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Chen Chu

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9 papers

1 author row

AAAI Conference 2026 Conference Paper

Adaptive Theory of Mind for LLM-based Multi-Agent Coordination

Chunjiang Mu
Ya Zeng
Qiaosheng Zhang
Kun Shao
Chen Chu
Hao Guo
Danyang Jia
Zhen Wang

Theory of Mind (ToM) refers to the ability to reason about others’ mental states, and higher-order ToM involves considering that others also possess their own ToM. Equipping large language model (LLM)-driven agents with ToM has long been considered to improve their coordination in multiagent collaborative tasks. However, we find that misaligned ToM orders—mismatches in the depth of ToM reasoning between agents—can lead to insufficient or excessive reasoning about others, thereby impairing their coordination. To address this issue, we design an adaptive ToM (A-ToM) agent, which can align in ToM orders with its partner. Based on prior interactions, the agent estimates the partner’s likely ToM order and leverages this estimation to predict the partner’s action, thereby facilitating behavioral coordination. We conduct empirical evaluations on four multi-agent coordination tasks: a repeated matrix game, two grid navigation tasks and an Overcooked task. The results validate our findings on ToM alignment and demonstrate the effectiveness of our AToM agent. Furthermore, we discuss the generalizability of our A-ToM to non-LLM-based agents, as well as what would diminish the importance of ToM alignment.

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AAMAS Conference 2026 Conference Paper

CraftUtopia: A LLM-based Multi-Agent System for Collaborative Construction in Minecraft

Wanli Fu
Hao Li
Siyue Ren
Chenxi Xing
Yang Chen
Chen Chu
Zhen Wang
Shuyue Hu

LLM-based multi-agent construction is a growing research area but prior works still suffer from low success rates for construction, reliance on predefined templates, and poor scalability as the number of agents increases. To address these challenges, we present CraftUtopia, an LLM-based multi-agent system (MAS) that constructs 3D structures in Minecraft from a single 2D reference image. CraftUtopia operates in two stages: Design, which converts the image into a Minecraft-compatible 3D blueprint, and Build, which decomposes the blueprint into spatially disjoint subtasks for parallel execution. CraftUtopia scales efficiently via two mechanisms: (i) hierarchical coordination, which organizes agents in a manager–foreman–worker hierarchy to separate responsibilities, and (ii) skill acquisition, which distills recurring action sequences into a shared skill library to reduce repeated LLM replanning. Across three representative builds, CraftUtopia achieves 100% success over five trials using only 2D inputs, scales effectively with more workers, and exhibits emergent human-like behaviors. A full demo is available at: https: //github. com/craftutopia-demo/CraftUtopia.

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AAAI Conference 2026 Conference Paper

Geo2Vec: Shape- and Distance-Aware Neural Representation of Geospatial Entities

Chen Chu
Cyrus Shahabi

Spatial representation learning is fundamental to GeoAI applications, including urban analytics, as it encodes the shapes, locations, and spatial relationships (topological and distance-based) of geo-entities such as points, polylines, and polygons. Existing methods either target a single geo-entity type or, like Poly2Vec, decompose entities into simpler components to enable Fourier transformation, introducing high computational cost. Moreover, since the transformed space lacks geometric alignment, these methods rely on uniform, non-adaptive sampling, which blurs fine-grained features like edges and boundaries. To address these limitations, we introduce Geo2Vec, a novel method inspired by signed distance fields (SDF) that operates directly in the original space. Geo2Vec adaptively samples points and encodes their signed distances (positive outside, negative inside), capturing geometry without decomposition. A neural network trained to approximate the SDF produces compact, geometry-aware, and unified representations for all geo-entity types. Additionally, we propose a rotation-invariant positional encoding to model high-frequency spatial variations and construct a structured and robust embedding space for downstream GeoAI models. Empirical results show that Geo2Vec consistently outperforms existing methods in representing shape and location, capturing topological and distance relationships, and achieving greater efficiency in real-world GeoAI applications.

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AAMAS Conference 2026 Conference Paper

Reputation as a Solution to Cooperation Collapse in LLM-based MASs

Siyue Ren
Wanli Fu
Xinkun Zou
Chen Shen
Yi Cai
Chen Chu
Zhen Wang
Shuyue Hu

Cooperation has long been a fundamental topic in both human society and AI systems. However, recent studies indicate that the collapse of cooperation may emerge in multi-agent systems (MASs) driven by large language models (LLMs). To address this challenge, we explore reputation systems as a remedy. We propose RepuNet, a dynamic, dual-level reputation framework that models both agent-level reputation dynamics and system-level network evolution. Specifically, driven by direct interactions and indirect gossip, agents form reputations for both themselves and their peers, and decide whether to connect or disconnect other agents for future interactions. Through three distinct scenarios, we show that RepuNet effectively avoids cooperation collapse, promoting and sustaining cooperation in LLM-based MASs. Moreover, we find that reputation systems can give rise to rich emergent behaviors in LLM-based MASs, such as the formation of cooperative clusters, the social isolation of exploitative agents, and the preference for sharing positive gossip rather than negative ones. The GitHub repository for our project can be accessed via the following link: https: //github. com/RGB-0000FF/RepuNet.

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IJCAI Conference 2024 Conference Paper

A Successful Strategy for Multichannel Iterated Prisoner’s Dilemma

Zhen Wang
Zhaoheng Cao
Juan Shi
Peican Zhu
Shuyue Hu
Chen Chu

Iterated prisoner’s dilemma (IPD) and its variants are fundamental models for understanding the evolution of cooperation in human society as well as AI systems. In this paper, we focus on multichannel IPD, and examine how an agent should behave to obtain generally high payoffs under this setting. We propose a novel strategy that chooses to cooperate or defect by considering the difference in the cumulative number of defections between two agents. We show that our proposed strategy is nice, retaliatory, and forgiving. Moreover, we analyze the performance of our proposed strategy across different scenarios, including the self-play settings with and without errors, as well as when facing various opponent strategies. In particular, we show that our proposed strategy is invincible and never loses to any opponent strategy in terms of the expected payoff. Last but not least, we empirically validate the evolutionary advantage of our strategy, and demonstrate its potential to serve as a catalyst for cooperation emergence.

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AAAI Conference 2023 Conference Paper

A Pair-Approximation Method for Modelling the Dynamics of Multi-Agent Stochastic Games

Chen Chu
Zheng Yuan
Shuyue Hu
Chunjiang Mu
Zhen Wang

Developing a dynamical model for learning in games has attracted much recent interest. In stochastic games, agents need to make decisions in multiple states, and transitions between states, in turn, influence the dynamics of strategies. While previous works typically focus either on 2-agent stochastic games or on normal form games under an infinite-agent setting, we aim at formally modelling the learning dynamics in stochastic games under the infinite-agent setting. With a novel use of pair-approximation method, we develop a formal model for myopic Q-learning in stochastic games with symmetric state transition. We verify the descriptive power of our model (a partial differential equation) across various games through comparisons with agent-based simulation results. Based on our proposed model, we can gain qualitative and quantitative insights into the influence of transition probabilities on the dynamics of strategies. In particular, we illustrate that a careful design of transition probabilities can help players overcome the social dilemmas and promote cooperation, even if agents are myopic learners.

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IJCAI Conference 2022 Conference Paper

A Formal Model for Multiagent Q-Learning Dynamics on Regular Graphs

Chen Chu
Yong Li
Jinzhuo Liu
Shuyue Hu
Xuelong Li
Zhen Wang

Modeling the dynamics of multi-agent learning has long been an important research topic. The focus of previous research has been either on 2-agent settings or well-mixed infinitely large agent populations. In this paper, we consider the scenario where n Q-learning agents locate on regular graphs, such that agents can only interact with their neighbors. We examine the local interactions between individuals and their neighbors, and derive a formal model to capture the Q-value dynamics of the entire population. Through comparisons with agent-based simulations on different types of regular graphs, we show that our model describes the agent learning dynamics in an exact manner.

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IJCAI Conference 2022 Conference Paper

Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach

Zhen Wang
Chunjiang Mu
Shuyue Hu
Chen Chu
Xuelong Li

Understanding the learning dynamics in multiagent systems is an important and challenging task. Past research on multi-agent learning mostly focuses on two-agent settings. In this paper, we consider the scenario in which a population of infinitely many agents apply regret minimization in repeated symmetric games. We propose a new formal model based on the master equation approach in statistical physics to describe the evolutionary dynamics in the agent population. Our model takes the form of a partial differential equation, which describes how the probability distribution of regret evolves over time. Through experiments, we show that our theoretical results are consistent with the agent-based simulation results.

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IJCAI Conference 2018 Conference Paper

Impression Allocation for Combating Fraud in E-commerce Via Deep Reinforcement Learning with Action Norm Penalty

Mengchen Zhao
Zhao Li
Bo An
Haifeng Lu
Yifan Yang
Chen Chu

Conducting fraud transactions has become popular among e-commerce sellers to make their products favorable to the platform and buyers, which decreases the utilization efficiency of buyer impressions and jeopardizes the business environment. Fraud detection techniques are necessary but not enough for the platform since it is impossible to recognize all the fraud transactions. In this paper, we focus on improving the platform's impression allocation mechanism to maximize its profit and reduce the sellers' fraudulent behaviors simultaneously. First, we learn a seller behavior model to predict the sellers' fraudulent behaviors from the real-world data provided by one of the largest e-commerce company in the world. Then, we formulate the platform's impression allocation problem as a continuous Markov Decision Process (MDP) with unbounded action space. In order to make the action executable in practice and facilitate learning, we propose a novel deep reinforcement learning algorithm DDPG-ANP that introduces an action norm penalty to the reward function. Experimental results show that our algorithm significantly outperforms existing baselines in terms of scalability and solution quality.

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