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20212026
most citedQuery Rewriting for Retrieval-Augmented Large Language Models

14 citations · 33 across the 24 of their papers we have counts for

collaborators

27 papers

cs.AI2026

PaceReasoner: Label-Free Learning to Answer Briefly from Long Reasoning

Changguo Jia, Zhiyou Xiao, Xinbei Ma +5

Extended chain-of-thought reasoning improves problem-solving performance but increases inference cost and latency, motivating more accurate answers with shorter reasoning. We intro…

cs.CV2026

UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City

Tianjie Ju, Zheng Wu, Yueqing Sun +15

Multimodal large language models (MLLMs) can interpret a street view, but reliable urban action depends on whether such local evidence remains useful after the agent starts to move…

cs.AI2026

Communication Policy Evolution for Proactive LLM Agents

Xinbei Ma, Jiyang Qiu, Yao Yao +10

LLM agents have rapidly evolved into autonomous systems, yet a persistent information gap remains between users and agents: communication is costly, while users' identical preferen…

cs.CL2026

Retrospective Progress-Aware Self-Refinement for LLM Agent Training

Xinbei Ma, Congmin Zheng, Jiyang Qiu +11

Long-horizon LLM-based agents receive rich environmental observations during interaction, yet outcome rewards provide limited explicit supervision about how individual actions adva…

cs.AI2026

Plan-MCTS: Plan Exploration for Action Exploitation in Web Navigation

Weiming Zhang, Jihong Wang, Jiamu Zhou +9

Large Language Models (LLMs) have empowered autonomous agents to handle complex web navigation tasks. While recent studies integrate tree search to enhance long-horizon reasoning,…

cs.LG2026

Adaptive Milestone Reward for GUI Agents

Congmin Zheng, Xiaoyun Mo, Xinbei Ma +10

Reinforcement Learning (RL) has emerged as a mainstream paradigm for training Mobile GUI Agents, yet it struggles with the temporal credit assignment problem inherent in long-horiz…

cs.CL2026

Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning

Zheng Wu, Xingyu Lou, Xinbei Ma +5

Large Language Model (LLM)-based agents significantly extend the utility of LLMs by interacting with dynamic environments. However, enabling agents to continually learn new tasks w…

cs.AI2025

Chain-of-Trigger: An Agentic Backdoor that Paradoxically Enhances Agentic Robustness

Jiyang Qiu, Xinbei Ma, Yunqing Xu +2

The rapid deployment of large language model (LLM)-based agents in real-world applications has raised serious concerns about their trustworthiness. In this work, we reveal the secu…

cs.AI2025

ParaCook: On Time-Efficient Planning for Multi-Agent Systems

Shiqi Zhang, Xinbei Ma, Yunqing Xu +7

Large Language Models (LLMs) exhibit strong reasoning abilities for planning long-horizon, real-world tasks, yet existing agent benchmarks focus on task completion while neglecting…

cs.AI2025

PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization

Zouying Cao, Runze Wang, Yifei Yang +4

Large Language Model (LLM) agents have demonstrated impressive capabilities in handling complex interactive problems. Existing LLM agents mainly generate natural language plans to…

cs.AI2025

Wide-Horizon Thinking and Simulation-Based Evaluation for Real-World LLM Planning with Multifaceted Constraints

Dongjie Yang, Chengqiang Lu, Qimeng Wang +4

Unlike reasoning, which often entails a deep sequence of deductive steps, complex real-world planning is characterized by the need to synthesize a broad spectrum of parallel and po…

cs.AI2025

EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attacks

Yijie Lu, Manman Zhao, Tianjie Ju +6

Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) are increasingly deployed yet vulnerable to Environmental Injection Attacks (EIAs).However…

cs.CL2025

How Deep is Love in LLMs' Hearts? Exploring Semantic Size in Human-like Cognition

Yao Yao, Yifei Yang, Xinbei Ma +4

How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes.…

cs.CL2025

Textual-to-Visual Iterative Self-Verification for Slide Generation

Yunqing Xu, Xinbei Ma, Jiyang Qiu +1

Generating presentation slides is a time-consuming task that urgently requires automation. Due to their limited flexibility and lack of automated refinement mechanisms, existing au…

cs.AI2025

Plan-over-Graph: Towards Parallelable LLM Agent Schedule

Shiqi Zhang, Xinbei Ma, Zouying Cao +2

Large Language Models (LLMs) have demonstrated exceptional abilities in reasoning for task planning. However, challenges remain under-explored for parallel schedules. This paper in…

cs.LG2025

LESA: Learnable LLM Layer Scaling-Up

Yifei Yang, Zouying Cao, Xinbei Ma +4

Training Large Language Models (LLMs) from scratch requires immense computational resources, making it prohibitively expensive. Model scaling-up offers a promising solution by leve…

cs.CL2024

MEGen: Generative Backdoor into Large Language Models via Model Editing

Jiyang Qiu, Xinbei Ma, Zhuosheng Zhang +3

Large language models (LLMs) have exhibited remarkable versatility and adaptability, while their widespread adoption across various applications also raises critical safety concern…

cs.CL2024★ 1 cited

Caution for the Environment: Multimodal LLM Agents are Susceptible to Environmental Distractions

Xinbei Ma, Yiting Wang, Yao Yao +4

This paper investigates the faithfulness of multimodal large language model (MLLM) agents in a graphical user interface (GUI) environment, aiming to address the research question o…

cs.CL2024

Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities

Tianjie Ju, Yiting Wang, Xinbei Ma +7

The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solvi…

cs.CL2024

CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation

Xinbei Ma, Zhuosheng Zhang, Hai Zhao

Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments, especially for graphical…

cs.CL2024

On the Robustness of Editing Large Language Models

Xinbei Ma, Tianjie Ju, Jiyang Qiu +4

Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation…

cs.CL2023★ 11 cited

Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents

Zhuosheng Zhang, Yao Yao, Aston Zhang +8

Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of…

cs.CL2023

Multi-turn Dialogue Comprehension from a Topic-aware Perspective

Xinbei Ma, Yi Xu, Hai Zhao +1

Dialogue related Machine Reading Comprehension requires language models to effectively decouple and model multi-turn dialogue passages. As a dialogue development goes after the int…

cs.CL2023★ 14 cited

Query Rewriting for Retrieval-Augmented Large Language Models

Xinbei Ma, Yeyun Gong, Pengcheng He +2

Large Language Models (LLMs) play powerful, black-box readers in the retrieve-then-read pipeline, making remarkable progress in knowledge-intensive tasks. This work introduces a ne…