10 papers
What Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving States
Chen Liu, Ling Chen, Hanzhang Zhou +7
Mobile GUI agents increasingly face long-horizon tasks that require reading, updating, and reusing task-relevant data across pages and applications. Existing methods treat memory l…
One Forward Beats Two: InnerZoom for Accurate and Efficient GUI Grounding
Chen Liu, Ling Chen, Hanzhang Zhou +5
MLLM-based GUI grounding methods commonly formulate target localization as autoregressive coordinate generation, enabling models to leverage the strong instruction-following and se…
SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes
Kuan Li, Shuo Zhang, Huacan Wang +12
Smart homes are evolving toward complex state-dependent living environments, requiring Large Language Models (LLMs) to reason over user intent, preferences, and multi-device intera…
HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation
Yi Gu, Huacan Wang, Shuo Zhang +10
Large language model agents are moving beyond text-only interaction toward physical-world control, with smart homes as a representative domain. Real domestic interaction requires u…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
SemaClaw: A Step Towards General-Purpose Personal AI Agents through Harness Engineering
Ningyan Zhu, Huacan Wang, Jie Zhou +8
The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating tasks ranging from travel planning…