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Showing 2026Show all

10 papers · 1 filter

cs.CL2026

Ask Less, Ask Better: Allocating User Attention in Proactive LLM Agents with Interruption Budgets

Xuanming Zhang, Amir H. Rezaeian, Miguel Ballesteros +6

LLM agents can ask clarifying questions to complete tasks, but answering each question demands users' time and effort. Existing evaluations typically measure this burden by countin…

cs.HC2026

SPEAR: Five Principles for Interactive Human-Agent Alignment

Tao Long, Lydia B. Chilton

Recent AI alignment work often frames alignment as a pre-deployment optimization problem: collect human feedback, learn preferences or principles, finetune the model, and deploy an…

cs.SE2026

FlowCheck: Helping End-Users Specify and Verify Intent in Vibe-Coded Web Apps

Reya Vir, Lydia Chilton, Zhuo Zhang +1

Vibe-coded applications often contain silent behavioral failures in which the interface appears functional even though user-visible information does not flow to the expected state…

cs.HC2026

fog: Expressing Motion and Emotion through Function Composition of AI-Generated Code

Vivian Liu, Lydia Chilton

Motion and emotion are core parts of intelligent, expressive behavior. In this paper, we introduce fog, a function composition framework for implementing and compose motion functio…

cs.HC2026

PERSONAJUDGE: Simulating Individual Human Preference Judgments with Evaluator-Specific Demonstration Data

Zeyu He, Xuan Qi, Subramanian Chidambaram +4

Large language models increasingly serve as judges in AI evaluation, but current approaches rely on consensus preferences that ignore individual evaluator variation. We propose a n…

cs.HC2026

Designing for the Moment: How One-Minute Interventions Fit or Falter Across Domains

Zahra Hassanzadeh, Anne Hsu, Rachel Kornfield +8

This paper explores the design space for one-minute digital interventions that prompt immediate action without onboarding or sensing. By embracing Fogg's Behavior Model and four de…

cs.HC2026

Conversational Customization of Productivity Systems: A Design Probe of Malleable AI Interfaces

Karthik Sreedhar, Aryan Kaul, Lydia B. Chilton

Customization has long been a central goal in interactive systems, yet prior work shows that end-user tailoring occurs infrequently and is often confined to initial setup or moment…

cs.HC2026

ZORO: Active Controls to Support Rule Following in Vibe Coding

Jenny Ma, Sitong Wang, Joshua H. Kung +1

Rules files (e.g., AGENTSmd, CLAUDEmd) are the primary mechanism for human-agent alignment when developers vibe code. However, they remain passive: it is not immediately appa…

cs.CL2026

Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data

Xuanming Zhang, Shwan Ashrafi, Aziza Mirsaidova +5

We study the reasoning behavior of large language models (LLMs) under limited computation budgets. In such settings, producing useful partial solutions quickly is often more practi…

cs.HC2026

Rewriting Video: Text-Driven Reauthoring of Video Footage

Sitong Wang, Anh Truong, Lydia B. Chilton +1

Video is a powerful medium for communication and storytelling, yet reauthoring existing footage remains challenging. Even simple edits often demand expertise, time, and careful pla…