论文

本文将数据安全形式化为 provenance monomials 上的聚合谓词，并提出 Passant——一个无需物化 provenance 即可强制执行 DFC 策略的可移植查询重写层。This paper formalizes data safety as aggregate predicates over provenance monomials and presents Passant, a portable query rewriting layer that enforces DFC policies without materializing provenance.

本工作提出 12 个具体指标，从一致性、鲁棒性、可预测性和安全性四个关键维度分解 Agent 可靠性，可与传统评估互补，并提供用于分析 Agent 表现、退化与失败方式的工具。This work proposes twelve concrete metrics that decompose agent reliability along four key dimensions: consistency, robustness, predictability, and safety, which complement traditional evaluations while offering tools for reasoning about how agents perform, degrade, and fail.

提出 Tatoxa，一种面向鞑靼语文本去毒的 SOTA 系统；对比实验表明，该方法在关键质量指标上优于现有开源及商用闭源 LLM。Tatoxa is presented, a novel state-of-the-art system for text detoxification in the Tatar language, and comparative experiments show that the proposed approach outperforms existing open source and proprietary commercial LLMs on key quality metrics.

Qwen-RobotManip 在所有 OOD 场景下大幅超越包括 π0.5 在内的已有 SOTA 模型，在 RoboChallenge 中排名第一，相对改进 20%，并在 AgileX ALOHA、Franka、UR、ARX 等真实机器人平台上完成验证。Qwen-RobotManip substantially outperforms prior state-of-the-art models, including $\pi$0.5, across all OOD settings, ranks 1st in RoboChallenge with a 20% relative improvement, and is validated on real-robot platforms including AgileX ALOHA, Franka, UR, and ARX.

实现了一个 RAG 框架，包含面向 LLM 的错误追踪与反例生成以提供更优上下文，并兼容最新版 Isabelle 与 Sledgehammer 以提升效率。A Retrieval-Augmented Generation framework, Error tracing and counterexample generation for improved context supplied to the Large Language Model, and Compatibility with the latest version of Isabelle and Sledgehammer is implemented for improved efficiency.

提出一种基于优化的方法，通过精炼现有内容排序来提升社交媒体信息流中新闻内容的可信度；同时构建一条鲁棒的半自动化流水线，基于检索增强打分与人工事实核查的混合方式为内容赋予可信度分数。An optimization-based method to improve the credibility of news content on social media feeds by refining existing content rankings is presented and a robust semi-automated pipeline for assigning credibility scores to content based on a mixture of retrieval-augmented score assignments and human-generated fact-checks is proposed.

结果表明稳定的循环深度需要计入参数访问次数（而非仅名义层数）的残差缩放规则；DeepLoop 在不存在物理块被重复访问时表现为中性，一旦启用循环深度则改善验证损失与下游准确率。The results show that stable recurrent depth requires residual scaling rules that account for parameter visits, not only nominal layer count, and DeepLoop is neutral when no physical block is revisited and improves validation loss and downstream accuracy once recurrent depth is activated.

提出一个模型，基于图像区域上的 CNN、句子上的双向 RNN 以及通过多模态嵌入对齐两种模态的结构化目标，生成图像及其区域的自然语言描述。A model that generates natural language descriptions of images and their regions based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding is presented.

训练后对齐往往较浅，会在微调中被侵蚀。而中训练干预能否在干净隔离于训练后的情况下产生持久对齐，此前未经检验。我们通过宪法式中训练来测试：在 120B 规模上，插入基于原则与价值观的内容，与仅做回放的对照组进行对比。我们基于 Anthropic 的 Constitution 构建了 394M token 的宪法语料，并采用 2×2 析因设计（课程顺序 × 审慎推理），形成四种宪法式中训练条件与一组对照，随后在自生成与既有...Post-training alignment is often shallow, eroding under fine-tuning. Whether midtraining interventions, cleanly isolated from post-training, can produce durable alignment remains untested. We test this via constitutional midtraining: inserting principled, values-based content into midtraining against a replay-only control at 120B scale. Our 394M-token constitutional corpus, built from Anthropic's Constitution, uses a 2x2 factorial design (curriculum ordering x deliberative reasoning) to produce four constitutionally midtrained conditions plus a control, evaluated on self-generated and establis

本文提出了不变风险最小化（IRM），一种用于在多个训练分布上估计不变相关性的学习范式，并展示了 IRM 学到的不变性如何与数据的因果结构相关，从而实现分布外泛化。This work introduces Invariant Risk Minimization, a learning paradigm to estimate invariant correlations across multiple training distributions and shows how the invariances learned by IRM relate to the causal structures governing the data and enable out-of-distribution generalization.