OrangeBot.AI Digest — 2025-12-18

Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.

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An extremely fast Python type checker and language server, written in Rust.

Letta is the platform for building stateful agents: open AI with advanced memory that can learn and self-improve over time.

Easily and securely send things from one computer to another 🐊 📦

SoTA open-source TTS

Recent advances in multimodal large language models unlock unprecedented opportunities for GUI automation. However, a fundamental challenge remains: how to efficiently acquire high-quality training data while maintaining annotation reliability? We introduce a self-evolving training pipeline powered by the Calibrated Step Reward System, which converts model-generated trajectories into reliable training signals through trajectory-level calibration, achieving >90% annotation accuracy with 10-100x lower cost. Leveraging this pipeline, we introduce Step-GUI, a family of models (4B/8B) that achieves state-of-the-art GUI performance (8B: 80.2% AndroidWorld, 48.5% OSWorld, 62.6% ScreenShot-Pro) while maintaining robust general capabilities. As GUI agent capabilities improve, practical deployment demands standardized interfaces across heterogeneous devices while protecting user privacy. To this end, we propose GUI-MCP, the first Model Context Protocol for GUI automation with hierarchical architecture that combines low-level atomic operations and high-level task delegation to local specialist models, enabling high-privacy execution where sensitive data stays on-device. Finally, to assess whether agents can handle authentic everyday usage, we introduce AndroidDaily, a benchmark grounded in real-world mobile usage patterns with 3146 static actions and 235 end-to-end tasks across high-frequency daily scenarios (8B: static 89.91%, end-to-end 52.50%). Our work advances the development of practical GUI agents and demonstrates strong potential for real-world deployment in everyday digital interactions.

Efficiency, as a critical practical challenge for LLM-driven agentic and reasoning systems, is increasingly constrained by the inherent latency of autoregressive (AR) decoding. Speculative decoding mitigates this cost through a draft-verify scheme, yet existing approaches rely on AR draft models (a.k.a., drafters), which introduce two fundamental issues: (1) step-wise uncertainty accumulation leads to a progressive collapse of trust between the target model and the drafter, and (2) inherently sequential decoding of AR drafters. Together, these factors cause limited speedups. In this paper, we show that a diffusion large language model (dLLM) drafters can naturally overcome these issues through its fundamentally different probabilistic modeling and efficient parallel decoding strategy. Building on this insight, we introduce DEER, an efficient speculative decoding framework that drafts with diffusion and verifies with AR models. To enable high-quality drafting, DEER employs a two-stage training pipeline to align the dLLM-based drafters with the target AR model, and further adopts single-step decoding to generate long draft segments. Experiments show DEER reaches draft acceptance lengths of up to 32 tokens, far surpassing the 10 tokens achieved by EAGLE-3. Moreover, on HumanEval with Qwen3-30B-A3B, DEER attains a 5.54x speedup, while EAGLE-3 achieves only 2.41x. Code, model, demo, etc, will be available at https://czc726.github.io/DEER/

Multi-token generation has emerged as a promising paradigm for accelerating transformer-based large model inference. Recent efforts primarily explore diffusion Large Language Models (dLLMs) for parallel decoding to reduce inference latency. To achieve AR-level generation quality, many techniques adapt AR models into dLLMs to enable parallel decoding. However, they suffer from limited speedup compared to AR models due to a pretrain-to-posttrain mismatch. Specifically, the masked data distribution in post-training deviates significantly from the real-world data distribution seen during pretraining, and dLLMs rely on bidirectional attention, which conflicts with the causal prior learned during pretraining and hinders the integration of exact KV cache reuse. To address this, we introduce Jacobi Forcing, a progressive distillation paradigm where models are trained on their own generated parallel decoding trajectories, smoothly shifting AR models into efficient parallel decoders while preserving their pretrained causal inference property. The models trained under this paradigm, Jacobi Forcing Model, achieves 3.8x wall-clock speedup on coding and math benchmarks with minimal loss in performance. Based on Jacobi Forcing Models' trajectory characteristics, we introduce multi-block decoding with rejection recycling, which enables up to 4.5x higher token acceptance count per iteration and nearly 4.0x wall-clock speedup, effectively trading additional compute for lower inference latency. Our code is available at https://github.com/hao-ai-lab/JacobiForcing.

Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy directly on on-device environments. While small-parameter models are progressively endowed with strong general capabilities, standard Vision Transformer (ViT) encoders remain a critical bottleneck, suffering from excessive latency and memory consumption when processing high-resolution inputs.To address these challenges, we introduce HyperVL, an efficient multimodal large language model tailored for on-device inference. HyperVL adopts an image-tiling strategy to cap peak memory usage and incorporates two novel techniques: (1) a Visual Resolution Compressor (VRC) that adaptively predicts optimal encoding resolutions to eliminate redundant computation, and (2) Dual Consistency Learning (DCL), which aligns multi-scale ViT encoders within a unified framework, enabling dynamic switching between visual branches under a shared LLM. Extensive experiments demonstrate that HyperVL achieves state-of-the-art performance among models of comparable size across multiple benchmarks. Furthermore, it significantly significantly reduces latency and power consumption on real mobile devices, demonstrating its practicality for on-device multimodal inference.

Recent reinforcement learning (RL) approaches like outcome-supervised GRPO have advanced chain-of-thought reasoning in Vision Language Models (VLMs), yet key issues linger: (i) reliance on costly and noisy hand-curated annotations or external verifiers; (ii) flat and sparse reward schemes in GRPO; and (iii) logical inconsistency between a chain's reasoning and its final answer. We present Puzzle Curriculum GRPO (PC-GRPO), a supervision-free recipe for RL with Verifiable Rewards (RLVR) that strengthens visual reasoning in VLMs without annotations or external verifiers. PC-GRPO replaces labels with three self-supervised puzzle environments: PatchFit, Rotation (with binary rewards) and Jigsaw (with graded partial credit mitigating reward sparsity). To counter flat rewards and vanishing group-relative advantages, we introduce a difficulty-aware curriculum that dynamically weights samples and peaks at medium difficulty. We further monitor Reasoning-Answer Consistency (RAC) during post-training: mirroring reports for vanilla GRPO in LLMs, RAC typically rises early then degrades; our curriculum delays this decline, and consistency-enforcing reward schemes further boost RAC. RAC correlates with downstream accuracy. Across diverse benchmarks and on Qwen-7B and Qwen-3B backbones, PC-GRPO improves reasoning quality, training stability, and end-task accuracy, offering a practical path to scalable, verifiable, and interpretable RL post-training for VLMs.

Universal transformers (UTs) have been widely used for complex reasoning tasks such as ARC-AGI and Sudoku, yet the specific sources of their performance gains remain underexplored. In this work, we systematically analyze UTs variants and show that improvements on ARC-AGI primarily arise from the recurrent inductive bias and strong nonlinear components of Transformer, rather than from elaborate architectural designs. Motivated by this finding, we propose the Universal Reasoning Model (URM), which enhances the UT with short convolution and truncated backpropagation. Our approach substantially improves reasoning performance, achieving state-of-the-art 53.8% pass@1 on ARC-AGI 1 and 16.0% pass@1 on ARC-AGI 2. Our code is avaliable at https://github.com/zitian-gao/URM.

We propose IC-Effect, an instruction-guided, DiT-based framework for few-shot video VFX editing that synthesizes complex effects (\eg flames, particles and cartoon characters) while strictly preserving spatial and temporal consistency. Video VFX editing is highly challenging because injected effects must blend seamlessly with the background, the background must remain entirely unchanged, and effect patterns must be learned efficiently from limited paired data. However, existing video editing models fail to satisfy these requirements. IC-Effect leverages the source video as clean contextual conditions, exploiting the contextual learning capability of DiT models to achieve precise background preservation and natural effect injection. A two-stage training strategy, consisting of general editing adaptation followed by effect-specific learning via Effect-LoRA, ensures strong instruction following and robust effect modeling. To further improve efficiency, we introduce spatiotemporal sparse tokenization, enabling high fidelity with substantially reduced computation. We also release a paired VFX editing dataset spanning 15 high-quality visual styles. Extensive experiments show that IC-Effect delivers high-quality, controllable, and temporally consistent VFX editing, opening new possibilities for video creation.

Recent visual generative models often struggle with consistency during image editing due to the entangled nature of raster images, where all visual content is fused into a single canvas. In contrast, professional design tools employ layered representations, allowing isolated edits while preserving consistency. Motivated by this, we propose Qwen-Image-Layered, an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers, enabling inherent editability, where each RGBA layer can be independently manipulated without affecting other content. To support variable-length decomposition, we introduce three key components: (1) an RGBA-VAE to unify the latent representations of RGB and RGBA images; (2) a VLD-MMDiT (Variable Layers Decomposition MMDiT) architecture capable of decomposing a variable number of image layers; and (3) a Multi-stage Training strategy to adapt a pretrained image generation model into a multilayer image decomposer. Furthermore, to address the scarcity of high-quality multilayer training images, we build a pipeline to extract and annotate multilayer images from Photoshop documents (PSD). Experiments demonstrate that our method significantly surpasses existing approaches in decomposition quality and establishes a new paradigm for consistent image editing. Our code and models are released on https://github.com/QwenLM/Qwen-Image-Layered{https://github.com/QwenLM/Qwen-Image-Layered}

The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. However, most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. In this paper, we present Skyra, a specialized multimodal large language model (MLLM) that identifies human-perceivable visual artifacts in AI-generated videos and leverages them as grounded evidence for both detection and explanation. To support this objective, we construct ViF-CoT-4K for Supervised Fine-Tuning (SFT), which represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations. We then develop a two-stage training strategy that systematically enhances our model's spatio-temporal artifact perception, explanation capability, and detection accuracy. To comprehensively evaluate Skyra, we introduce ViF-Bench, a benchmark comprising 3K high-quality samples generated by over ten state-of-the-art video generators. Extensive experiments demonstrate that Skyra surpasses existing methods across multiple benchmarks, while our evaluation yields valuable insights for advancing explainable AI-generated video detection.

Generative models can synthesize highly realistic content, so-called deepfakes, that are already being misused at scale to undermine digital media authenticity. Current deepfake detection methods are unreliable for two reasons: (i) distinguishing inauthentic content post-hoc is often impossible (e.g., with memorized samples), leading to an unbounded false positive rate (FPR); and (ii) detection lacks robustness, as adversaries can adapt to known detectors with near-perfect accuracy using minimal computational resources. To address these limitations, we propose a resynthesis framework to determine if a sample is authentic or if its authenticity can be plausibly denied. We make two key contributions focusing on the high-precision, low-recall setting against efficient (i.e., compute-restricted) adversaries. First, we demonstrate that our calibrated resynthesis method is the most reliable approach for verifying authentic samples while maintaining controllable, low FPRs. Second, we show that our method achieves adversarial robustness against efficient adversaries, whereas prior methods are easily evaded under identical compute budgets. Our approach supports multiple modalities and leverages state-of-the-art inversion techniques.

Recent multilingual named entity recognition (NER) work has shown that large language models (LLMs) can provide effective synthetic supervision, yet such datasets have mostly appeared as by-products of broader experiments rather than as systematic, reusable resources. We introduce FiNERweb, a dataset-creation pipeline that scales the teacher-student paradigm to 91 languages and 25 scripts. Building on FineWeb-Edu, our approach trains regression models to identify NER-relevant passages and annotates them with multilingual LLMs, resulting in about 225k passages with 235k distinct entity labels. Our experiments show that the regression model achieves more than 84 F1, and that models trained on FiNERweb obtain comparable or improved performance in zero shot transfer settings on English, Thai, and Swahili, despite being trained on 19x less data than strong baselines. In addition, we assess annotation quality using LLM-as-a-judge and observe consistently high scores for both faithfulness (3.99 out of 5) and completeness (4.05 out of 5), indicating reliable and informative annotations. Further, we release the dataset with both English labels and translated label sets in the respective target languages because we observe that the performance of current state-of-the-art models drops by 0.02 to 0.09 F1 when evaluated using target language labels instead of English ones. We release FiNERweb together with all accompanying artifacts to the research community in order to facilitate more effective student-teacher training for multilingual named entity recognition.

Spatial understanding over continuous visual input is crucial for MLLMs to evolve into general-purpose assistants in physical environments. Yet there is still no comprehensive benchmark that holistically assesses the progress toward this goal. In this work, we introduce MMSI-Video-Bench, a fully human-annotated benchmark for video-based spatial intelligence in MLLMs. It operationalizes a four-level framework, Perception, Planning, Prediction, and Cross-Video Reasoning, through 1,106 questions grounded in 1,278 clips from 25 datasets and in-house videos. Each item is carefully designed and reviewed by 3DV experts with explanatory rationales to ensure precise, unambiguous grounding. Leveraging its diverse data sources and holistic task coverage, MMSI-Video-Bench also supports three domain-oriented sub-benchmarks (Indoor Scene Perception Bench, Robot Bench and Grounding Bench) for targeted capability assessment. We evaluate 25 strong open-source and proprietary MLLMs, revealing a striking human--AI gap: many models perform near chance, and the best reasoning model lags humans by nearly 60%. We further find that spatially fine-tuned models still fail to generalize effectively on our benchmark. Fine-grained error analysis exposes systematic failures in geometric reasoning, motion grounding, long-horizon prediction, and cross-video correspondence. We also show that typical frame-sampling strategies transfer poorly to our reasoning-intensive benchmark, and that neither 3D spatial cues nor chain-of-thought prompting yields meaningful gains. We expect our benchmark to establish a solid testbed for advancing video-based spatial intelligence.

Reinforcement learning has become essential for strengthening the reasoning abilities of large language models, yet current exploration mechanisms remain fundamentally misaligned with how these models actually learn. Entropy bonuses and external semantic comparators encourage surface level variation but offer no guarantee that sampled trajectories differ in the update directions that shape optimization. We propose G2RL, a gradient guided reinforcement learning framework in which exploration is driven not by external heuristics but by the model own first order update geometry. For each response, G2RL constructs a sequence level feature from the model final layer sensitivity, obtainable at negligible cost from a standard forward pass, and measures how each trajectory would reshape the policy by comparing these features within a sampled group. Trajectories that introduce novel gradient directions receive a bounded multiplicative reward scaler, while redundant or off manifold updates are deemphasized, yielding a self referential exploration signal that is naturally aligned with PPO style stability and KL control. Across math and general reasoning benchmarks (MATH500, AMC, AIME24, AIME25, GPQA, MMLUpro) on Qwen3 base 1.7B and 4B models, G2RL consistently improves pass@1, maj@16, and pass@k over entropy based GRPO and external embedding methods. Analyzing the induced geometry, we find that G2RL expands exploration into substantially more orthogonal and often opposing gradient directions while maintaining semantic coherence, revealing that a policy own update space provides a far more faithful and effective basis for guiding exploration in large language model reinforcement learning.

As humans, we are natural any-horizon reasoners, i.e., we can decide whether to iteratively skim long videos or watch short ones in full when necessary for a given task. With this in mind, one would expect video reasoning models to reason flexibly across different durations. However, SOTA models are still trained to predict answers in a single turn while processing a large number of frames, akin to watching an entire long video, requiring significant resources. This raises the question: Is it possible to develop performant any-horizon video reasoning systems? Inspired by human behavior, we first propose SAGE, an agent system that performs multi-turn reasoning on long videos while handling simpler problems in a single turn. Secondly, we introduce an easy synthetic data generation pipeline using Gemini-2.5-Flash to train the orchestrator, SAGE-MM, which lies at the core of SAGE. We further propose an effective RL post-training recipe essential for instilling any-horizon reasoning ability in SAGE-MM. Thirdly, we curate SAGE-Bench with an average duration of greater than 700 seconds for evaluating video reasoning ability in real-world entertainment use cases. Lastly, we empirically validate the effectiveness of our system, data, and RL recipe, observing notable improvements of up to 6.1% on open-ended video reasoning tasks, as well as an impressive 8.2% improvement on videos longer than 10 minutes.

In recent multimodal research, the diffusion paradigm has emerged as a promising alternative to the autoregressive paradigm (AR), owing to its unique decoding advantages. However, due to the capability limitations of the base diffusion language model, the performance of the diffusion vision language model (dVLM) still lags significantly behind that of mainstream models. This leads to a simple yet fundamental question: Is it possible to construct dVLMs based on existing powerful AR models? In response, we propose DiffusionVL, a dVLM family that could be translated from any powerful AR models. Through simple fine-tuning, we successfully adapt AR pre-trained models into the diffusion paradigm. This approach yields two key observations: (1) The paradigm shift from AR-based multimodal models to diffusion is remarkably effective. (2) Direct conversion of an AR language model to a dVLM is also feasible, achieving performance competitive with LLaVA-style visual-instruction-tuning. Further, we introduce a block-decoding design into dVLMs that supports arbitrary-length generation and KV cache reuse, achieving a significant inference speedup. We conduct a large number of experiments. Despite training with less than 5% of the data required by prior methods, DiffusionVL achieves a comprehensive performance improvement-a 34.4% gain on the MMMU-Pro (vision) bench and 37.5% gain on the MME (Cog.) bench-alongside a 2x inference speedup. The model and code are released at https://github.com/hustvl/DiffusionVL.

丹麦 IT 部长向交通部移交了首台没有安装任何微软软件的 Linux 笔记本电脑。交通部是名为 SIA Open 的试点项目的首个客户，最终目标是让 1.5 万用户使用开源替代，避免过度依赖微软等私有软件供应商，掌控系统和数据的主权。交通部门的负责人 Stefan Søsted 表示要确保自己知道重要的信息储存在什么地方。相比私有软件，开源替代并不逊色且价格更低。微软丹麦分公司回应称，微软的解决方案定价合理且具有竞争力，兼具高安全性、创新性和高效协作性，微软欢迎竞争，开源与微软产品之间并不存在矛盾。

扫地机器人 Roomba 的制造商 iRobot 最近申请破产重组。根据重组协议，iRobot 的控制权将转交给其代工厂及最大债权人深圳杉川机器人公司。公司 CEO Gary Cohen 接受日经采访时表示，破产原因是产品技术创新方面落后中国竞争企业四年。关于进入杉川集团旗下后的业务运营，他表示：“将保持 Roomba 的品牌和各地区的销售体制，并引入中国的产品开发速度”。他主张称，计划将总部功能和营销部门留在美国，以此“与（其他）中国企业划清界限”。关于 Roomba 收集的数据的管理，他明确表示：“现在和今后都不会（保存在中国的服务器上）”。他同时强调，云服务的利用和应用程序开发将继续保持以美国为中心的体制。

索尼互动于今年 7 月向加州法庭起诉腾讯抄袭其《地平线》系列游戏，指控腾讯版权和商标侵犯。腾讯旗下工作室 Polaris Quest 最近发布了《Light of Motiram》的预告片，该游戏背景与索尼旗下工作室 Guerrilla Games 开发的《地平线（Horizon）》 系列游戏《地平线零之曙光》和《地平线 西之绝境》几乎如出一辙，都发生在机械占领的末世后文明世界，女主角身穿类似猎人的服装与机械生物进行战斗。索尼称，腾讯曾试图获得《地平线》系列的 IP 授权但遭到了它的拒绝。本周双方达成了不公开的和解，《Light of Motiram》已经在 Steam 和 Epic Games Store 下架。索尼和腾讯都拒绝对此发表进一步的评论。

塑料开启了全新的便利时代，从此廉价的一次性用品充斥着千家万户。但普通人也因此暴露在成千上万种化学物质中，这些物质会从塑料制品渗入尘埃、食物和水，最终进入人体。瑞士和挪威科学家建立了一个数据库，记录了与塑料相关的 1.6 万种化学物质。这 1.6 万种化学物质中，有逾 5400 种被认为对人类健康有害，仅 161 种被认定为无害，其余逾万种物质尚缺乏足够数据评估其安全性。这些化学物质通过多种途径进入食物：黑色塑料餐具和托盘通常含有溴化阻燃剂，溴化阻燃剂已被证实与儿童智商下降和神经发育问题有关，研究发现孕妇体内溴化阻燃剂水平每增加 10 倍，其子女的智商平均会下降 3.7 点；不粘锅、可降解餐盘和餐具通常含有全氟烷基和多氟烷基物质（PFAS）。这些化学物质在环境中或人体内需要数年才能分解，它们与肾癌、睾丸癌以及儿童自闭症和多动症等发育问题相关。

根据 Steam Replay 2025 年度总结，2025 年 Steam 用户 14% 的游戏时间是花在新游戏上，44% 的游戏时间花在 1 至 7 年历史的游戏上，40% 的游戏时长花在超过八年的游戏上——长久不衰的游戏如 DOTA 2 和《绝地求生（PUBG: Battlegrounds）》都属于这一类别。Steam 用户平均玩的游戏数量只有 4 款，PC 玩家和主机玩家类似，都越来越倾向于少数几款游戏。

亿万富翁 Jared Isaacman 的 NASA 局长任命堪称一波三折：特朗普最初提名 Isaacman 为 NASA 局长，但五月撤回了提名，当时特朗普与马克斯（Elon Musk）关系破裂，而 Isaacman 是马斯克青睐的人选。Isaacman 此后加强了对特朗普阵营的个人攻势，包括向特朗普的政治行动委员会 MAGA Inc.两次捐赠了 100 万美元，以及向共和党全国委员会捐赠 17.5 万美元，从而改善了与特朗普的关系，重新获得了特朗普的任命。本周美国参议院以 67 票赞成 30 票反对确认了对 Isaacman 的 NASA 局长任命。

美国在线学习平台 Coursera 宣布达成全股票交易收购竞争对手 Udemy，合并后的公司估值 25 亿美元。新冠疫情期间在线教育的需求一度激增，但疫情后需求下降，在线教育行业陷入困境。根据收购协议，Udemy 股东每持有 1 股 Udemy 股票将获得 0.8 股 Coursera 股票，对 Udemy 的估值约为 9.3 亿美元。交易预计将于明年下半年完成，仍需获得监管机构和股东的批准。Coursera 业务模式是与大学和机构合作提供学位课程和专业证书；而 Udemy 则运营着一个让独立讲师直接向消费​​者和企业销售课程的市场。两家公司的股价今年都出现了下跌，合并后的公司将致力于吸引企业客户，为公司员工提供 AI、数据科学和软件开发方面的再培训。

日本科学家报告研发出一种堪称完美的塑料，由植物纤维素制成，植物纤维素是世界最丰富的有机化合物。新塑料强度高、柔韧性好，能在自然环境中快速分解，没有任何微塑料污染。微塑料是一种全球性污染物，几乎存在于所有生态系统中，甚至包括人体组织和血液。大多数标榜“可生物降解”的塑料要么无法在海洋环境中降解，要么需要长时间才能降解，而且会在降解过程中留下微塑料。基于植物的新塑料由木浆衍生物羧甲基纤维和正电荷聚乙烯亚胺胍离子，以及一种增塑剂有机盐氯化胆碱构成。根据氯化胆碱的用量，柔韧性可从坚硬如玻璃到拉伸至其原始长度的 130%。

骨骼肌组织和功能随年龄增长会逐渐衰退，到 60 岁左右可能会影响独立生活和健康。瑞典的一项长达 47 年的研究发现，即使持续进行有规律的训练，体能、力量和耐力在 35 岁左右开始逐渐下降。研究发现，最大有氧能力和肌肉耐力在 26-36 岁达到峰值，一开始缓慢下降，然后加速下降，男女之间的下降速度没有什么差异。肌肉力量男性在 27 岁达到峰值，女性在 19 岁达到峰值。从巅峰期到 63 岁，总体体能下降幅度为 30% 至 48%。即使成年人开始锻炼的时间较晚，他们的身体机能仍然可以提高 5%-10%，证明锻炼永远不晚。

欧盟放宽其原定于 2035 年实施的内燃机汽车禁令，允许少量低排放汽车继续销售。欧盟原计划到 2035 年新车的排放量必须为零，或比 2021 年的水平降低 100%。现在放宽为降低 90%，为插电混动汽车留出了空间——此类汽车配备电动机和内燃机，无需寻找充电站即可利用内燃机为电池充电。欧盟官员表示此举不会影响欧盟 27 个成员国在 2050 年实现经济碳中和的目标。

功能磁共振成像（fMRI）过去三十年被广泛用于大脑研究，但根据发表在《Nature Neuroscience》上的一项研究，fMRI 信号存在大量的噪音，四成 fMRI 信号与实际大脑活动不一致。研究人员发现，40% 的案例中 fMRI 信号增强与脑活动减弱相关。在脑活动增强的区域，fMRI 信号反而减弱。研究结果表明，磁共振成像（MRI）测量的氧含量与神经元活动之间不存在普遍有效的耦合。这项发现从根本上挑战了以往对 fMRI 数据与神经元活动之间关系的解读方式。

苹果和 Google 最近都释出了紧急更新去修复正被利用的 0day。苹果向 iPhone、iPad 和 Mac 释出更新修复两个 WebKit bug，苹果表示漏洞可能被用于对特定目标发动复杂攻击，它没有披露细节。Google 释出 Chrome 更新修复了多个安全漏洞，其中包括正被利用 0day CVE-2025-14174，该 bug 属于越界内存访问漏洞。两家公司都没有透露细节，但看起来是相关的，Google 将 CVE-2025-14174 的发现归功于苹果的安全工程团队和 Google 的安全团队 Threat Analysis Group。目前看来该 0day 是被间谍软件利用。苹果 2025 年至今修复了 9 个 0day，Google 今年至今修复了 8 个 Chrome 0day。

蓝箭航天本月初执行了朱雀三号火箭的首飞任务，火箭发射成功但回收失败。朱雀三号的外形模仿了 SpaceX 的 Falcon 9 火箭。SpaceX 正在开发它的新一代重型火箭 Starship，因此越来越多的中国火箭初创公司也在开发外形类似 Starship 的火箭，当然不太可能第一步就开发重型火箭，它们开发的是 Starship 的缩小版本。名叫上海大航跃迁的火箭公司宣布正在开发中大型可重复使用运载火箭，使用了类似 Starship 的用两根金属筷子夹住火箭的回收技术；北京宇石空间也表示在研发甲烷燃料火箭，使用类似夹火箭回收技术，它对于模仿 SpaceX 的火箭毫不掩饰，称其技术方案与马斯克的 SpaceX 公司完全一致；北京领航星箭公司将其正在研发的火箭命名为星舰一号。这些火箭初创企业能否成功研制出缩小版 Starship 还是未知之数。

德州检察长 Ken Paxton 起诉五大制造商三星、LG、索尼、海信和 TCL，指控未经同意监视用户。新闻稿指控五大电视制造商使用 Automated Content Recognition(‘ACR’) 技术非法收集用户个人数据，称 ACR 像一个不请自来的隐形数字入侵者。这种软件能每 500 毫秒对电视屏幕进行截图，实时监控观看活动，在用户不知情或未经同意下将这些信息传输回公司。然后出售消费者信息获利，这些信息被用于跨平台投放定向广告。该技术将用户隐私和敏感信息如密码和银行信息置于风险之中。Paxton 的新闻稿还指出，海信和 TCL 总部在中国，称两家公司的中国关系引发了对消费者数据收集的严重担忧。

英国气象局宣布 2025 年是自 1910 年有记录以来英国阳光最充足的一年。2025 年还有两周结束，但已记录到 1622 小时的日照时间，打破了 2003 年创下的纪录。除 2 月和 10 月外，所有月份日照时间高于平均水平。气候变化正以多种方式影响天气——气温升高、冬季更潮湿、夏季更干燥——但气候变化与日照时长之间的联系仍然不明确。英国气象局表示这一趋势的原因尚不明确。