论文

研究表明，机器学习在公共部门的成功，将更少依赖模型准确率的突破，而更多依赖机构能否构建出透明、可复现、可问责且受公民信任的数据基础设施。It is shown that the success of machine learning in the public sector will depend less on breakthroughs in model accuracy and more on the ability of institutions to engineer transparent, reproducible, and accountable data infrastructures that citizens can trust.

本文探讨 Paxos 与 Raft 何者更优地解决分布式共识问题，通过以 Raft 的术语与实用抽象描述一个简化的 Paxos 算法，精确揭示两者的差异。This paper considers the question of which algorithm, Paxos or Raft, is the better solution to distributed consensus to determine exactly how they differ by describing a simplified Paxos algorithm using Raft's terminology and pragmatic abstractions.

WARP：一个直接从已发布权重还原微调模型训练数据混合比例的框架，抽取几何特征并映射至各领域占比，可采用无参数 softmax 读出器，或基于合成混合训练的 MLP 投影器。WARP is introduced, a framework that recovers a fine-tuned model's training mixtures directly from its released weights and extracts geometric features and maps them to domain proportions using either a parameter-free softmax readout or an MLP projector trained on synthetic mixtures.

本文提出 Randomized YaRN，一种通过将基于 YaRN 的位置外推与随机位置编码和长度课程相结合来提升长度泛化能力的训练方法，表明渐进式地将模型暴露于分布外位置分布是实现可泛化长上下文推理的有效方案。Randomized YaRN is proposed, a training method that improves length generalization by combining YaRN-based positional extrapolation with randomized positional encoding and a length curriculum, and suggests that progressively exposing models to OOD positional distributions provides an effective recipe for generalizable long-context reasoning.

发现 ALiBi 的失效模式会显著损害 token 检索，而对标准 decoder 基准影响较小；提出四种训练时缓解策略，在 passkey 检索上获得最一致的提升。It is found that ALiBi's failure mode can substantially impair token retrieval while having only a minor effect on standard decoder benchmarks, and proposes four training-time mitigation strategies that yield the most consistent improvements in passkey retrieval.