Jingyao Zhang — accepted AI-conference papers

2026

Enhancing Pre-training Data Detection in LLMs Through Discriminative and Symmetric Prefix Selection

Kai Sun, Yuxin Lin, Bo Dong, Jingyao Zhang, Bin Shi

AAAI 2026technical

The rapid development of large language models (LLMs) has relied on access to high-quality, large-scale datasets, yet growing concerns around data privacy and security have spurred substantial research into pre-training data detection. While state-of-the-art (SOTA) methods such as RECALL and CON-REC

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2026

Scope Delineation Before Localization: A Two-Stage Framework for Enhancing Failure Attribution in Multi-Agent Systems

Kai Sun, Wenqiang Li, Bo Dong, Yuxin Lin, Jingyao Zhang, Bin Shi

AAAI 2026technical

Large language models (LLMs) are seeing growing adoption in multi-agent systems. In these systems, efficient failure attribution is critical for ensuring robustness and interpretability. Current LLM-based attribution methods often face challenges with lengthy logs and lacking expert knowledge. Drawi

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