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Liuyu Xiang

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6 papers
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6

AAMAS Conference 2026 Conference Paper

The Web Tool Trap: Understanding and Mitigating Over-Reliance in Browsing Agents

  • Jiawei Guo
  • Hongjie Nie
  • Qianbo Zang
  • Shu Yang
  • Shuodi Liu
  • Yiwei Ru
  • Liuyu Xiang
  • Di Wang

Large Language Model (LLM) agents that browse the web are increasingly important, but their effectiveness is hindered by imperfect integration of internal knowledge and external tools. We introduce BrowseBench and present the first systematic investigation into over-reliance patterns of browsing agents. Through controlled experiments, we identify three distinct failure modes: (1) Excessive Conservatism—unnecessary tool invocations for known information; (2) Over-trust in Web Sources—uncritical acceptance of retrieved content; and (3) Planning Deficiency—lack of query decomposition strategies. To address these, we propose three mitigation strategies: Direct Preference Optimization (DPO), Attention Refinement (AR), and Hierarchical Query Decomposition (HQD). Experiments demonstrate that our interventions significantly reduce over-reliance and enhance performance.

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AAMAS Conference 2025 Conference Paper

RainbowArena: A Multi-Agent Toolkit for Reinforcement Learning and Large Language Models in Competitive Tabletop Games

  • Yingzhuo Liu
  • Shuodi Liu
  • Hongsong Tang
  • Yubing Ma
  • Zikang Li
  • Junge Zhang
  • Liuyu Xiang
  • Zhaofeng He

Tabletop games have gained little to no attention, despite offering a range of unique challenges compared to card or board games. We introduce RainbowArena, an open-source toolkit for reinforcement learning and large language models in competitive tabletop games. The goal of RainbowArena is to provide a unified, scalable platform that supports both Reinforcement Learning (RL) and Large Language Models (LLM), and push forward the research in tabletop games. RainbowArena consists of three modules: game, agent and evaluation. We design unified components and interfaces for various tabletop games. To better integrate with game environments, we devise an efficient self-play framework for RL agents, and a standardized prompt structure for LLM agents. Additionally, agents of all types can be evaluated within the evaluation framework. Finally, we evaluate various types of agents across different games and analyze the runtime efficiency for each game.

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AAAI Conference 2024 Conference Paper

Debiased Novel Category Discovering and Localization

  • Juexiao Feng
  • Yuhong Yang
  • Yanchun Xie
  • Yaqian Li
  • Yandong Guo
  • Yuchen Guo
  • Yuwei He
  • Liuyu Xiang

In recent years, object detection in deep learning has experienced rapid development. However, most existing object detection models perform well only on closed-set datasets, ignoring a large number of potential objects whose categories are not defined in the training set. These objects are often identified as background or incorrectly classified as pre-defined categories by the detectors. In this paper, we focus on the challenging problem of Novel Class Discovery and Localization (NCDL), aiming to train detectors that can detect the categories present in the training data, while also actively discover, localize, and cluster new categories. We analyze existing NCDL methods and identify the core issue: object detectors tend to be biased towards seen objects, and this leads to the neglect of unseen targets. To address this issue, we first propose an Debiased Region Mining (DRM) approach that combines class-agnostic Region Proposal Network (RPN) and class-aware RPN in a complementary manner. Additionally, we suggest to improve the representation network through semi-supervised contrastive learning by leveraging unlabeled data. Finally, we adopt a simple and efficient mini-batch K-means clustering method for novel class discovery. We conduct extensive experiments on the NCDL benchmark, and the results demonstrate that the proposed DRM approach significantly outperforms previous methods, establishing a new state-of-the-art.

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AAAI Conference 2022 Conference Paper

ReMoNet: Recurrent Multi-Output Network for Efficient Video Denoising

  • Liuyu Xiang
  • Jundong Zhou
  • Jirui Liu
  • Zerun Wang
  • Haidong Huang
  • Jie Hu
  • Jungong Han
  • Yuchen Guo

While deep neural network-based video denoising methods have achieved promising results, it is still hard to deploy them on mobile devices due to their high computational cost and memory demands. This paper aims to develop a lightweight deep video denoising method that is friendly to resource-constrained mobile devices. Inspired by the facts that 1) consecutive video frames usually contain redundant temporal coherency, and 2) neural networks are usually over-parameterized, we propose a multi-input multi-output (MIMO) paradigm to process consecutive video frames within one-forward-pass. The basic idea is concretized to a novel architecture termed Recurrent Multi-output Network (ReMoNet), which consists of recurrent temporal fusion and temporal aggregation blocks and is further reinforced by similarity-based mutual distillation. We conduct extensive experiments on NVIDIA GPU and Qualcomm Snapdragon 888 mobile platform with Gaussian noise and simulated Image- Signal-Processor (ISP) noise. The experimental results show that ReMoNet is both effective and efficient on video denoising. Moreover, we show that ReMoNet is more robust under higher noise level scenarios.

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AAAI Conference 2019 Conference Paper

Adaptive Region Embedding for Text Classification

  • Liuyu Xiang
  • Xiaoming Jin
  • Lan Yi
  • Guiguang Ding

Deep learning models such as convolutional neural networks and recurrent networks are widely applied in text classification. In spite of their great success, most deep learning models neglect the importance of modeling context information, which is crucial to understanding texts. In this work, we propose the Adaptive Region Embedding to learn context representation to improve text classification. Specifically, a metanetwork is learned to generate a context matrix for each region, and each word interacts with its corresponding context matrix to produce the regional representation for further classification. Compared to previous models that are designed to capture context information, our model contains less parameters and is more flexible. We extensively evaluate our method on 8 benchmark datasets for text classification. The experimental results prove that our method achieves state-of-the-art performances and effectively avoids word ambiguity.

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IJCAI Conference 2019 Conference Paper

Incremental Few-Shot Learning for Pedestrian Attribute Recognition

  • Liuyu Xiang
  • Xiaoming Jin
  • Guiguang Ding
  • Jungong Han
  • Leida Li

Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications. However, most existing methods are designed for a fixed set of attributes. They are unable to handle the incremental few-shot learning scenario, i. e. adapting a well-trained model to newly added attributes with scarce data, which commonly exists in the real world. In this work, we present a meta learning based method to address this issue. The core of our framework is a meta architecture capable of disentangling multiple attribute information and generalizing rapidly to new coming attributes. By conducting extensive experiments on the benchmark dataset PETA and RAP under the incremental few-shot setting, we show that our method is able to perform the task with competitive performances and low resource requirements.

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v2026.09.13