提出Stemma方法，利用诱导决策区域揭示大模型来源，提升黑盒测试的鲁棒性。
arXiv:2607.25880v1 Announce Type: cross Abstract: LLM provenance testing asks whether a suspect LLM belongs to the same lineage as a source. Existing …
用检索增强搜索做黑盒程序优化，beam search逐步迭代，LLM优化新思路。
arXiv:2501.18916v2 Announce Type: replace Abstract: Recent work has demonstrated the potential of large language models (LLMs) for program optimizatio…
无需访问模型内部，用广义JS散度精准识别AI生成文本，给大模型内容检测开了扇新窗
arXiv:2510.07500v3 Announce Type: replace Abstract: We study black-box detection of machine-generated text under practical constraints: the scoring mo…
系统性对比多种大模型黑盒不确定性估计方法，助你理解LLM可靠性与风险量化。
arXiv:2606.19868v1 Announce Type: new Abstract: Although large language models (LLMs) have shown strong capabilities across a wide range of tasks, the…
基于结构信息理论的黑盒不确定性量化方法，无需访问模型内部，已被UAI 2026接收，为LLM可靠性评估提供新思路。
arXiv:2511.16275v4 Announce Type: replace-cross Abstract: Reliable uncertainty quantification (UQ) is essential for deploying large language models (L…