collaborators

7 papers

cs.CL2026

Muse Glimmer 30B: An On-Device Agentic Model

Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327

We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…

cs.CL2026

Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering

Yifan Wang, Xinkui Lin, Yongxiu Xu +9

Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely adopted for long-term conversation…

cs.AI2026

GSAR: Goal-State-Anchor Rewards for Mobile GUI Agents with Self-Evolving Data Synthesis

Long Zhang, Yuhan Chen, Chaoran Zhang +7

Vision-Language Models (VLMs) based GUI agents stand to benefit significantly from online reinforcement learning (RL). However, their training is bottlenecked by two fundamental is…

cs.AI2026

AppDeltaWorld: Transition-Grounded Delta Code World Model for Mobile GUI Agents

Weikai Xu, Yunren Feng, Haoxiang Lei +6

Mobile GUI agents can operate apps through pixel perception and touch actions, making them a promising interface for collecting and improving long-horizon mobile interaction polici…

cs.AI2026

How Mobile World Model Guides GUI Agents?

Weikai Xu, Kun Huang, Yunren Feng +10

Recent advances in vision-language models have enabled mobile GUI agents to perceive visual interfaces and execute user instructions, but reliable prediction of action consequences…

cs.CL2026

CoME: Empowering Channel-of-Mobile-Experts with Informative Hybrid-Capabilities Reasoning

Yuxuan Liu, Weikai Xu, Kun Huang +9

Mobile Agents can autonomously execute user instructions, which requires hybrid-capabilities reasoning, including screen summary, subtask planning, action decision and action funct…

cs.CL2025

MobileIPL: Enhancing Mobile Agents Thinking Process via Iterative Preference Learning

Kun Huang, Weikai Xu, Yuxuan Liu +6

The Chain of Action-Planning Thoughts (CoaT) paradigm has been shown to improve the reasoning performance of VLM-based mobile agents in GUI tasks. However, the scarcity of diverse…