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.

提出 Verification-Aware Training，一种插件式框架，在每一步训练中模拟验证并将产生的 accept 与 reject 模式转化为监督信号，在数学、代码和聊天基准上提升了平均接受长度和实际加速比。Verification-Aware Training is introduced, a plug-in framework that simulates verification at every training step and turns the resulting accept and reject patterns into supervision and improves average acceptance length and wall-clock speedup across math, code, and chat benchmarks.

该工作展示了一个紧凑的移动机械手，复用其手指同时完成运动与交互，无需独立的运动机构。This work demonstrates a compact mobile manipulator that reuses its fingers for locomotion and interaction, without a separate locomotion mechanism.

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.

本文给出证据，说明 superposition 是 Transformer 架构的内在属性，而非训练过程中涌现的结果，并证明通过轻量级 fine-tuning 可以在很大程度上恢复线性性。This work provides evidence that superposition is an intrinsic property of the Transformer architecture rather than an emergent consequence of training, and demonstrates that linearity can be substantially restored through lightweight fine-tuning.

本文提出 ExpertAlign，一种无需域标签或独立 routing 模型的框架，可在每个 token 上对全体 teacher 池进行监督路由；研究表明 token 级路由能够利用跨域互补监督，并减少对 prompt 级域指派的单一依赖。This work proposes ExpertAlign, a framework that routes supervision over the full teacher pool at each token, without domain labels or training a separate routing model, and demonstrates token-level routing can exploit cross-domain complementary supervision, and reduce exclusive reliance on prompt-level domain assignment.

实验表明，屏蔽高熵偏移位置相比朴素的 SD 提升了分布外泛化能力，由此得到的自蒸馏 judges 在所评估的主观子类别上比基于结果监督 RL 训练的 judges 高出 2-9 个百分点，同时在客观子类别上保持竞争力。Experiments show that masking higher-entropy-shift positions improves out-of-distribution generalization over naive SD, and the resulting self-distilled judges outperform judges trained with outcome-supervised RL by 2-9 percentage points on the evaluated subjective subcategories, while remaining competitive on objective ones.

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.