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Chen Shen

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5 papers

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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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JBHI Journal 2025 Journal Article

Cybersecure End-to-End FPGA-Accelerated ECG Monitoring for Precision Diagnosis With Personalized CWT and Adversarial Defense

Tiancheng Cao
Wei Soon Ng
Chen Shen
Hongtao Li
Rong Tan
Dawei Wang
Hen-Wei Huang

Advancements in wearable technology and edge computing have transformed cardiovascular monitoring, driving the demand for private, secure, and real-time diagnostic solutions. This paper presents an edge-wearable ECG monitoring system that integrates personalized continuous wavelet transform (CWT) preprocessing, a DeepFool–FGSM adversarial defense, and an optimized parallel PoolFormer architecture for resource-constrained FPGA deployment. The personalized CWT captures individual-specific ECG features and mitigates model-inversion privacy risks. The defense approach balances robustness and computational efficiency and reduces hardware complexity and energy via quantization-aware training (QAT). Evaluations on field programmable gate array (FPGA) confirm high diagnostic accuracy (98. 93%), real-time inference (latency <1. 7 ms), and improved robustness against adversarial perturbations, with 0. 055 W FPGA-core power. Together, the system delivers confidentiality, integrity, and availability for cybersecure, personalized ECG monitoring at the edge.

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NeurIPS Conference 2025 Conference Paper

GeoCAD: Local Geometry-Controllable CAD Generation with Large Language Models

Zhanwei Zhang
Kaiyuan Liu
Junjie Liu
Wenxiao Wang
Binbin Lin
Liang Xie
Chen Shen
Deng Cai

Local geometry-controllable computer-aided design (CAD) generation aims to modify local parts of CAD models automatically, enhancing design efficiency. It also ensures that the shapes of newly generated local parts follow user-specific geometric instructions (e. g. , an isosceles right triangle or a rectangle with one corner cut off). However, existing methods encounter challenges in achieving this goal. Specifically, they either lack the ability to follow textual instructions or are unable to focus on the local parts. To address this limitation, we introduce GeoCAD, a user-friendly and local geometry-controllable CAD generation method. Specifically, we first propose a complementary captioning strategy to generate geometric instructions for local parts. This strategy involves vertex-based and VLLM-based captioning for systematically annotating simple and complex parts, respectively. In this way, we caption $\sim$221k different local parts in total. In the training stage, given a CAD model, we randomly mask a local part. Then, using its geometric instruction and the remaining parts as input, we prompt large language models (LLMs) to predict the masked part. During inference, users can specify any local part for modification while adhering to a variety of predefined geometric instructions. Extensive experiments demonstrate the effectiveness of GeoCAD in generation quality, validity and text-to-CAD consistency.

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NeurIPS Conference 2024 Conference Paper

Instance-adaptive Zero-shot Chain-of-Thought Prompting

Xiaosong Yuan
Chen Shen
Shaotian Yan
Xiaofeng Zhang
Liang Xie
Wenxiao Wang
Renchu Guan
Ying Wang

Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of large language models (LLMs) in real-world reasoning tasks. Nonetheless, the efficacy of a singular, task-level prompt uniformly applied across the whole of instances is inherently limited since one prompt cannot be a good partner for all, a more appropriate approach should consider the interaction between the prompt and each instance meticulously. This work introduces an instance-adaptive prompting algorithm as an alternative zero-shot CoT reasoning scheme by adaptively differentiating good and bad prompts. Concretely, we first employ analysis on LLMs through the lens of information flow to detect the mechanism under zero-shot CoT reasoning, in which we discover that information flows from question to prompt and question to rationale jointly influence the reasoning results most. We notice that a better zero-shot CoT reasoning needs the prompt to obtain semantic information from the question then the rationale aggregates sufficient information from the question directly and via the prompt indirectly. On the contrary, lacking any of those would probably lead to a bad one. Stem from that, we further propose an instance-adaptive prompting strategy (IAP) for zero-shot CoT reasoning. Experiments conducted with LLaMA-2, LLaMA-3, and Qwen on math, logic, and commonsense reasoning tasks (e. g. , GSM8K, MMLU, Causal Judgement) obtain consistent improvement, demonstrating that the instance-adaptive zero-shot CoT prompting performs better than other task-level methods with some curated prompts or sophisticated procedures, showing the significance of our findings in the zero-shot CoT reasoning mechanism.

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AAAI Conference 2023 Conference Paper

Emergence of Punishment in Social Dilemma with Environmental Feedback

Zhen Wang
Zhao Song
Chen Shen
Shuyue Hu

Altruistic punishment (or punishment) has been extensively shown as an important mechanism for promoting cooperation in human societies. In AI, the emergence of punishment has received much recent interest. In this paper, we contribute with a novel evolutionary game theoretic model to study the impacts of environmental feedback. Whereas a population of agents plays public goods games, there exists a third-party population whose payoffs depend not only on whether to punish or not, but also on the state of the environment (e.g., how cooperative the agents in a social dilemma are). Focusing on one-shot public goods games, we show that environmental feedback, by itself, can lead to the emergence of punishment. We analyze the co-evolution of punishment and cooperation, and derive conditions for their co-presence, co-dominance and co-extinction. Moreover, we show that the system can exhibit bistability as well as cyclic dynamics. Our findings provide a new explanation for the emergence of punishment. On the other hand, our results also alert the need for careful design of implementing punishment in multi-agent systems, as the resulting evolutionary dynamics can be somewhat complex.

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