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Zhenpeng Chen

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4 papers

1 author row

AAAI Conference 2026 Conference Paper

BAMAS: Structuring Budget-Aware Multi-Agent Systems

Liming Yang
Junyu Luo
Xuanzhe Liu
Yiling Lou
Zhenpeng Chen

Large language model (LLM)-based multi-agent systems have emerged as a powerful paradigm for enabling autonomous agents to solve complex tasks. As these systems scale in complexity, cost becomes an important consideration for practical deployment. However, existing work rarely addresses how to structure multi-agent systems under explicit budget constraints. In this paper, we propose BAMAS, a novel approach for building multi-agent systems with budget awareness. BAMAS first selects an optimal set of LLMs by formulating and solving an Integer Linear Programming problem that balances performance and cost. It then determines how these LLMs should collaborate by leveraging a reinforcement learning-based method to select the interaction topology. Finally, the system is instantiated and executed based on the selected agents and their collaboration topology. We evaluate BAMAS on three representative tasks and compare it with state-of-the-art agent construction methods. Results show that BAMAS achieves comparable performance while reducing cost by up to 86%.

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NeurIPS Conference 2025 Conference Paper

Can Agent Fix Agent Issues?

Alfin Wijaya Rahardja
Junwei Liu
Weitong Chen
Zhenpeng Chen
Yiling Lou

LLM-based agent systems are emerging as a new software paradigm and have been widely adopted across diverse domains such as medicine, robotics, and programming. However, maintaining these systems requires substantial effort, as they are inevitably prone to bugs and continually evolve to meet changing external requirements. Therefore, automatically resolving agent issues (i. e. ,bug reports or feature requests) is a crucial and challenging task. While recent software engineering (SE) agents (e. g. , SWE-agent) have shown promise in addressing issues in traditional software systems, it remains unclear how effectively they can resolve real-world issues in agent systems, which differ significantly from traditional software. To fill this gap, we first manually analyze 201 real-world agent issues and identify common categories of agent issues. We then spend 500 person-hours constructing AgentIssue-bench, a reproducible benchmark comprising 50 agent issue resolution tasks (each with an executable environment and failure-triggering tests). We further evaluate state-of-the-art SE agents on AgentIssue-bench and reveal their limited effectiveness (. e. , with only 0. 67% - 4. 67% resolution rates). These results underscore the unique challenges of maintaining agent systems compared to traditional software, highlighting the need for further research to develop advanced SE agents for resolving agent issues.

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IJCAI Conference 2020 Conference Paper

Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification (Extended Abstract)

Zhenpeng Chen
Sheng Shen
Ziniu Hu
Xuan Lu
Qiaozhu Mei
Xuanzhe Liu

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages. To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i. e. , the source language, usually English) to another language with fewer labels (i. e. , the target language). The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages. This approach, however, often fails to capture sentiment knowledge specific to the target language. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns. We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language. The learned representations are then integrated to facilitate cross-lingual sentiment classification.

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AAAI Conference 2020 Conference Paper

Solving Sequential Text Classification as Board-Game Playing

Chen Qian
Fuli Feng
Lijie Wen
Zhenpeng Chen
Li Lin
Yanan Zheng
Tat-Seng Chua

Sequential Text Classiﬁcation (STC) aims to classify a sequence of text fragments (e. g. , words in a sentence or sentences in a document) into a sequence of labels. In addition to the intra-fragment text contents, considering the interfragment context dependencies is also important for STC. Previous sequence labeling approaches largely generate a sequence of labels in left-to-right reading order. However, the need for context information in making decisions varies across different fragments and is not strictly organized in a left-to-right order. Therefore, it is appealing to label the fragments that need less consideration of context information ﬁrst before labeling the fragments that need more. In this paper, we propose a novel model that labels a sequence of fragments in jumping order. Speciﬁcally, we devise a dedicated boardgame to develop a correspondence between solving STC and board-game playing. By deﬁning proper game rules and devising a game state evaluator in which context clues are injected, at each round, each player is effectively pushed to ﬁnd the optimal move without position restrictions via considering the current game state, which corresponds to producing a label for an unlabeled fragment jumpily with the consideration of the contexts clues. The ﬁnal game-end state is viewed as the optimal label sequence. Extensive results on three representative datasets show that the proposed approach outperforms the state-of-the-art methods with statistical signiﬁcance.

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