activity
20232026
most citedAppAgent: Multimodal Agents as Smartphone Users

11 citations · 11 across the 6 of their papers we have counts for

collaborators

6 papers

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.CV2025

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…

cs.CL2024

Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging

Deyuan Liu, Zhanyue Qin, Hairu Wang +12

While large language models (LLMs) excel in many domains, their complexity and scale challenge deployment in resource-limited environments. Current compression techniques, such as…

cs.CV2023★ 11 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…