Why LLM Decisions Should Be Deterministic
LLM决策的确定性边界关键不是一致性,而是可审计性——每个决策都有可复现的实现。
TL;DR: I originally treated deterministic boundaries around LLMs as a consistency mechanism. I now think their real value is auditability. If the syst…
LLM决策的确定性边界关键不是一致性,而是可审计性——每个决策都有可复现的实现。
TL;DR: I originally treated deterministic boundaries around LLMs as a consistency mechanism. I now think their real value is auditability. If the syst…
医学影像模型开发的未来方向:如何在自主生成的同时保证全流程可审计?这篇论文提出了新框架。
arXiv:2607.10522v1 Announce Type: cross Abstract: Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by co…
基于呼吸音和临床信号,用可审计的LLM提示链工作流实现急性哮喘风险评估,医疗AI新突破。
arXiv:2606.08247v1 Announce Type: cross Abstract: Acute asthma risk assessment requires rapid interpretation of respiratory sounds, oxygenation, airfl…
基于人类编写本体论,实现LLM代理的可证明安全与可审计性,为智能体可靠性提供新路径。
arXiv:2606.04903v1 Announce Type: cross Abstract: We introduce the LLM agent architecture Agentic Redux, intended for use with nontrivial problem doma…