2 citations · 2 across the 8 of their papers we have counts for
12 papers
Can Agents Work for Everyone? Cross-User Reliability for Mobile GUI Agents in Personalized User Interfaces
Yeji Park, Jaeyun Shim, Taesik Gong
Mobile GUI agents increasingly operate on interfaces influenced by users' histories and preferences, but their reliability across different users remains underexplored. We introduc…
Adapting Vision-Language-Action Models to Unknown Visual Disruptions During Execution
Ahin Lee, Jinwoo Seo, Youngsoo Jang +1
Visual disruptions can arise while a robot is executing a task, leaving a vision-language-action (VLA) policy to respond without knowing the disruption type or timing. We introduce…
SharpDraft: Accelerating Long-Context Speculative Decoding with Cardinality-Aware Query Scaling
Jeonghoon Park, Seongwoon Jo, Jongwon Lee +1
Long-form reasoning makes inference expensive, and speculative decoding mitigates this cost by verifying multiple draft tokens in parallel. Its speedup, however, can fade as contex…
PastForward: Faster On-Device GUI Agents via Computational Experience Reuse
Taehwan Park, Changmin Lee, Hayeon Lee +1
Running GUI agents on edge devices can keep sensitive screens and interaction histories local, but the computational cost of inference at every action step makes deployment challen…
ACE: Adapter Consolidation across Experts for Parameter-Efficient Fine-Tuning of MoE LLMs
Ahin Lee, Sehyun Yun, Joonha Park +1
Parameter-efficient fine-tuning (PEFT) of mixture-of-experts (MoE) models commonly attaches a separate low-rank adapter to each expert. This expert-wise design fragments adaptation…
EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning
Ahin Lee, Sehyun Yun, Taesik Gong
Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing parameter-efficient fine-tuning (PE…
ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues
Yeji Park, Jiwon Tark, Taesik Gong
Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific…
From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG
Changmin Lee, Jaemin Kim, Taesik Gong
With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and responsiveness. To handle the…
Premover: Fast Vision-Language-Action Control via Early Execution During Instruction Delivery
Joonha Park, Jiseung Jeong, Taesik Gong
Vision-Language-Action (VLA) policies are typically evaluated under the assumption that the robot starts acting only after the user has finished typing or speaking. In real interac…
DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators
Taesik Gong, Fahim Kawsar, Chulhong Min
Tiny machine learning (TinyML) aims to run ML models on small devices and is increasingly favored for its enhanced privacy, reduced latency, and low cost. Recently, the advent of t…
An AI-Native Runtime for Multi-Wearable Environments
Chulhong Min, Utku Günay Acer, SiYoung Jang +5
The miniaturization of AI accelerators is paving the way for next-generation wearable applications within wearable technologies. We introduce Mojito, an AI-native runtime with adva…
Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables
Taesik Gong, Si Young Jang, Utku Günay Acer +2
The advent of tiny artificial intelligence (AI) accelerators enables AI to run at the extreme edge, offering reduced latency, lower power cost, and improved privacy. When integrate…