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A growing body of work in cognitive science suggests that reportable conscious access is associated with global ignition over distributed memory systems, while such activation is only partially accessible as individuals cannot directly access or enumerate all activated contents. This tension suggests a plausible mechanism that cognition may rely on a compact representation that approximates the global influence of activation on downstream processing. Inspired by this idea, we introduce the concept of Mindscape Activation Signature (MiA-Signature), a compressed representation of the global activation pattern induced by a query. In LLM systems, this is instantiated via submodular-based selection of high-level concepts that cover the activated context space, optionally refined through lightweight iterative updates using working memory. The resulting MiA-Signature serves as a conditioning signal that approximates the effect of the full activation state while remaining computationally tractable. Integrating MiA-Signatures into both RAG and agentic systems yields consistent performance gains across multiple long-context understanding tasks.

We present our winning system for Task~B (generation with reference passages) in SemEval-2026 Task~8: MTRAGEval. Our method is a heterogeneous ensemble of seven LLMs with two prompting variants, where a GPT-4o-mini judge selects the best candidate per instance. We ranked 1st out of 26 teams, achieving a conditioned harmonic mean of 0.7827 and outperforming the strongest baseline (gpt-oss-120b, 0.6390). Ablations show that diversity in model families, scales, and prompting strategies is essential, with the ensemble consistently beating any single model. We also introduce Meno-Lite-0.1, a 7B domain-adapted model with a strong cost--performance trade-off, and analyse MTRAGEval, highlighting annotation limitations and directions for improvement. Our code is publicly available: https://github.com/RaguTeam/ragu_mtrag_semeval

LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills distilled from experience provide a natural substrate for self-evolution, where high-quality skill curation serves as the key bottleneck. Existing approaches either rely on manual skill curation, prescribe heuristic skill operations, or train for short-horizon skill operations. However, they still struggle to learn complex long-term curation policies from indirect and delayed feedback. To tackle this challenge, we propose SkillOS, an experience-driven RL training recipe for learning skill curation in self-evolving agents. SkillOS pairs a frozen agent executor that retrieves and applies skills with a trainable skill curator that updates an external SkillRepo from accumulated experience. To provide learning signals for curation, we design composite rewards and train on grouped task streams based on skill-relevant task dependencies, where earlier trajectories update the SkillRepo, and later related tasks evaluate these updates. Across multi-turn agentic tasks and single-turn reasoning tasks, SkillOS consistently outperforms memory-free and strong memory-based baselines in both effectiveness and efficiency, with the learned skill curator generalizing across different executor backbones and task domains. Further analyses show that the learned curator produces more targeted skill use, while the skills in SkillRepo evolve into more richly structured Markdown files that encode higher-level meta-skills over time.

Reinforcement learning with verifiable rewards, particularly Group Relative Policy Optimization (GRPO), has significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, in complex tasks, GRPO frequently suffers from the ``zero-advantage problem'': when all sampled rollouts for a query fail, the relative advantage collapses to zero. Consequently, the model loses effective training signals for these questions, wasting the training data and computational budget. While simply increasing the sampling budget for these questions is a common remedy, the static sampling policy inherently constrains reasoning exploration, limiting the success rate. In this paper, we propose Lorem Perturbation for Exploration (LoPE), a simple yet effective training framework to break this exploration bottleneck. We posit that task-irrelevant prompt-space perturbations can shift the model's output distribution enough to unlock orthogonal reasoning pathways for hard questions. Specifically, LoPE prepends sequences stochastically assembled from Lorem Ipsum vocabulary (a pseudo-Latin placeholder text) to the prompts before resampling. Experiments across 1.7B, 4B, and 7B models demonstrate that LoPE significantly outperforms resampling with the original prompts. Further analysis reveals that other Latin-based random sequences with low perplexity are also effective perturbations. Our results establish LoPE as a strong baseline for broadening exploration in LLM reinforcement learning.

While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generalization) inherited from pre-trained Vision-Language Models, they still struggle with complex real-world tasks requiring broader functional capabilities (e.g. motion awareness, memory-aware decision making, and physical sensing). To address this, we introduce RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT), an architecture that unifies these capabilities by integrating heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. RLDX-1 further combines this architecture with system-level design choices, including synthesizing training data for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time deployment. Through empirical evaluation, we show that RLDX-1 consistently outperforms recent frontier VLAs (e.g. π_{0.5} and GR00T N1.6) across both simulation benchmarks and real-world tasks that require broad functional capabilities beyond general versatility. In particular, RLDX-1 shows superiority in ALLEX humanoid tasks by achieving success rates of 86.8% while π_{0.5} and GR00T N1.6 achieve around 40%, highlighting the ability of RLDX-1 to control a high-DoF humanoid robot under diverse functional demands. Together, these results position RLDX-1 as a promising step toward reliable VLAs for complex, contact-rich, and dynamic real-world dexterous manipulation.

Driving world models serve as a pivotal technology for autonomous driving by simulating environmental dynamics. However, existing approaches predominantly focus on future scene generation, often overlooking comprehensive 3D scene understanding. Conversely, while Large Language Models (LLMs) demonstrate impressive reasoning capabilities, they lack the capacity to predict future geometric evolution, creating a significant disparity between semantic interpretation and physical simulation. To bridge this gap, we propose HERMES++, a unified driving world model that integrates 3D scene understanding and future geometry prediction within a single framework. Our approach addresses the distinct requirements of these tasks through synergistic designs. First, a BEV representation consolidates multi-view spatial information into a structure compatible with LLMs. Second, we introduce LLM-enhanced world queries to facilitate knowledge transfer from the understanding branch. Third, a Current-to-Future Link is designed to bridge the temporal gap, conditioning geometric evolution on semantic context. Finally, to enforce structural integrity, we employ a Joint Geometric Optimization strategy that integrates explicit geometric constraints with implicit latent regularization to align internal representations with geometry-aware priors. Extensive evaluations on multiple benchmarks validate the effectiveness of our method. HERMES++ achieves strong performance, outperforming specialist approaches in both future point cloud prediction and 3D scene understanding tasks. The model and code will be publicly released at https://github.com/H-EmbodVis/HERMESV2.

The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterparts (e.g, Z-Image-Turbo and FLUX.2-klein). However, these models present significant challenges for directly continuous supervised fine-tuning. For example, applying the commonly used fine-tuning technique would compromises their inherent few-step inference capability. To address this, we propose D-OPSD, a novel training paradigm for step-distilled diffusion models that enables on-policy learning during supervised fine-tuning. We first find that the modern diffusion model where the LLM/VLM serves as the encoder can inherit its encoder's in-context capabilities. This enables us to make the training as an on-policy self-distillation process. Specifically, during training, we make the model acts as both the teacher and the student with different contexts, where the student is conditioned only on the text feature, while the teacher is conditioned on the multimodal feature of both the text prompt and the target image. Training minimizes the two predicted distributions over the student's own roll-outs. By optimized on the model's own trajectory and under it's own supervision, D-OPSD enables the model to learn new concept, style, etc. without sacrificing the original few-step capacity.

We present JoyAI-Image, a unified multimodal foundation model for visual understanding, text-to-image generation, and instruction-guided image editing. JoyAI-Image couples a spatially enhanced Multimodal Large Language Model (MLLM) with a Multimodal Diffusion Transformer (MMDiT), allowing perception and generation to interact through a shared multimodal interface. Around this architecture, we build a scalable training recipe that combines unified instruction tuning, long-text rendering supervision, spatially grounded data, and both general and spatial editing signals. This design gives the model broad multimodal capability while strengthening geometry-aware reasoning and controllable visual synthesis. Experiments across understanding, generation, long-text rendering, and editing benchmarks show that JoyAI-Image achieves state-of-the-art or highly competitive performance. More importantly, the bidirectional loop between enhanced understanding, controllable spatial editing, and novel-view-assisted reasoning enables the model to move beyond general visual competence toward stronger spatial intelligence. These results suggest a promising path for unified visual models in downstream applications such as vision-language-action systems and world models.

This report describes ARIS (Auto-Research-in-sleep), an open-source research harness for autonomous research, including its architecture, assurance mechanisms, and early deployment experience. The performance of agent systems built on LLMs depends on both the model weights and the harness around them, which governs what information to store, retrieve, and present to the model. For long-horizon research workflows, the central failure mode is not a visible breakdown but a plausible unsupported success: a long-running agent can produce claims whose evidential support is incomplete, misreported, or silently inherited from the executor's framing. Therefore, we present ARIS as a research harness that coordinates machine-learning research workflows through cross-model adversarial collaboration as a default configuration: an executor model drives forward progress while a reviewer from a different model family is recommended to critique intermediate artifacts and request revisions. ARIS has three architectural layers. The execution layer provides more than 65 reusable Markdown-defined skills, model integrations via MCP, a persistent research wiki for iterative reuse of prior findings, and deterministic figure generation. The orchestration layer coordinates five end-to-end workflows with adjustable effort settings and configurable routing to reviewer models. The assurance layer includes a three-stage process for checking whether experimental claims are supported by evidence: integrity verification, result-to-claim mapping, and claim auditing that cross-checks manuscript statements against the claim ledger and raw evidence, as well as a five-pass scientific-editing pipeline, mathematical-proof checks, and visual inspection of the rendered PDF. A prototype self-improvement loop records research traces and proposes harness improvements that are adopted only after reviewer approval.

Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The typical industry recipe involves a highly resource-intensive pipeline spanning pre-training, continual pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL). In this report, we show that when fueled with informative and high-difficulty trajectories, a simple SFT approach could be surprisingly powerful for training frontier search agents. By introducing three simple data synthesis modifications: scaling knowledge graph size for richer exploration, expanding the tool set size for broader functionality, and strict low-step filtering, we establish a stronger baseline. Trained on merely 10.6k data points, our OpenSeeker-v2 achieves state-of-the-art performance across 4 benchmarks (30B-sized agents with ReAct paradigm): 46.0% on BrowseComp, 58.1% on BrowseComp-ZH, 34.6% on Humanity's Last Exam, and 78.0% on xbench, surpassing even Tongyi DeepResearch trained with heavy CPT+SFT+RL pipeline, which achieves 43.4%, 46.7%, 32.9%, and 75.0%, respectively. Notably, OpenSeeker-v2 represents the first state-of-the-art search agent within its model scale and paradigm to be developed by a purely academic team using only SFT. We are excited to open-source the OpenSeeker-v2 model weights and share our simple yet effective findings to make frontier search agent research more accessible to the community.

The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiable rewards (RLVR). However, SFT introduces distributional drift that neither preserves the model's original capabilities nor faithfully matches the supervision distribution. This problem is further amplified in multimodal reasoning, where perception errors and reasoning failures follow distinct drift patterns that compound during subsequent RL. We introduce PRISM, a three-stage pipeline that mitigates this drift by inserting an explicit distribution-alignment stage between SFT and RLVR. Building on the principle of on-policy distillation (OPD), PRISM casts alignment as a black-box, response-level adversarial game between the policy and a Mixture-of-Experts (MoE) discriminator with dedicated perception and reasoning experts, providing disentangled corrective signals that steer the policy toward the supervision distribution without requiring access to teacher logits. While 1.26M public demonstrations suffice for broad SFT initialization, distribution alignment demands higher-fidelity supervision; we therefore curate 113K additional demonstrations from Gemini 3 Flash, featuring dense visual grounding and step-by-step reasoning on the hardest unsolved problems. Experiments on Qwen3-VL show that PRISM consistently improves downstream RLVR performance across multiple RL algorithms (GRPO, DAPO, GSPO) and diverse multimodal benchmarks, improving average accuracy by +4.4 and +6.0 points over the SFT-to-RLVR baseline on 4B and 8B, respectively. Our code, data, and model checkpoints are publicly available at https://github.com/XIAO4579/PRISM.

Multimodal Large Language Models (MLLMs) have demonstrated strong image-level visual understanding and reasoning, yet their pixel-level perception across both images and videos remains limited. Foundation segmentation models such as the SAM series produce high-quality masks, but they rely on low-level visual prompts and cannot natively interpret complex conversational instructions. Existing segmentation MLLMs narrow this gap, but are usually specialized for either images or videos and rarely support both textual and visual prompts in one interface. We introduce X2SAM, a unified segmentation MLLM that extends any-segmentation capabilities from images to videos. Given conversational instructions and visual prompts, X2SAM couples an LLM with a Mask Memory module that stores guided vision features for temporally consistent video mask generation. The same formulation supports generic, open-vocabulary, referring, reasoning, grounded conversation generation, interactive, and visual grounded segmentation across image and video inputs. We further introduce the Video Visual Grounded (V-VGD) segmentation benchmark, which evaluates whether a model can segment object tracks in videos from interactive visual prompts. With a unified joint training strategy over heterogeneous image and video datasets, X2SAM delivers strong video segmentation performance, remains competitive on image segmentation benchmarks, and preserves general image and video chat ability.

Krysta Fauria / Associated Press : Anthropic, OpenAI, and other AI firms met with Hindu, Sikh, and Greek Orthodox leaders to draft principles on how to infuse models with ethics and morality —  As concerns mount over artificial intelligence and its rapid integration into society, tech companies are increasingly turning …

Moneycontrol : OpenAI, Anthropic, and Google's enterprise push with PE firms poses a new competitive threat to India's IT industry, as services become increasingly automatable —  On Wall Street, the announcements sounded like the next phase of the artificial intelligence (AI) boom: frontier model companies …

Kate Clark / Wall Street Journal : A profile of Anthropic CFO Krishna Rao, who tends to take a conservative approach to revenue projections and has chosen to raise less money than is available —  Krishna Rao is navigating unprecedented growth, compute constraints and the idiosyncratic Amodeis

Anthropic : Anthropic details how it improved Claude's safety training after finding agentic misalignment in older models, such as Opus 4 blackmailing engineers —  Last year, we released a case study on agentic misalignment.  In experimental scenarios, we showed that AI models from many different …

Nathan Lambert / Interconnects AI : Impressions of China's AI ecosystem after visiting many leading AI labs there, and the similarities and differences in working on LLMs in China and the West —  Lessons from my trip to talk to most of the leading AI labs in China. … Audio playback is not supported on your browser.  Please upgrade.

Rachel Metz / Bloomberg : Akamai says it struck a seven-year cloud computing deal with a “leading frontier model provider”; sources: the deal was with Anthropic and is worth $1.8B —  Anthropic PBC has signed a $1.8 billion computing deal with cloud services provider Akamai Technologies Inc. to meet surging demand …

Anissa Gardizy / The Information : Sources: OpenAI and Broadcom discuss terms for Broadcom to finance initial custom chip production for ~$18B, conditioned on Microsoft buying ~40% of the chips —  When OpenAI and chip designer Broadcom announced last fall that they would make custom artificial intelligence chips together, they positioned it as a done deal.

Anthropic : Anthropic researchers detail “natural language autoencoders”, which convert LLM activations, the numbers encoding a model's thoughts, into natural language text —  When you talk to an AI model like Claude, you talk to it in words.  Internally, Claude processes those words …

Simon Willison / Simon Willison's Weblog : While Anthropic will use the Colossus 1 data center, which has a really bad environmental record, xAI retains the larger Colossus 2 for its own AI training —  There weren't a lot of big new announcements from Anthropic at yesterday's Code w/ Claude event, but the biggest by far …

Ben Schoon / 9to5Google : Google Chrome silently installs a ~4GB Gemini Nano model on desktop devices; Google says it has been offered since 2024 and users can remove it via settings —  The ongoing march of AI features continues to go on, whether you want it to or not, and a recent update to Google Chrome probably installed …

Jay Peters / The Verge : Musk v. Altman: Mira Murati testifies that Sam Altman lied to her about the safety standards for a new OpenAI model and that he made her work more difficult —  OpenAI's former CEO testified under oath that Altman lied to her. … Mira Murati, OpenAI's former CTO, has testified under oath …

Axios : Anthropic says it signed a deal with SpaceX to use “all of the compute capacity” at Colossus 1, giving it access to over 300 MW of new capacity within the month —  Anthropic said Wednesday it has struck a deal to gain access to compute capacity from Elon Musk's SpaceX …

根据 Linux 基金会公布的 2025 年年度报告，去年它在 Linux 内核项目上的开支为 841 万美元，占到了总预算的 2.95%，其中 Linux 内核作者 Linus Torvalds 薪水大约为 150 万美元（其中包括百万美元的“其它”收入，该收入未明确定义）。Linux 基金会其实是一个行业协会，并非公益性非营利组织，它的资金来自于科技巨头的赞助，从董事会成员的构成就可以看出，它的董事来自索尼、华为、OpenAI、高通、三星、微软、甲骨文、Google 和 Meta 等。Linux 基金会托管了大约 1500 个开源项目，Linux 内核也不是最大的项目，它在区块链上支出占到了总预算的 4%。

法国检方对马斯克（Elon Musk）及其 X 平台展开刑事调查。法国执法部门三个月前搜查了 X 位于巴黎的办公室，传唤马斯克接受讯问。检方原计划于今年 4 月约谈马斯克及前 X CEO Linda Yaccarino，但两人都未现身。 现在法国当局正试图以刑事指控相威胁，强制他们到场接受讯问。除未成年人色情图像外，调查还涉及 Grok 传播否认纳粹大屠杀的言论以及深度伪造色情。检方称，如果马斯克和 Yaccarino 再次缺席他们将面临缺席起诉。

五大出版商 Hachette、Macmillan、McGraw Hill、Elsevier 和 Cengage 以及作家 Scott Turow 起诉 Meta 公司及其 CEO 扎克伯格(Mark Zuckerberg)，指控扎克伯格个人授权和积极鼓励大规模版权侵犯，使用盗版图书、期刊论文和网络抓取的资料训练 Meta 公司的 Llama AI 系统。Meta 否认有任何不当行为，表示将应诉，称法院已认定使用受版权保护的材料训练 AI 属于合理使用。用版权材料训练 AI 可能是合理使用，但 Meta 使用了非法手段获取了版权材料。起诉书称，为了赢得 AI 军备竞赛并构建一个功能完善的生成式 AI 模型，Meta 和扎克伯格遵循了其“快速行动打破常规”的信条，首先从盗版网站非法下载了数百万本受版权保护的书籍和期刊文章，未经授权抓取了几乎整个互联网的内容，构成了历史上最大规模的版权侵权之一。

马斯克（Elon Musk）上周在法庭上作证指控 OpenAI 的另外两位联合创始人 Greg Brockman 和 Sam Altman 放弃创办时的其非营利使命以谋取个人私利。本周 Brockman 出庭作证，被迫在陪审团前阅读个人日记，似乎印证了马斯克的指控。Brockman 称他从学生时期就写日记，在职业生涯中通过写日记去思考重大决策。这些日记是在去年 10 月作为证据递交到法庭，今年 1 月解封。2017 年马斯克向 OpenAI 发出最后通牒，要么完全由他掌控 OpenAI 的营利性部门，要么 OpenAI 继续保持非营利性质。而 Brockman 同一时间在日记里畅谈了赚钱的好处。在 OpenAI 成立了不由马斯克掌控的营利性部门之后，Brockman 个人在 OpenAI 的股份如今价值 300 亿美元。他还在日记中纠结投票反对马斯克的计划或者投票支持将马斯克逐出董事会是否在道德上是错误的。他在日记中写道：“从他手中夺走这家非营利机构是错误的。在道德上是败坏的。”

Google Chrome 被发现在合格设备上静默下载了 4GB 大小的 Gemini Nano 模型，而且会在用户删除之后重新下载。Gemini Nano 就是 Google 受争议的 Prompt API 所针对的本地模型，运行该模型需要至少有 4GB 显存、16GB 内存和至少 22GB 可用空间（浏览器安装包所在分区）。Google Chrome 有 38 亿用户，是市场份额最高的浏览器，满足运行 Gemini Nano 要求的设备至少数以亿计，即使不考虑重复下载，为如此多的设备静默下载 4GB 数据也是难以想象的资源浪费。此外值得一提是 Chrome 安装包大小是 1GB 左右，悄悄下载的模型大小四倍于浏览器本身，超出了大多数用户对额外功能大小的预期。Gemini Nano 下载在被称为 OptGuideOnDeviceModel 的文件夹内，该名字代表 OptimizationGuide on-device model storage。

加州民主党参议员 Adam Schiff 提出了获得两党支持的新法案——《The Literacy in Future Technologies Artificial Intelligence（LIFT AI Act）》，旨在修改 K-12 课程加入 AI 素养课，为 AI 课程以及相关教材、教师培训等提供资助。法案将 AI 素养定义为使用 AI，具体是指“具备与年龄相符的知识和能力，能有效使用 AI，批判性解读输出，解决 AI 世界中的问题，以及降低潜在风险。法案得到了主要 AI 公司如 OpenAI、Google 和微软，以及美国教师联合会、信息技术产业理事会、软件与信息产业协会、惠普公司等的支持。

日程安排平台 Cal.com 上月宣布从开源转为闭源，理由是 AI 工具更容易从开源代码中发现漏洞，而安全性依赖于模糊，因此闭源有助于提高安全。现在英国国家医疗服务体系（NHS）以相同的理由准备关闭它几乎所有的开源库，这一决定引发了广泛争议和批评。批评者指出 NHS 公布的大部分开源库是数据集、内部工具、指南、研究工具、前端设计等，它们不会因为安全扫描技术的进步而受到影响。此外是否开源对于 Anthropic Mythos 之类的 AI 工具并无区别，因为它们也能分析二进制程序并寻找漏洞。批评者发表了公开信，呼吁 NHS 保持其代码公开。

OpenAI Codex CLI 系统提示词专门加入了一条对地精（Goblins）等词的限制：“never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query”。官方解释称，从 GPT-5.1 开始该公司的模型在比喻中提及 goblin 等词的频率大增，ChatGPT 中 goblin 的使用量增加了 175%，gremlin 使用量增加了 52%。它为此展开了调查，发现是因为 Nerdy 个性无意中奖励了此类比喻，导致高频使用 goblin 的行为扩散。为解决该问题，OpenAI 淘汰了 Nerdy 个性，移除了对 goblin 友好的奖励信号，从训练数据过滤掉相关示例，防止其再次不恰当的出现。

Google Chrome 在 2025 年提出了 Prompt API，也就是为浏览器集成的本地模型——使用前需要下载——提供统一的 JavaScript API。Google 还有意让该 API 成为一个 W3C 标准。Chrome 桌面版集成的大模型是 Gemini Nano，使用该模型需要本地设备至少有 4GB 显存、16GB 内存和至少 22GB 可用空间（浏览器所在硬盘）。Mozilla 开发者发表声明反对 Chrome 的 Prompt API。开发者认为该 API 存在巨大的互操作性问题，因为不同的模型都有各种独特的特性，因此系统提示词需要对模型进行针对性调整，然而对一个模型进行的调整对另一个模型就可能是过度修正。为了实现互操作性，Mozilla 和 Apple 可能不得不获得 Google 模型的授权，或者发布一个与 Google 模型特性兼容的模型。另一个大问题是模型的中立性缺乏。

用 Rust 开发的文本编辑器项目 Zed 宣布发布 1.0 版本。开发者表示 1.0 版本并不意味着“完成”或“完美”，而是意味着到达了一个关键点。开发者还宣称 Zed 编辑器是一个 AI 原生编辑器，能并行运行多个 AI 智能体，包括 Claude Agent、Codex、OpenCode，以及 Cursor。AI 构建在编辑器的基础架构之中，而不是附加组件。

2024 年马斯克（Elon Musk）向旧金山高等法院起诉 OpenAI 及其联合创始人 Sam Altman 和 Greg Brockman 违反公司的创始原则，将商业利益置于公共利益之上。OpenAI 则公开了马斯克的邮件，证明作为曾经的联合创始人，马斯克同意 OpenAI 建立一个盈利实体，还表示将提供资金，但之后暂停了资金支持，他的目的是获得多数股权和董事会控制权，双方最终因此终止了合作。本周这起诉讼正式进入审讯阶段，马斯克在法庭上作证，称创办 OpenAI 是将其作为一家非盈利公司去对抗 Google，如果 OpenAI 的目标是盈利他不会支持它。马斯克称他在与 Google 联合创始人 Larry Page 就 AI 安全问题上发生争执后萌生了创办非盈利 AI 公司的想法。他担心 Page 没有认真对待 AI 安全问题，因此希望通过一个非盈利的开源替代方案去对抗 Google。

斯坦福、伦敦帝国理工和互联网档案馆的研究人员发表论文《The Impact of AI-Generated Text on the Internet》，他们利用互联网档案馆的数据发现在 ChatGPT 发布三年之后，35% 的新网站是 AI 生成或使用 AI 辅助，而 ChatGPT 之前的比例是零。论文合作者、斯坦福 AI 研究员 Jonas Dolezal 说，人类用了几十年时间塑造互联网，但它的很大一部分仅仅三年就被 AI 重新定义，我们正见证数字化景观在短时间内的一次重大转变。