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9 故事创新
DeltaML-Bench 发现代理脚手架改变了机器学习研究任务的成功
新的 arXiv 基准测试报告称，基于搜索的脚手架极大地改善了 GPT-5 在不完善的机器学习研究存储库上的结果，而标准配置则显示出规范游戏。arxiv.org创新
预印本提出了对物理视觉语言模型预测的答案级信任检查
一份新的预印本提出了一种与模型无关的方法，用于确定有关物理量的个体视觉语言答案是否值得信赖。受控干预可以捕获一些重复一致遗漏的稳定但不正确的答案，但拒绝更多的失败也会减少保留的正确答案。arxiv.org创新
预印本审计发现常见的信用信号无法识别法学硕士代理人中因果重要的步骤
arXiv 预印本报告称，在 ALFWorld 重播审计中，三个广泛使用的阶梯级信用信号在识别哪些决策因果性地改变了 LLM 代理人的结果方面并没有比机会更好。arxiv.org安全
Preprint 为强化学习代理提出了自适应安全防护罩
一份新的预印本建议在强化学习代理学习未知的转移概率时更新其安全约束，从而可能将概率屏蔽扩展到环境模型不完整的设置。arxiv.org创新
Paper outlines assurance path for an onboard ML helicopter-weight estimator
A new arXiv preprint describes an LSTM-based supervised model for estimating helicopter weight during takeoff and an assurance process aimed at running it on legacy airborne computers.arxiv.org创新
DeltaMomentum paper proposes direction-aware optimizer updates for neural-network training
An arXiv preprint introduces DeltaMomentum, an optimizer update designed to forget frequently and rarely seen gradient directions at different rates, reporting faster training across language, image and vision benchmarks.arxiv.org创新
Transformer study estimates days before severe COPD flare-ups from home-ventilator data
An arXiv paper describes a two-stage transformer that uses seven days of home-ventilator pressure and flow waveforms to identify high risk of severe AECOPD and estimate the days remaining before an event. The authors report strong results, but the source does not establish clinical deployment or external validation.arxiv.org创新
Preprint proposes a two-hemisphere architecture for continual learning
An arXiv preprint proposes 4MAS, a neural-model architecture combining asymmetric modules, memory mechanisms, experience replay and sleep-like consolidation to address catastrophic forgetting.arxiv.org创新
Paper proposes using an LLM to generate tabular anomaly detectors from normal data
An arXiv paper introduces LLM-Detector, a prompt-based method that uses an LLM to synthesize anomaly-scoring code from statistical summaries, causal dependencies and prototypes in normal tabular data. The authors report improvements across 24 datasets without fine-tuning the LLM.arxiv.org