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9 故事创新
Delta2Gamma 报告使用 EEG 检测阿尔茨海默病的准确度为 92.4%
一篇新的 arXiv 论文描述了一种自我监督的脑电图方法，该方法报告在 ADFTD 队列中区分阿尔茨海默病和认知正常对照的准确度为 92.4%。arxiv.org创新
Agentic ESOpt 提出了针对长视野 AI 代理的低内存微调
arXiv 论文介绍了 Agentic ESOpt，这是一种提出的进化策略框架，用于使用推理级 GPU 内存微调长视野语言模型代理。作者报告了 Qwen-3.5-27B 在 WebArena-Lite 上的改进以及 36 个提示优化设置中的 28 个的改进。arxiv.org创新
论文提出 CSE 可以评估 MSE 之外的不规则时间序列预测
arXiv 的一篇论文认为，均方误差可能会误判不规则的时间序列预测，并提出了一种在合成、半合成和八个真实世界数据集上进行测试的连续时间度量。arxiv.org创新
Study proposes transfer-aware curriculum sampling for language-model training
An arXiv paper introduces Relative Transfer, a measure of how training examples at different difficulty levels affect one another, and proposes Transfer-aware Dynamic Curriculum Sampling for adapting training schedules during post-training.arxiv.org创新
Co-RL paper reports label-free reasoning gains from diverse model cohorts
An arXiv preprint describes Co-RL, a multi-agent reinforcement-learning framework in which separate models reward one another. The authors report gains across text-only and multimodal benchmarks without ground-truth labels, while acknowledging the risks of self-reinforcing errors and training collapse.arxiv.org创新
A proposed fine-tuning method allocates training effort across related tasks
A new arXiv paper proposes Task Specialization Fine-Tuning to allocate limited fine-tuning across related task regions. It reports improved task coverage in optimization, control, and LLM experiments, but gives no numerical results or independent validation.arxiv.org创新
SURE-Ridge proposes a faster way to infer causal graphs from limited data
Accepted to the 2026 Asilomar Conference, a paper proposes SURE-Ridge, a closed-form method for causal-graph estimation in equal-variance linear Gaussian models. Authors report lower small-sample structural error and fastest tested-baseline runtime; the source does not establish performance beyond these assumptions.arxiv.org创新
J-Miner paper reports executable decision rules extracted from language-model classifiers
Researchers describe J-Miner, a method that converts internal signals from fine-tuned language-model classifiers into inspectable rules, reporting up to 98.3% reproduction of source decisions and nearly the same average task accuracy in much smaller student models.arxiv.org创新
Paper tests letting AI agents switch specialized LoRA adapters mid-task
A single-author arXiv paper reports that giving an agent a tool to switch between specialized LoRA adapters helped it solve two synthetic coding tasks and reduced the reported capability tax by up to 18 times.arxiv.org