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arXiv Computation and Language By ZhuoXin Liu, Zhiming Ma, Ying Zhang, Mengzheng Yang, Yifan Wang, Zhengqi Huang, Yanhan Zhou, Zekun Lin, Jun Zhang, Shun Zhang, Yue Chen, Qiao Zhao, Peng Chen

RiskChainBench: A Benchmark for Obfuscated Platform Message Restoration and Evidence-Grounded Web Investigation

Read the original on arXiv Computation and Language →

RiskChainBench is a new benchmark that pairs 3,600 synthetic token‑text restoration inputs with 600 human‑labeled local web environments to evaluate how well models can recover obfuscated platform messages and then investigate the associated websites. The benchmark measures both message restoration accuracy and the subsequent web‑investigation decision, using a fixed multimodal evidence judge to assess faithfulness, sufficiency, completeness, and consistency. Across ten models, performance varies widely, with entry recovery ranging from 35.2% to 95.2% and web decision accuracy from 26.3% to 62.8%, highlighting execution failures as the main bottleneck.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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