Large language models (LLMs) have recently advanced graph neural networks (GNNs) by enriching node representations with semantic information, giving rise to LLM-enhanced GNNs that achieve substantial performance gains. However, how such semantic enhancement affects privacy risks remains largely underexplored. To bridge...
Long-Zhu He, Ze-Kun Wen, Chao-Zhuo Li et al.· 0 citations
World models support planning, control, learning from imagined experience, and model-mediated evaluation by predicting action-conditioned consequences. Their safety cannot be inferred from predictive accuracy alone: errors in observations, state and memory, dynamics, evaluation, execution, or feedback can propagate int...
Xiao-Mao Wang, Li-Tian Zhang, Xin-Ze Shi et al.· 0 citations
Agentic orchestration delegates tasks to sub-agents with independent contexts, providing a key path to extending the effective execution horizon of large language model agents. However, existing orchestration methods face two limitations: in execution, current methods fix sub-agents'goals and harnesses at creation time...
Xue-Yang Feng, Chao-Zhuo Li, Xiao-He Bo et al.· 0 citations
Text-Attributed Graphs (TAGs) are prevalent in various real-world scenarios, where each node is associated with a text attribute. Representation learning on TAGs relies on a comprehensive understanding of both the textual attributes and the topological connections. Recent works have enhanced graph neural networks (GNNs...
Hao Yan, Chao-Zhuo Li, Jun Yin et al.· ACM Transactions on Intellig...· 0 citations
Results show that long-horizon reflective data is an effective route toward self-improving agents, and synthesize long-horizon improvement trajectories from machine learning and algorithmic programming tasks, two domains that offer verifiable feedback and reward sustained iteration.
Hong-Jin Qian, Chao-Fan Li, Kun Luo et al.· 0 citations
A holistic framework based on learnable information gain, which measures how much novel, parameterizable information a round provides relative to the previous round, and proposes ATRI (Adaptive Training Regulation via Information-gain), which reweights samples within a round and halts training across rounds when inform...
Chen-Xu Wang, Chao-Zhuo Li, Xin-Ze Shi et al.· 0 citations
Just-In-Time Agent Memory (JAM), a trainable framework for query-conditioned context construction at runtime, is proposed, where it achieves stronger task performance than AOT-style memory systems while remaining substantially more efficient than prior trained agentic memory approaches.
Bing-Yu Yan, Chao-Fan Li, Hong-Jin Qian et al.· 0 citations
The results show that LLMs continue to struggle with the cross-lingual understanding of Chinese internet buzzwords, particularly in fine-grained non-literal interpretation, robust equivalent matching under option perturbations, and calibrated harmfulness detection, which highlights the persistent challenges posed by cu...
Yi-Fan Wang, Jun-Yu Lu, Qi-Fan Wang et al.· 0 citations
TFM4POP is the first framework to introduce tabular foundation models (TFMs) into popularity prediction, leveraging their pre-trained tabular priors to unify the modeling of multiple heterogeneous information views and constructs a comprehensive multi-view cascade benchmark that covers all four information views.
Wenting Zhu, Chenghua Gong, Sanchuan Guo et al.· 0 citations
EvoBreak is an experience-conditioned sequential attack that operates through individually benign attack-stage tasks and induced experiences, revealing benign experience composition as a persistent attack surface in self-evolving agents.
Bing-Yu Yan, Xiao-Ming Zhang, Chao-Zhuo Li et al.· 2 citations
This paper proposes the End-to-End Dual Dynamic (ED2) recommender, the first LLM-based sequential RS which adopts dual dynamic index mechanism, targeting resolving the above limitations simultaneously.
Jun Yin, Zhengxin Zeng, Mingzheng Li et al.· The Web Conference· 27 citations
This work introduces Audio-Zero, the first label-free self-evolution framework in the field of LALMs that improves fine-grained auditory perception and reasoning and reveals that increasingly fine-grained auditory descriptions emerge naturally from game pressure.
Siqian Tong, Xuan Li, Chao-Zhuo Li et al.· arXiv.org· 1 citation
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