Author

Ze-Xue He

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Review Jul 2026

AISPA: User-Centric System Prompt Auditing for Large Language Model Applications

System prompts are instructions configured by developers to govern the behaviors of foundation models in AI applications. They are used throughout commercial AI products, but are rarely disclosed to the public or regulators, creating a serious trust and accountability gap in the wide deployment of AI systems. In this p...

Xiangning Lin, Shenzhe Zhu, Shu Yang et al. · arXiv.org · 0 citations

#artificial intelligence Preprint Sep 2026

DocHop: Benchmarking Out-of-domain Multi-hop Reasoning in Information-Dense Documents

Multimodal Large Language Models (MLLMs) have achieved strong performance on structured visual understanding tasks such as chart and document question answering. However, existing benchmarks typically evaluate these domains in isolation, leaving underexplored a key capability: whether models can use textual context to...

Zhuoran Yu, Le Thien Phuc Nguyen, Jaden Park et al. · 1 citation

Jul 2026

Learning More from Less: Reinforcement Learning from Hindsight

Learning from Hindsight is presented, which brings hindsight relabeling to RL post-training of VLAs by scoring failed rollouts against the tasks they actually achieved, and achieves 5 times improvement in sample efficiency, and outperforms a dense progress-reward baseline on out-of-distribution LIBERO-PRO tasks.

Iris Xu, Sunshine Jiang, John Marangola et al. · arXiv.org · 0 citations