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提出 MED3pa + Detectron 的统一诊断流程，通过定位低置信度患者子群并检测有害协变量偏移，来评估临床预测模型的可靠性；在 MIMIC-IV 三个临床任务上验证了局部偏移检测的价值。
研究评估了大语言模型在患者证据随时间演变时更新临床判断的可靠性，发现模型存在对恶化证据反应过度、受先验判断因果影响等失败模式，并提出EVLU方法来识别更可靠但覆盖更少的判断修订，确立了纵向信念更新作为LLM可靠性的独立评估维度。
This NeurIPS 2026 workshop paper introduces Long-Transduction, a controlled diagnostic benchmark measuring how well models sustain stateful, context-dependent operations over long generations. Evaluating seven open-weight models, it finds severe degradation when scaling context length (up to 128K), varying input format, and increasing local task complexity.
The article shares seven practical code-based guards to make AI agents more reliable by mitigating failures, such as verifying actions before execution and ensuring state consistency, which complement prompt improvements.
The article describes a bug where an AI voice model on Telnyx calls ended prematurely due to a tool call, and the fix involved implementing code guards to prevent early termination, highlighting reliability concerns with model-controlled actions.
The paper introduces PLSP, a pre-hoc framework for out-of-distribution prediction, using credibility metrics to enhance machine learning model reliability.
The author conducted a test comparing multiple AI models on a research task and found that models sometimes confidently disagree. They suggest that AI agents should consider multiple model opinions for important decisions like planning, code review, or research, and ask how others handle this.
A comprehensive survey on uncertainty quantification in symbolic regression, reviewing frequentist, Bayesian, and model selection approaches to address the lack of reliability support in real-world decision processes.