activity
20232026
most citedCoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming

26 citations · 41 across the 17 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.CL2026

You Really Didn't Get That? Benchmarking Social Pragmatic Inference for Indirect and Playful Chinese Online Comments

Shiwei Hong, Junjie Ma, Emma Jiren Wang +5

Chinese online comments often convey social meaning through indirect and playful language that is hard to interpret without context. Existing evaluations largely organize items aro…

cs.HC2026

Balancing Safety and Autonomy: Accessibility-Oriented Interventions in Generative AI for Cognitive Impairment

Yibo Meng, Jingruo Chen, Lyumanshan Ye +2

Generative AI systems are increasingly used by older adults with cognitive impairment for everyday tasks such as information seeking, health management, and communication. While th…

cs.HC2026

Mixed-Initiative Context: Structuring and Managing Context for Human-AI Collaboration

Haichang Li, Qinshi Zhang, Piaohong Wang +1

In the human-AI collaboration area, the context formed naturally through multi-turn interactions is typically flattened into a chronological sequence and treated as a fixed whole i…

cs.HC2026★ 1 cited

InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews

Yi Wen, Yu Zhang, Sriram Suresh +3

Semi-structured interviews are a common method in qualitative research. However, conducting high-quality interviews is cognitively demanding and requires strong interviewing skills…

cs.HC2026

The Configuration of Space: Probing the Way Social Interaction and Perception are Affected by Task-Specific Spatial Representations in Online Video Communication

Yihuan Chen, Kexue Fu, Qianyi Chen +2

Humans live and act in 3D space, but often work and communicate on 2D surfaces. The prevalence of online communication on 2D screens raises the issue of whether human spatial confi…

cs.AI2026

ProAct: Agentic Lookahead in Interactive Environments

Yangbin Yu, Mingyu Yang, Junyou Li +9

Existing Large Language Model (LLM) agents struggle in interactive environments requiring long-horizon planning, primarily due to compounding errors when simulating future states.…