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Lejun Zhang

Publications and source records attributed to Lejun Zhang.

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WolfSociety: Understanding Collective Risk from Harmful-Agent Scaling in Financial Agent Societies

Safety evaluations typically focus on individual agents, but interacting agents can spread harmful information and influence the environment in which later decisions are made. We study how collective failure changes with harmful-agent fraction and society size in a controlled financial agent society, where agents communicate over a social network and trade in a shared market. In the primary financial scenario, collective failure requires broad harmful diffusion together with severe price dislocation or liquidity stress. Across all tested society sizes, failure remains rare at low harmful fractions but rises sharply over a narrow range. As society size grows from N=100 to N=2000, the harmful fraction associated with a 50% failure probability decreases from 4.7% to 2.2%, while the corresponding number of harmful agents increases from approximately 5 to 44. In contrast, when the number of harmful agents is held fixed, their impact becomes weaker as the society grows. Controlled interventions further show that broader network reach shifts the collapse boundary toward lower harmful fractions, whereas stronger conformity alone has little effect. To characterize these effects, we introduce Agent Society Dynamics, a finite-size framework for relating harmful-agent fraction, society size, and interaction structure to collective failure. Overall, our results reveal a nonlinear, size-dependent collapse transition in financial agent societies, showing that collective failure depends not only on the prevalence of harmful agents but also on the size and interaction structure of the surrounding society. Code is available at https://github.com/SAIL-Research-Lab/WolfSociety.

physics.soc-ph

Lotus: Efficient LLM Training by Randomized Low-Rank Gradient Projection with Adaptive Subspace Switching

Training efficiency in large-scale models is typically assessed through memory consumption, training time, and model performance. Current methods often exhibit trade-offs among these metrics, as optimizing one generally degrades at least one of the others. Addressing this trade-off remains a central challenge in algorithm design. While GaLore enables memory-efficient training by updating gradients in a low-rank subspace, it incurs a comparable extra training time cost due to the Singular Value Decomposition(SVD) process on gradients. In this paper, we propose Lotus, a method that resolves this trade-off by simply modifying the projection process. We propose a criterion that quantifies the displacement of the unit gradient to enable efficient transitions between low-rank gradient subspaces. Experimental results indicate that Lotus is the most efficient method, achieving a 30% reduction in training time and a 40% decrease in memory consumption for gradient and optimizer states. Additionally, it outperforms the baseline method in both pre-training and fine-tuning tasks.

cs.LG

TennisTV: Do Multimodal Large Language Models Understand Tennis Rallies?

Multimodal large language models (MLLMs) excel at general video understanding but struggle with fast, high-frequency sports like tennis, where rally clips are short yet information-dense. To systematically evaluate MLLMs in this challenging domain, we present TennisTV, the first and most comprehensive benchmark for tennis video understanding. TennisTV models each rally as a temporal-ordered sequence of consecutive stroke events, using automated pipelines for filtering and question generation. It covers 8 tasks from the stroke level to the rally level and includes 2527 human-verified questions. Evaluating 17 representative MLLMs, we provide the first systematic assessment of tennis video understanding. Results yield two key insights: (i) frame-sampling density should be tailored and balanced across tasks, and (ii) improving temporal grounding is essential for stronger reasoning.

cs.CV

Value driven Analysis Framework of Service Ecosystem Evolution Mechanism

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. To meet the customized user demands, Service Ecosystem is emerging as a complex social-technology system, which is formed with various IT services through cross-border integration. However, how to analyze and promote the evolution mechanism of service ecosystem is still a serious challenge in the field, which is of great significance to achieve the expected system evolution trends. Based on this, this paper proposes a value-driven analysis framework of service ecosystem, including value creation, value operation, value realization and value distribution. In addition, a computational experiment system is established to verify the effectiveness of the analysis framework, which stimulates the effect of different operation strategies on the value network in the service ecosystem. The result shows that our analysis framework can provide new means and ideas for the analysis of service ecosystem evolution, and can also support the design of operation strategies. Index

cs.OH