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Russian speech synthesis presents distinctive challenges, including vowel reduction, consonant devoicing, variable stress patterns, homograph ambiguity, and unnatural intonation. This paper introduces Balalaika, a novel dataset comprising more than 2,000 hours of studio-quality Russian speech with comprehensive textual annotations, including punctuation and stress markings. Experimental results show that models trained on Balalaika significantly outperform those trained on existing datasets in both speech synthesis and enhancement tasks. We detail the dataset construction pipeline, annotation methodology, and results of comparative evaluations.

Diffusion-based large language models (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs, offering faster inference and greater interactivity via parallel decoding and bidirectional modeling. However, despite strong performance in code generation and text infilling, we identify a fundamental safety concern: existing alignment mechanisms fail to safeguard dLLMs against context-aware, masked-input adversarial prompts, exposing novel vulnerabilities. To this end, we present DIJA, the first systematic study and jailbreak attack framework that exploits unique safety weaknesses of dLLMs. Specifically, our proposed DIJA constructs adversarial interleaved mask-text prompts that exploit the text generation mechanisms of dLLMs, i.e., bidirectional modeling and parallel decoding. Bidirectional modeling drives the model to produce contextually consistent outputs for masked spans, even when harmful, while parallel decoding limits model dynamic filtering and rejection sampling of unsafe content. This causes standard alignment mechanisms to fail, enabling harmful completions in alignment-tuned dLLMs, even when harmful behaviors or unsafe instructions are directly exposed in the prompt. Through comprehensive experiments, we demonstrate that DIJA significantly outperforms existing jailbreak methods, exposing a previously overlooked threat surface in dLLM architectures. Notably, our method achieves up to 100% keyword-based ASR on Dream-Instruct, surpassing the strongest prior baseline, ReNeLLM, by up to 78.5% in evaluator-based ASR on JailbreakBench and by 37.7 points in StrongREJECT score, while requiring no rewriting or hiding of harmful content in the jailbreak prompt. Our findings underscore the urgent need for rethinking safety alignment in this emerging class of language models. Code is available at https://github.com/ZichenWen1/DIJA.

We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2, CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in SSL clustering methods. While modern models rely on assigning image features to large codebooks via clustering algorithms like Sinkhorn-Knopp, they fail to account for the inherent ambiguity in clustering semantics. To address this, we introduce a parameter-efficient, multi-head clustering projector based on nested Matryoshka representations. This design progressively refines features into increasingly fine-grained clusters without increasing the model size, enabling both performance and memory efficiency. Additionally, we propose a novel positional disentanglement strategy that explicitly removes positional biases from dense representations, thereby improving the encoding of semantic content. This leads to consistent gains on several downstream benchmarks, demonstrating the utility of cleaner feature spaces. Our contributions establish a new standard for transparent, high-performance vision models and open a path toward more reproducible and generalizable foundation models for the broader AI community. The code and model checkpoints are available at https://github.com/valeoai/Franca.

This paper focuses on monolithic Multimodal Large Language Models (MLLMs), which integrate visual encoding and language decoding into a single model. Existing structures and pre-training strategies for monolithic MLLMs often suffer from unstable optimization and catastrophic forgetting. To address these challenges, our key idea is to embed a new visual parameter space into a pre-trained LLM, enabling stable learning of visual knowledge from noisy data via delta tuning. Based on this principle, we first introduce Mono-InternVL, an advanced monolithic MLLM that incorporates a set of visual experts through a multimodal mixture-of-experts architecture. In addition, we design an innovative Endogenous Visual Pre-training (EViP) for Mono-InternVL to maximize its visual capabilities via progressive learning. Mono-InternVL achieves competitive performance against existing MLLMs but also leads to relatively expensive data cost. Therefore, we further present Mono-InternVL-1.5, a cheaper and stronger monolithic MLLM equipped with an improved EViP (EViP++). EViP++ introduces additional visual attention experts to Mono-InternVL-1.5 and re-organizes the pre-training process in an efficient manner. During inference, it includes a fused CUDA kernel to speed up its MoE operations. With these designs, Mono-InternVL-1.5 significantly reduces training and inference costs, while still maintaining competitive performance with Mono-InternVL. To evaluate our approach, we conduct extensive experiments across 15 benchmarks. Results demonstrate that Mono-InternVL outperforms existing monolithic MLLMs on 12 out of 15 benchmarks, e.g., +114-point improvement over Emu3 on OCRBench. Compared to its modular counterpart, i.e., InternVL-1.5, Mono-InternVL-1.5 achieves similar multimodal performance while reducing first-token latency by up to 69%. Code and models are released at https://github.com/OpenGVLab/Mono-InternVL.

Disentangling content and style from a single image, known as content-style decomposition (CSD), enables recontextualization of extracted content and stylization of extracted styles, offering greater creative flexibility in visual synthesis. While recent personalization methods have explored the decomposition of explicit content style, they remain tailored for diffusion models. Meanwhile, Visual Autoregressive Modeling (VAR) has emerged as a promising alternative with a next-scale prediction paradigm, achieving performance comparable to that of diffusion models. In this paper, we explore VAR as a generative framework for CSD, leveraging its scale-wise generation process for improved disentanglement. To this end, we propose CSD-VAR, a novel method that introduces three key innovations: (1) a scale-aware alternating optimization strategy that aligns content and style representation with their respective scales to enhance separation, (2) an SVD-based rectification method to mitigate content leakage into style representations, and (3) an Augmented Key-Value (K-V) memory enhancing content identity preservation. To benchmark this task, we introduce CSD-100, a dataset specifically designed for content-style decomposition, featuring diverse subjects rendered in various artistic styles. Experiments demonstrate that CSD-VAR outperforms prior approaches, achieving superior content preservation and stylization fidelity.

In the era of Large Language Models (LLMs), alignment has emerged as a fundamental yet challenging problem in the pursuit of more reliable, controllable, and capable machine intelligence. The recent success of reasoning models and conversational AI systems has underscored the critical role of reinforcement learning (RL) in enhancing these systems, driving increased research interest at the intersection of RL and LLM alignment. This paper provides a comprehensive review of recent advances in LLM alignment through the lens of inverse reinforcement learning (IRL), emphasizing the distinctions between RL techniques employed in LLM alignment and those in conventional RL tasks. In particular, we highlight the necessity of constructing neural reward models from human data and discuss the formal and practical implications of this paradigm shift. We begin by introducing fundamental concepts in RL to provide a foundation for readers unfamiliar with the field. We then examine recent advances in this research agenda, discussing key challenges and opportunities in conducting IRL for LLM alignment. Beyond methodological considerations, we explore practical aspects, including datasets, benchmarks, evaluation metrics, infrastructure, and computationally efficient training and inference techniques. Finally, we draw insights from the literature on sparse-reward RL to identify open questions and potential research directions. By synthesizing findings from diverse studies, we aim to provide a structured and critical overview of the field, highlight unresolved challenges, and outline promising future directions for improving LLM alignment through RL and IRL techniques.

Graphical User Interface (GUI) grounding maps natural language instructions to precise interface locations for autonomous interaction. Current reinforcement learning approaches use binary rewards that treat elements as hit-or-miss targets, creating sparse signals that ignore the continuous nature of spatial interactions. Motivated by human clicking behavior that naturally forms Gaussian distributions centered on target elements, we introduce GUI Gaussian Grounding Rewards (GUI-G^2), a principled reward framework that models GUI elements as continuous Gaussian distributions across the interface plane. GUI-G^2 incorporates two synergistic mechanisms: Gaussian point rewards model precise localization through exponentially decaying distributions centered on element centroids, while coverage rewards assess spatial alignment by measuring the overlap between predicted Gaussian distributions and target regions. To handle diverse element scales, we develop an adaptive variance mechanism that calibrates reward distributions based on element dimensions. This framework transforms GUI grounding from sparse binary classification to dense continuous optimization, where Gaussian distributions generate rich gradient signals that guide models toward optimal interaction positions. Extensive experiments across ScreenSpot, ScreenSpot-v2, and ScreenSpot-Pro benchmarks demonstrate that GUI-G^2, substantially outperforms state-of-the-art method UI-TARS-72B, with the most significant improvement of 24.7% on ScreenSpot-Pro. Our analysis reveals that continuous modeling provides superior robustness to interface variations and enhanced generalization to unseen layouts, establishing a new paradigm for spatial reasoning in GUI interaction tasks.

Large language models have recently evolved from fluent text generation to advanced reasoning across diverse domains, giving rise to reasoning language models. Among these domains, mathematical reasoning serves as a representative benchmark as it requires precise multi-step logic and abstract reasoning, which can be generalized to other tasks. While closed-source RLMs such as GPT-o3 demonstrate impressive reasoning capabilities, their proprietary nature limits transparency and reproducibility. Although many open-source projects aim to close this gap, most of them lack sufficient openness by omitting critical resources such as datasets and detailed training configurations, which hinders reproducibility. To contribute toward greater transparency in RLM development, we introduce the MiroMind-M1 series, a set of fully open-source RLMs built on the Qwen-2.5 backbone that match or exceed the performance of existing open-source RLMs. Specifically, our models are trained in two stages: SFT on a carefully curated corpus of 719K math-reasoning problems with verified CoT trajectories, followed by RLVR on 62K challenging and verifiable problems. To enhance the robustness and efficiency of the RLVR process, we introduce Context-Aware Multi-Stage Policy Optimization, an algorithm that integrates length-progressive training with an adaptive repetition penalty to encourage context-aware RL training. Our model achieves state-of-the-art or competitive performance and superior token efficiency among Qwen-2.5-based open-source 7B and 32B models on the AIME24, AIME25, and MATH benchmarks. To facilitate reproducibility, we release the complete stack: models (MiroMind-M1-SFT-7B, MiroMind-M1-RL-7B, MiroMind-M1-RL-32B); datasets (MiroMind-M1-SFT-719K, MiroMind-M1-RL-62K); and all training and evaluation configurations. We hope these resources will support further research and foster community advancement.

Recent advances in large reasoning models highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising method for enhancing AI's capabilities, particularly in solving complex logical tasks. However, it remains unclear whether RLVR truly expands a model's reasoning boundary or merely amplifies high-reward outputs that the base model already knows for improved precision. This study presents a theoretical and empirical investigation that provides fresh insights into the potential limits of RLVR. First, we offer a new theoretical perspective that RLVR is constrained by the base model's support-unable to sample solutions with zero initial probability-and operates as a conservative reweighting mechanism that may restrict the discovery of entirely original solutions. We also identify an entropy-reward tradeoff: while RLVR reliably enhances precision, it may progressively narrow exploration and potentially overlook correct yet underrepresented solutions. Extensive empirical experiments validate that while RLVR consistently improves pass@1, the shrinkage of empirical support generally outweighs the expansion of empirical support under larger sampling budgets, failing to recover correct answers that were previously accessible to the base model. Interestingly, we also observe that while RLVR sometimes increases token-level entropy, resulting in greater uncertainty at each generation step, answer-level entropy declines, indicating that these seemingly more uncertain paths ultimately converge onto a smaller set of distinct answers. Taken together, these findings reveal potential limits of RLVR in extending reasoning horizons. Breaking this invisible leash may require future algorithmic innovations such as explicit exploration mechanisms or hybrid strategies that seed probability mass into underrepresented solution regions.

Recent advances in generative modeling enable image editing assistants that follow natural language instructions without additional user input. Their supervised training requires millions of triplets: original image, instruction, edited image. Yet mining pixel-accurate examples is hard. Each edit must affect only prompt-specified regions, preserve stylistic coherence, respect physical plausibility, and retain visual appeal. The lack of robust automated edit-quality metrics hinders reliable automation at scale. We present an automated, modular pipeline that mines high-fidelity triplets across domains, resolutions, instruction complexities, and styles. Built on public generative models and running without human intervention, our system uses a task-tuned Gemini validator to score instruction adherence and aesthetics directly, removing any need for segmentation or grounding models. Inversion and compositional bootstrapping enlarge the mined set by approximately 2.2x, enabling large-scale high-fidelity training data. By automating the most repetitive annotation steps, the approach allows a new scale of training without human labeling effort. To democratize research in this resource-intensive area, we release NHR-Edit: an open dataset of 358k high-quality triplets. In the largest cross-dataset evaluation, it surpasses all public alternatives. We also release Bagel-NHR-Edit, an open-source fine-tuned Bagel model, which achieves state-of-the-art metrics in our experiments.

The advent of Large Language Model (LLM)-powered agents has revolutionized artificial intelligence by enabling solutions to complex, open-ended tasks through web-based information-seeking (IS) capabilities. The scarcity of high-quality training data has limited the development of IS agents. Existing approaches typically adopt an information-driven paradigm that first collects web data and then generates questions based on the retrieval. However, this may lead to inconsistency between information structure and reasoning structure, question and answer. To mitigate, we propose a formalization-driven IS data synthesis framework WebShaper to construct a dataset. WebShaper systematically formalizes IS tasks through set theory. Central to the formalization is the concept of Knowledge Projections (KP), which enables precise control over reasoning structure by KP operation compositions. During synthesis, we begin by creating seed tasks, then use a multi-step expansion process. At each step, an agentic Expander expands the current formal question more complex with retrieval and validation tools based on our formalization. We train our model on the synthesized dataset. Experiment results demonstrate that WebShaper achieves state-of-the-art performance among open-sourced IS agents on GAIA and WebWalkerQA benchmarks.

Recent advances in 3D neural representations and instance-level editing models have enabled the efficient creation of high-quality 3D content. However, achieving precise local 3D edits remains challenging, especially for Gaussian Splatting, due to inconsistent multi-view 2D part segmentations and inherently ambiguous nature of Score Distillation Sampling (SDS) loss. To address these limitations, we propose RoMaP, a novel local 3D Gaussian editing framework that enables precise and drastic part-level modifications. First, we introduce a robust 3D mask generation module with our 3D-Geometry Aware Label Prediction (3D-GALP), which uses spherical harmonics (SH) coefficients to model view-dependent label variations and soft-label property, yielding accurate and consistent part segmentations across viewpoints. Second, we propose a regularized SDS loss that combines the standard SDS loss with additional regularizers. In particular, an L1 anchor loss is introduced via our Scheduled Latent Mixing and Part (SLaMP) editing method, which generates high-quality part-edited 2D images and confines modifications only to the target region while preserving contextual coherence. Additional regularizers, such as Gaussian prior removal, further improve flexibility by allowing changes beyond the existing context, and robust 3D masking prevents unintended edits. Experimental results demonstrate that our RoMaP achieves state-of-the-art local 3D editing on both reconstructed and generated Gaussian scenes and objects qualitatively and quantitatively, making it possible for more robust and flexible part-level 3D Gaussian editing. Code is available at https://janeyeon.github.io/romap.

Debian 发布团队宣布 Debian 13.0 "Trixie"计划于 8 月 9 日释出，7 月 27 日完全冻结。Debian Trixie 代表了为期两年的开发历程，内核采用 Linux 6.12 LTS，包含桌面环境 GNOME 48 、GCC 14.2 编译器、Python 3.13 等大量软件更新。Debian Trixie 将首次正式支持 64 位 RISC-V 架构。

对数据库 Dimensions 的分析发现，与 AI 相关的研究论文数量已从 2000 年的不到 8500 篇增长到 2024 年的 5.7 万多篇。2000 年，中国学者仅发表了 671 篇 AI 论文，但到 2024 年，他们发表了 23695 篇与 AI 相关的论文，超过了美国（6378篇）、英国（2747篇）和欧盟（10055篇）的总和。中国产生的海量AI论文也推动了创纪录的专利申请。2024 年中国研究人员提交了 35423 项与 AI 相关的专利申请，是美国、英国、加拿大、日本、韩国5国提交的专利申请总数（2678项）的 13 倍多。研究还显示，中国的 AI研 究正变得越来越独立。过去几年中，美国、英国和欧盟的科学家与中国学者共同撰写论文的频率比他们彼此间合著的频率更高。但在 4 个地区中，中国学者的国际合作率最低。随着中国庞大的 AI 研究队伍的成熟，国际合作可能会进一步减少。研究发现，中国拥有约 3 万名各个年龄段的 AI 研究人员，而美国约有 1 万名。中国的 AI 研究队伍也明显更年轻。

国际研究团队首次确定了太阳以外的恒星周围开始形成行星的时刻，这是人类首次观察到行星系统形成的早期阶段，并为我们探索自身太阳系的起源提供全新视角。这颗诞生中的行星系统围绕着一颗名为 HOPS 315 的原恒星运转，HOPS 315 距离我们约 1,300 光年，与新生的太阳类似。在太阳系中，最早在地球目前绕太阳位置附近凝结的固体物质被发现藏在古老的陨石中。天文学家对这些原始岩石进行年代测定，以确定太阳系形成的起始时间。这些陨石富含一氧化硅(SiO) 的晶体矿物，可以在年轻行星盘的极高温度下凝结。随着时间的推移，这些新凝结的固体会结合在一起，随着它们的体积和质量的增加，为行星的形成播下了种子。太阳系中第一批几千米大小的行星，最终发展成像地球或木星核心这样的行星，正是在这些晶体矿物凝结后形成的。天文学家在新的发现中，找到了这些热矿物在 HOPS-315 周围的圆盘中开始凝结的证据。研究结果显示，SiO 以气态存在于这颗宝宝恒星周围，也存在于这些结晶矿物中，这表示它才刚开始凝固。研究人员表示这个过程从未在原行星盘，甚至在我们太阳系以外的任何地方出现过。

在被发现使用中国工程师为五角大楼的云计算系统提供技术支持之后，微软周五表示已经调整了安排，确保不会有中国的工程师团队为国防部的政府云以及相关服务提供技术支持。在这之前，国防部长 Pete Hegseth 表示将对此展开调查。中国工程师的技术支持受到了持有安全许可的美国公民的监督，但调查发现执行监督任务的美国公民缺乏专业能力去理解外国工程师的工作。

流媒体巨头 Netflix 表示首次在原创剧集中首次使用生成式 AI 制作了视觉特效。联席 CEO Ted Sarandos 称阿根廷科幻剧《The Eternaut》使用生成式 AI 制作了一段布宜诺斯艾利斯建筑物倒塌的镜头，速度比使用传统特效工具快了 10倍 。他表示 生成式 AI 技术让预算有限的制作团队更快更低成本的完成特效镜头。新加坡动画工作室 CraveFX 的联合创始人 Davier Yoon 认为影视剧公司使用生成式 AI 只是时间问题，AI 让小型工作室也能制作看起来庞大预算的视觉效果。他称，决定最终图像的是艺术家而不是 AI。

开源办公软件项目 LibreOffice 指责微软故意使用不必要复杂的文件格式，通过 Microsoft 365 文档锁定用户。LibreOffice 的文档使用开放标准格式 OpenDocument Format(ODF)，该文档格式不受任何公司控制。微软则使用非标准的 Office Open XML(OOXML)文档格式。LibreOffice 称，微软的 OOXML 格式包含深度嵌套的结构，使用非直观的命名约定和大量可选元素，使得非微软开发商难以实现。LibreOffice 将文档格式的这种情况与铁路系统进行了对比，铁路轨道是公有的，但控制系统过于复杂以至于竞争对手无法制造兼容的列车。

Arch Linux 项目发出安全警告，7 月 16 日晚 8 点左右（UTC+2）一个恶意软件包上传到了 Arch User Repository(AUR)，几个小时后同一位用户又上传了两个恶意包，这些软件包会安装来自同一个 GitHub 库的脚本，该脚本被识别为 RAT（远程访问木马）。三个恶意软件包都与 Firefox 或其分支相关： librewolf-fix-bin，firefox-patch-bin，zen-browser-patched-bin。开发者建议如果安装了这些恶意软件包，立即清除并采取安全防御措施。Reddit 用户随后报告了更多的恶意 AUR 包，相关软件包在被举报之后迅速移除了。

OpenAI 本应在上周发布自 GPT-2 以来首个开放权重模型，但 CEO Sam Altman 以安全审查的理由推迟了发布。美国迄今发布的性能最出色的开放模型是 Meta 的 Llama 4，除此之外微软发布了 Phi-4 14B，Google 发布了最多 270 亿参数的多模态模型 Gemma3。相比之下，中国的大模型明显比美国更为开放性能也更为出色。DeepSeek 发布了有 6710 亿个参数的 R1 模型；阿里巴巴发布了一系列通义千问模型 QwQ、Qwen3-235B-A22B 和 30B-A3B；MiniMax 在 Apache 2.0 下发布了有 4560 亿个参数的推理模型 M1，其上下文窗口一百万 token；百度开源了参数规模 470 亿到 4240 亿的文心模型；华为开源了盘古模型；北京月之暗面发布了 1 万亿参数的 Kimi 2 模型。美国最先进的模型都是闭源私有的，而中国最先进的模型开放了权重，公开了技术文档等细节。

加拿大公民 Harald Herchen 因涉嫌在台湾太鲁阁国家公园谋杀妻子、加州山景城教师 Alice Ku 而被判向其父母赔偿 2360 万美元。两人是在 2017 年 10 月秘密结婚，2019 年 11 月 29 日 Alice Ku 在游玩国家公园后失踪。Herchen 声称失踪前他送妻子去了火车站，但手机信号塔数据显示他们的手机直接回到了酒店。Herchen 还声称 Alice Ku 与年轻英俊的导游私奔了。她的一封电邮似乎可以证明他是清白的。但根据向 Google 发去的传票，Google 提供的 IP 证据显示这封邮件是从 Herchen 下榻酒店的 WiFi 发送出去的。Herchen 在旅游期间还发生了手部骨折，他对骨折的原因不同时间给出了不同的说法。陪审团裁决他需要对其妻子的死亡负责。由于谋杀发生在台湾，与美国没有引渡协议，因此美国无法起诉他，只能施加罚款。

2019 年离婚、自新冠疫情以来基本上一直住在游轮上的 Valve 联合创始人 Gabe Newell 罕见的接受了一位 YouTube 主播 Zalkar Saliev 的采访，谈论了他的个人生活。62 岁的 Newell 称他在卧室工作一周工作七天。他说，自己起床、工作，潜水，然后再工作，再潜水或去健身，然后再继续工作。他说自己不是被迫加班，而是做自己感觉有趣的东西。他的工作内容包括了 AI、Steam、研究气溶胶病原体检测装置、脑机接口等等。他控制了 Valve 50.1% 的股份，净资产大约 100 亿美元。

KDE 项目官方博客宣布了 KDE Plasma 6.5 的一项重大视觉更新：窗口支持圆角了。Plasma 6.5 预计将于 2025 年 10 月 21 日释出。开发者称，窗口圆角是用户期盼已久的功能，甚至社区有第三方插件 kde-rounded-corners 提供圆角支持。官方支持意味着对第三方方案需求的减少。在 Plasma 6.5 中，窗口圆角将默认启用，但为喜欢旧外观的用户提供了一个选项。

一项旨在预防线粒体 DNA 疾病遗传的开创性体外受精技术（IVF）——原核移植，已成功帮助 8 名婴儿健康出生。这些婴儿共有四男四女，其中一对为同卵双胞胎。他们由 7 名携带高风险线粒体 DNA 突变的女性所生，但均未表现出任何线粒体疾病迹象。该技术通过将母亲受精卵中的核 DNA，转移到一个健康捐赠者去核的卵子中，从而避免将母亲线粒体中的致病突变遗传给下一代。由此产生的胚胎，携带了父母的核 DNA 和捐赠者的线粒体DNA，因此被称为“三亲婴儿”。线粒体疾病，由线粒体中的基因突变引发，可能导致肌肉无力、癫痫、发育迟缓、器官衰竭乃至死亡。尽管常规体外受精检测可识别多数突变，但很多时候依然存在不确定性。这是“三亲婴儿”相关技术得以出现的原因。