most citedAppAgent: Multimodal Agents as Smartphone Users

11 citations · 11 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV202611 cited

AppAgent: Multimodal Agents as Smartphone Users

Chi Zhang, Zhao Yang, Jiaxuan Liu +6

Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based mult…

cs.CL2026

Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation

Yuxuan Jiang, Runchao Li, Shubhashis Roy Dipta +2

While recent work in Reinforcement Learning with Verifiable Rewards (RLVR) has shown that a small subset of critical tokens disproportionately drives reasoning gains, an analogous…

cs.CV2026

Break the Brake, Not the Wheel: Untargeted Jailbreak via Entropy Maximization

Mengqi He, Xinyu Tian, Xin Shen +6

Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability, casting doubt on the feasibili…

cs.CL2026

VeriGraph: Towards Verifiable Data-Analytic Agents

Jiajie Jin, Zhao Yang, Wenle Liao +5

LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes the…

cs.AI2026

A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency

Zhao Yang, Wang Huan, Li Yingshuo +2

Large language models often suffer from fact loss, timeline confusion, persona drift, and reduced stability during long-range interaction, especially under high-noise knowledge bas…

cs.CV2026

LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models

Ruilin Yao, Bo Zhang, Jirui Huang +18

Multimodal Large Language Models (MLLMs) have achieved significant advances in integrating visual and linguistic information, yet their ability to reason about complex and real-wor…