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本文讨论了Codex额度快速消耗可能的原因,包括IP质量问题、频繁切换账号等,并提供了自测用量是否正常的方法。
本文剖析了文档抽取系统中基于字段的选择性风险控制的三种失效模式,并提出一个由低到高的有效性阶梯式修复方案,通过使用前沿AI模型在真实数据上的实验证明了其改进效果。
SeqLLM is a framework that injects behavioral-sequence modeling into pretrained LLMs while preserving language ability, enabling joint analysis of text and behavior for high-stakes decisions. Deployed at WeChat Pay, it improves merchant screening precision from 92.0% to 97.5% and achieves state-of-the-art results on open recommendation benchmarks.
This paper introduces Opal (Opportunity-aware Policy Authorization for Laboratories), a framework that certifies whether adaptive experimentation should be enabled by precommitting to non-trivial adaptation, controlled target risk, and positive executed value after cost. It establishes an impossibility boundary and demonstrates the method on a Cell Painting dataset, achieving risk control and positive value.
深度复盘红杉中国(红山)在投资决策中的风控漏洞,以虎头局渣打饼行等案例揭示其‘快准狠’策略导致的尽调失灵与资本催熟问题,并总结其战略纠偏方向。
CP-Agent 提出了一种借助大型语言模型的校准风险控制方法,用于反馈驱动型竞赛编程,无需参数更新即可在基准测试上取得显著改进。
介绍了Conformal Selective Acting (CSA),一种用于RLVR训练的LLM的部署时包装器,它提供了对单个流的任意时刻有效的选择性风险控制,从而在不进行池化或长期平均的情况下,能够在受监管环境中安全部署。