Weekly Digest — 2025-W34

Beta release of Archon OS - the knowledge and task management backbone for AI coding assistants.

LLM agents built for control. Designed for real-world use. Deployed in minutes.

This is a repo with links to everything you'd ever want to learn about data engineering

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

Supercharge your workflow automation with this curated collection of n8n templates! Instantly connect your favorite apps-like Gmail, Telegram, Google Drive, Slack, and more-with ready-to-use, AI-powered automations. Save time, boost productivity, and unlock the true potential of n8n in just a few clicks.

PixiEditor is a Universal Editor for all your 2D needs

Supercharge Your LLM with the Fastest KV Cache Layer

BillionMail gives you open-source MailServer, NewsLetter, Email Marketing — fully self-hosted, dev-friendly, and free from monthly fees. Join the discord: https://discord.gg/asfXzBUhZr

Financial data platform for analysts, quants and AI agents.

Sim is an open-source AI agent workflow builder. Sim Studio's interface is a lightweight, intuitive way to quickly build and deploy LLMs that connect with your favorite tools.

💖🧸 Self hosted, you owned Grok Companion, a container of souls of waifu, cyber livings to bring them into our worlds, wishing to achieve Neuro-sama's altitude. Capable of realtime voice chat, Minecraft, Factorio playing. Web / macOS / Windows supported.

JavaScript API for Chrome and Firefox

Stormy — AI agent for influencer marketing

Deeply understand yourself and every relationship

Build full-stack applications with Dualite - securely

Open-source stack for industrial-grade LLM applications

Generate sample files of various types and sizes in seconds.

Deep code research, straight from Slack or your browser

Reach Inbox Zero by speaking with your email & calendar

Curiosity Lens: Your Visual Agent

First Music API trained on licensed data, commercial-ready

Production grade vibe coding

Your Product Hunt Filter

Easily transcribe & translate lectures and meetings

We investigate the potential of large language models (LLMs) to serve as efficient simulators for agentic search tasks in reinforcement learning (RL), thereby reducing dependence on costly interactions with external search engines. To this end, we first quantify the intrinsic search capability of LLMs via structured prompting and repeated sampling, which we term Self-Search. Our results reveal that LLMs exhibit strong scaling behavior with respect to the inference budget, achieving high pass@k on question-answering benchmarks, including the challenging BrowseComp task. Building on these observations, we introduce Self-Search RL (SSRL), which enhances LLMs' Self-Search capability through format-based and rule-based rewards. SSRL enables models to iteratively refine their knowledge utilization internally, without requiring access to external tools. Empirical evaluations demonstrate that SSRL-trained policy models provide a cost-effective and stable environment for search-driven RL training, reducing reliance on external search engines and facilitating robust sim-to-real transfer. We draw the following conclusions: 1) LLMs possess world knowledge that can be effectively elicited to achieve high performance; 2) SSRL demonstrates the potential of leveraging internal knowledge to reduce hallucination; 3) SSRL-trained models integrate seamlessly with external search engines without additional effort. Our findings highlight the potential of LLMs to support more scalable RL agent training.

Following OpenAI's introduction of the ``thinking with images'' concept, recent efforts have explored stimulating the use of visual information in the reasoning process to enhance model performance in perception and reasoning tasks. However, to the best of our knowledge, no open-source work currently offers a feature set as rich as proprietary models (O3), which can perform diverse image manipulations and simultaneously enhance logical reasoning capabilities through code. In this paper, we make a preliminary attempt in this direction by introducing Thyme (Think Beyond Images), a novel paradigm for enabling MLLMs to transcend existing ``think with images'' approaches by autonomously generating and executing diverse image processing and computational operations via executable code. This approach not only facilitates a rich, on-the-fly set of image manipulations (e.g., cropping, rotation, contrast enhancement) but also allows for mathematical computations, all while maintaining high autonomy in deciding when and how to apply these operations. We activate this capability through a two-stage training strategy: an initial SFT on a curated dataset of 500K samples to teach code generation, followed by a RL phase to refine decision-making. For the RL stage, we manually collect and design high-resolution question-answer pairs to increase the learning difficulty, and we propose GRPO-ATS (Group Relative Policy Optimization with Adaptive Temperature Sampling), an algorithm that applies distinct temperatures to text and code generation to balance reasoning exploration with code execution precision. We conduct extensive experimental analysis and ablation studies. Comprehensive evaluations on nearly 20 benchmarks show that Thyme yields significant and consistent performance gains, particularly in challenging high-resolution perception and complex reasoning tasks.

Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm. This technical report introduces DINOv3, a major milestone toward realizing this vision by leveraging simple yet effective strategies. First, we leverage the benefit of scaling both dataset and model size by careful data preparation, design, and optimization. Second, we introduce a new method called Gram anchoring, which effectively addresses the known yet unsolved issue of dense feature maps degrading during long training schedules. Finally, we apply post-hoc strategies that further enhance our models' flexibility with respect to resolution, model size, and alignment with text. As a result, we present a versatile vision foundation model that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. We also share the DINOv3 suite of vision models, designed to advance the state of the art on a wide spectrum of tasks and data by providing scalable solutions for diverse resource constraints and deployment scenarios.

Recent advances in large language model (LLM) pretraining have shown that simply scaling data quantity eventually leads to diminishing returns, hitting a data wall. In response, the use of synthetic data for pretraining has emerged as a promising paradigm for pushing the frontier of performance. Despite this, the factors affecting synthetic data quality remain poorly understood. In this work, we introduce BeyondWeb, a synthetic data generation framework that produces high-quality synthetic data for pretraining. BeyondWeb significantly extends the capabilities of traditional web-scale datasets, outperforming state-of-the-art synthetic pretraining datasets such as Cosmopedia and Nemotron-CC's high-quality synthetic subset (Nemotron-Synth) by up to 5.1 percentage points (pp) and 2.6pp, respectively, when averaged across a suite of 14 benchmark evaluations. It delivers up to 7.7x faster training than open web data and 2.7x faster than Nemotron-Synth. Remarkably, a 3B model trained for 180B tokens on BeyondWeb outperforms an 8B model trained for the same token budget on Cosmopedia. We also present several insights from BeyondWeb on synthetic data for pretraining: what drives its benefits, which data to rephrase and how, and the impact of model size and family on data quality. Overall, our work shows that there's no silver bullet for generating high-quality synthetic pretraining data. The best outcomes require jointly optimizing many factors, a challenging task that requires rigorous science and practical expertise. Naive approaches can yield modest improvements, potentially at great cost, while well-executed methods can yield transformative improvements, as exemplified by BeyondWeb.

Paper search is an important activity for researchers, typically involving using a query with description of a topic to find relevant papers. As research deepens, paper search requirements may become more flexible, sometimes involving specific details such as module configuration rather than being limited to coarse-grained topics. However, previous paper search systems are unable to meet these flexible-grained requirements, as these systems mainly collect paper abstracts to construct index of corpus, which lack detailed information to support retrieval by finer-grained queries. In this work, we propose PaperRegister, consisted of offline hierarchical indexing and online adaptive retrieval, transforming traditional abstract-based index into hierarchical index tree for paper search, thereby supporting queries at flexible granularity. Experiments on paper search tasks across a range of granularity demonstrate that PaperRegister achieves the state-of-the-art performance, and particularly excels in fine-grained scenarios, highlighting the good potential as an effective solution for flexible-grained paper search in real-world applications. Code for this work is in https://github.com/Li-Z-Q/PaperRegister.

Although LLM inference has emerged as a critical workload for many downstream applications, efficiently inferring LLMs is challenging due to the substantial memory footprint and bandwidth requirements. In parallel, compute capabilities have steadily outpaced both memory capacity and bandwidth over the last few decades, a trend that remains evident in modern GPU hardware and exacerbates the challenge of LLM inference. As such, new algorithms are emerging that trade increased computation for reduced memory operations. To that end, we present XQuant, which takes advantage of this trend, enabling an order-of-magnitude reduction in memory consumption through low-bit quantization with substantial accuracy benefits relative to state-of-the-art KV cache quantization methods. We accomplish this by quantizing and caching the layer input activations X, instead of using standard KV caching, and then rematerializing the Keys and Values on-the-fly during inference. This results in an immediate 2times memory savings compared to KV caching. By applying XQuant, we achieve up to sim 7.7times memory savings with <0.1 perplexity degradation compared to the FP16 baseline. Furthermore, our approach leverages the fact that X values are similar across layers. Building on this observation, we introduce XQuant-CL, which exploits the cross-layer similarity in the X embeddings for extreme compression. Across different models, XQuant-CL attains up to 10times memory savings relative to the FP16 baseline with only 0.01 perplexity degradation, and 12.5times memory savings with only 0.1 perplexity degradation. XQuant exploits the rapidly increasing compute capabilities of hardware platforms to eliminate the memory bottleneck, while surpassing state-of-the-art KV cache quantization methods and achieving near-FP16 accuracy across a wide range of models.

We present Ovis2.5, a successor to Ovis2 designed for native-resolution visual perception and strong multimodal reasoning. Ovis2.5 integrates a native-resolution vision transformer that processes images at their native, variable resolutions, avoiding the degradation from fixed-resolution tiling and preserving both fine detail and global layout -- crucial for visually dense content like complex charts. To strengthen reasoning, we train the model to move beyond linear chain-of-thought and perform reflection -- including self-checking and revision. This advanced capability is exposed as an optional "thinking mode" at inference time, allowing users to trade latency for enhanced accuracy on difficult inputs. The model is trained via a comprehensive five-phase curriculum that progressively builds its skills. The process begins with foundational visual and multimodal pretraining, advances through large-scale instruction tuning, and culminates in alignment and reasoning enhancement using DPO and GRPO. To scale these upgrades efficiently, we employ multimodal data packing and hybrid parallelism, yielding a significant end-to-end speedup. We release two open-source models: Ovis2.5-9B and Ovis2.5-2B. The latter continues the "small model, big performance" philosophy of Ovis2, making it ideal for resource-constrained, on-device scenarios. On the OpenCompass multimodal leaderboard, Ovis2.5-9B averages 78.3, marking a substantial improvement over its predecessor, Ovis2-8B, and achieving state-of-the-art results among open-source MLLMs in the sub-40B parameter range; Ovis2.5-2B scores 73.9, establishing SOTA for its size. Beyond aggregate scores, Ovis2.5 achieves leading results on STEM benchmarks, exhibits strong capabilities on grounding and video tasks, and achieves open-source SOTA at its scale for complex chart analysis.

Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters and entities. Given the LLM's diminished reasoning over extended context and high computational cost, retrieval-based approaches remain a pivotal role in practice. However, traditional RAG methods can fall short due to their stateless, single-step retrieval process, which often overlooks the dynamic nature of capturing interconnected relations within long-range context. In this work, we propose ComoRAG, holding the principle that narrative reasoning is not a one-shot process, but a dynamic, evolving interplay between new evidence acquisition and past knowledge consolidation, analogous to human cognition when reasoning with memory-related signals in the brain. Specifically, when encountering a reasoning impasse, ComoRAG undergoes iterative reasoning cycles while interacting with a dynamic memory workspace. In each cycle, it generates probing queries to devise new exploratory paths, then integrates the retrieved evidence of new aspects into a global memory pool, thereby supporting the emergence of a coherent context for the query resolution. Across four challenging long-context narrative benchmarks (200K+ tokens), ComoRAG outperforms strong RAG baselines with consistent relative gains up to 11% compared to the strongest baseline. Further analysis reveals that ComoRAG is particularly advantageous for complex queries requiring global comprehension, offering a principled, cognitively motivated paradigm for retrieval-based long context comprehension towards stateful reasoning. Our code is publicly released at https://github.com/EternityJune25/ComoRAG

We present 4DNeX, the first feed-forward framework for generating 4D (i.e., dynamic 3D) scene representations from a single image. In contrast to existing methods that rely on computationally intensive optimization or require multi-frame video inputs, 4DNeX enables efficient, end-to-end image-to-4D generation by fine-tuning a pretrained video diffusion model. Specifically, 1) to alleviate the scarcity of 4D data, we construct 4DNeX-10M, a large-scale dataset with high-quality 4D annotations generated using advanced reconstruction approaches. 2) we introduce a unified 6D video representation that jointly models RGB and XYZ sequences, facilitating structured learning of both appearance and geometry. 3) we propose a set of simple yet effective adaptation strategies to repurpose pretrained video diffusion models for 4D modeling. 4DNeX produces high-quality dynamic point clouds that enable novel-view video synthesis. Extensive experiments demonstrate that 4DNeX outperforms existing 4D generation methods in efficiency and generalizability, offering a scalable solution for image-to-4D modeling and laying the foundation for generative 4D world models that simulate dynamic scene evolution.

We propose a novel approach to image generation by decomposing an image into a structured sequence, where each element in the sequence shares the same spatial resolution but differs in the number of unique tokens used, capturing different level of visual granularity. Image generation is carried out through our newly introduced Next Visual Granularity (NVG) generation framework, which generates a visual granularity sequence beginning from an empty image and progressively refines it, from global layout to fine details, in a structured manner. This iterative process encodes a hierarchical, layered representation that offers fine-grained control over the generation process across multiple granularity levels. We train a series of NVG models for class-conditional image generation on the ImageNet dataset and observe clear scaling behavior. Compared to the VAR series, NVG consistently outperforms it in terms of FID scores (3.30 -> 3.03, 2.57 ->2.44, 2.09 -> 2.06). We also conduct extensive analysis to showcase the capability and potential of the NVG framework. Our code and models will be released.

Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer models, as the foundation of modern LLMs, offer a strong baseline with excellent scaling properties. However, the traditional transformer architecture requires substantial computations and poses significant obstacles for large-scale training and practical deployment. In this survey, we offer a systematic examination of innovative LLM architectures that address the inherent limitations of transformers and boost the efficiency. Starting from language modeling, this survey covers the background and technical details of linear and sparse sequence modeling methods, efficient full attention variants, sparse mixture-of-experts, hybrid model architectures incorporating the above techniques, and emerging diffusion LLMs. Additionally, we discuss applications of these techniques to other modalities and consider their wider implications for developing scalable, resource-aware foundation models. By grouping recent studies into the above category, this survey presents a blueprint of modern efficient LLM architectures, and we hope this could help motivate future research toward more efficient, versatile AI systems.

Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting. In this work, we present the first systematic evaluation of 5 methods for improving prompt robustness within a unified experimental framework. We benchmark these techniques on 8 models from Llama, Qwen and Gemma families across 52 tasks from Natural Instructions dataset. Our evaluation covers robustness methods from both fine-tuned and in-context learning paradigms, and tests their generalization against multiple types of distribution shifts. Finally, we extend our analysis to GPT-4.1 and DeepSeek V3 to assess frontier models' current robustness to format perturbations. Our findings offer actionable insights into the relative effectiveness of these robustness methods, enabling practitioners to make informed decisions when aiming for stable and reliable LLM performance in real-world applications. Code: https://github.com/AIRI-Institute/when-punctuation-matters.

根据发表在《Metabolism》期刊上的一项研究，韩国大学医学院的研究人员报告名为 ReHMGB1 的蛋白质在人体血液中传递衰老信号。ReHMGB1 代表 Reduced High Motion Group Box 1，会触发细胞衰老，永久性丧失其功能。它不仅会在局部发生，还会通过血液循环向全身传递破坏信号，尤其是在损伤或疾病的刺激下。对小鼠研究发现，如果阻断 ReHMGB1 的信号传递，肌肉受损的小鼠会显著加快肌肉再生速度，改善身体机能，减少细胞衰老迹象，减轻全身炎症。ReHMGB1 也具有有益功能，警告人体某个身体组织受损需要修复。

在西雅图举办的第 83 届世界科幻大会宣布了 2025 年雨果奖获奖名单： 最佳长篇小说：Robert Jackson Bennett 的《The Tainted Cup》，《Shadow of the Leviathan》系列第一部，故事发生在一个被海墙环绕的帝国 Khanum，每逢雨季巨兽利维坦会出现，然后被击退，帝国公民需要时刻关注海墙的缺口，故事始于调查一起谋杀案；  最佳中长篇小说：Ray Nayle 的《The Tusks of Extinction》；  最佳中短篇小说：Naomi Kritzer 的《The Four Sisters Overlooking the Sea》； 最佳短篇小说：Nghi Vo 的《Stitched to Skin Like Family Is》： 最佳系列小说：Rebecca Roanhorse 的《Between Earth and Sky》系列；  最佳科幻电视剧：《星际迷航：下层舰员》第五季第 10 集《The New Next Generation》； 最佳电影：《沙丘：第二部》： 最佳游戏：《卡德洞窟（Caves of Qud）》（龙腾世纪4、塞尔达传说和 1000xRESIST 等入围）。

FFmpeg 的开发迁移到了使用 Forgejo 的自托管平台 code.ffmpeg.org。Forgejo 是 Gitea 的分支，是一个类似 GitHub 的 Git 软件开发和版本控制平台，支持 bug 跟踪、Wiki 和代码审查等功能。迁移到 Forgejo 的一个重要原因是随着越来越多的开发者参与 FFmpeg 项目，现有的基于邮件列表的开发模式已经无法满足需求。短时间内邮件列表仍然继续使用，但随着时间的推移将会逐渐减少使用。

克罗地亚将其数字游民签证有效期从一年延长至三年，允许非欧盟居民及其近亲在该国远程工作和生活。数字游民签证是一种短期签证，大部分国家的数字游民签证有效期是六个月到一年。克罗地亚更新了它的政策，允许最长三年，而且允许亲密的家庭成员加入，所谓亲密家庭成员指的是同居三年以上但无子女的伴侣，或同居短于三年有子女的伴侣。当地官员表示此举旨在吸引更多人才前来该国工作和生活。克罗地亚的生活成本较低，但仍需改善其网络基础设施。

数据显示，今年前七个月电动汽车销量逾 1070 万辆，同比增长 27%。其中中国销量 650 万辆同比增长 29%， 欧洲 230 万辆增长 30%，北美 100 万辆增长 2%，其它地区总销量 90 万辆增长 42%。欧洲地区的德国、英国和意大利销量都有强劲增长，欧洲地区的纯电增长 30% 插电混动增长 32%。美国的电动汽车市场则因为政策阻力而销售疲软。

知名开源虚拟终端 PuTTY 官网网址非常长——www.chiark.greenend.org.uk/~sgtatham/putty。开发者曾经在官网 FAQ 中自信的表示，用户不会找错 PuTTY 官方地址，因为用户在 Google 中搜索 PuTTY 第一个结果就是它。然而今天第一个结果是 www.putty.org——由一位反疫苗者运营的第三方网站，他因为疫情而开始热衷传播虚假信息。此事在开源社区引发了激烈的讨论，最终开发团队于 8 月 14 日宣布注册了一个更简单易记的域名——putty.software。

去年初震惊整个开源和网络安全社区的 XZ 后门事件并没有离我们而去。在 XZ 事件中，攻击者 Jia Tan（化名，未必是华人）潜伏 XZ Utils 项目长达两年多时间，最终获得信任成为项目的共同维护者，之后他或他们利用其权限悄悄在 xz-utils 包中植入了一个复杂的后门。在恶意版本大规模传播前，后门就被发现了，因此没有造成大问题。但 Binarly REsearch 的调查发现，在攻击期间构建的部分 Docker 镜像仍然包含有 XZ Utils 后门。安全研究人员从 DockerHub 上发现了超过 35 个含有后门的镜像。虽然数字不多，但研究人员只扫描了一小部分镜像，而且只针对 Debian 发行版，其它发行版如 Fedora 和 OpenSUSE 情况未知。

英国芯片设计公司 Arm 的大股东日本软银集团向陷入困境的英特尔公司投资 20 亿美元，入股芯片巨人。与此同时，美国政府也在与英特尔公司谈判入股 10%。软银将以每股 23 美元的价格收购英特尔的普通股，将持有约 2% 的英特尔股份。软银今年二季度是四年来首次盈利，英特尔现任 CEO 陈立武曾是软银董事。英特尔曾是最强大的半导体公司，如今已经落后英伟达和 AMD 等公司。它目前正在重组裁员。

MIT 发表报告《The GenAI Divide: State of AI in Business 2025》称，95% 的企业生成式 AI 试验失败了。虽然企业纷纷整合大模型，但只有 5% 的 AI 试点项目实现了收入的快速增长，大多数项目停滞，对损益表几乎没有产生可衡量的影响。研究基于对 150 名高管的访谈，350 名员工的调查以及对 300 个公开的 AI 部署项目的分析。报告主要作者 Aditya Challapally 解释说，95% 的企业部署生成式 AI 表现不佳不是因为大模型的质量，而是因为 ChatGPT 之类的通用工具因其灵活性对个人用户非常有用，但它们无法从工作流程中学习或适应工作流程，因此企业部署停滞不前。逾半数的生成式 AI 预算是投入在销售和营销工具上面，但研究显示后台业务自动化投资回报率最高——在后台部署 AI 有助于消除业务流程外包、削减外部营力成本和简化运营。

在美国登月计划受挫之际，中国的登月计划过去几个月则取得了显著进展，中国有可能在美国之前登陆月球。中国载人航天工程办公室于 8 月 6 日成功测试了 26 吨的揽月着陆器高保真模型。揽月月面着陆器是登月舱和推进舱组成，主要用于环月轨道和月球表面间的航天员运输，可搭载 2 名航天员往返，中国国家航天局在声明中再次确认计划在 2030 年前登月。中国还在上周完成了新一代载人运载火箭长征十号的首次系留点火试验，6 月份完成了梦舟载人飞船零高度逃逸试验。

英格兰儿童事务专员 Rachel de Souza 表示，政府需要阻止儿童使用 VPN 绕过色情网站的年龄验证。她表示该漏洞需要堵上，呼吁 VPN 服务也要验证年龄。在 PornHub、Reddit 和 X 等网站开始对访问成人内容的英国用户验证年龄后，VPN 成为英国苹果 App Store 下载量最多的应用。对于限制 VPN 的评论，英国科学、创新和科技部发言人回应称，VPN 是成人使用的合法工具，目前没有禁止的计划。但如果平台故意向儿童推销 VPN 作为绕过年龄验证方法，将面临严厉执法和巨额罚款。

天文学家利用 ALMA 观测 12P/Pons-Brooks 彗星，发现其中的水具有与地球海洋几乎相同的同位素组成比例。这一发现进一步支持了「彗星可能在年轻地球上带来水与部分生命所需分子原料」的假说。一般认为，地球上的水在数十亿年前由彗星、小行星及陨石撞击带来。然而，过去观测多数彗星的水同位素比例与地球差异明显。本次结果则提供了迄今最有力的证据，显示至少部分彗星携带的水与地球水有相同的化学「指纹」。在这项研究中，科学家首次绘制出 12P 彗星的彗发（包围彗核的气体）中普通水（H₂O）与重水（HDO，即水分子里的一个氢被带有额外中子的氘取代）的空间分布。观测时间点为 12P 彗星接近太阳之际，结合毫米／次毫米波与红外线数据，精确测定了氘氢比（D/H）。透过绘制 H₂O 与 HDO 的分布，研究团队判断这些气体确实来自彗核内的冰冻物质，而非在彗发中由化学作用或其他过程生成。测得的 D/H 比值为 (1.71 ± 0.44) × 10⁻⁴，这个数值在所有已观测的彗星中偏低，并与地球海洋完全一致。