OrangeBot.AI Digest — 2026-02-27

The construction of World Models capable of learning, simulating, and reasoning about objective physical laws constitutes a foundational challenge in the pursuit of Artificial General Intelligence. Recent advancements represented by video generation models like Sora have demonstrated the potential of data-driven scaling laws to approximate physical dynamics, while the emerging Unified Multimodal Model (UMM) offers a promising architectural paradigm for integrating perception, language, and reasoning. Despite these advances, the field still lacks a principled theoretical framework that defines the essential properties requisite for a General World Model. In this paper, we propose that a World Model must be grounded in the Trinity of Consistency: Modal Consistency as the semantic interface, Spatial Consistency as the geometric basis, and Temporal Consistency as the causal engine. Through this tripartite lens, we systematically review the evolution of multimodal learning, revealing a trajectory from loosely coupled specialized modules toward unified architectures that enable the synergistic emergence of internal world simulators. To complement this conceptual framework, we introduce CoW-Bench, a benchmark centered on multi-frame reasoning and generation scenarios. CoW-Bench evaluates both video generation models and UMMs under a unified evaluation protocol. Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.

As Large Multimodal Models (LMMs) scale up and reinforcement learning (RL) methods mature, LMMs have made notable progress in complex reasoning and decision making. Yet training still relies on static data and fixed recipes, making it difficult to diagnose capability blind spots or provide dynamic, targeted reinforcement. Motivated by findings that test driven error exposure and feedback based correction outperform repetitive practice, we propose Diagnostic-driven Progressive Evolution (DPE), a spiral loop where diagnosis steers data generation and reinforcement, and each iteration re-diagnoses the updated model to drive the next round of targeted improvement. DPE has two key components. First, multiple agents annotate and quality control massive unlabeled multimodal data, using tools such as web search and image editing to produce diverse, realistic samples. Second, DPE attributes failures to specific weaknesses, dynamically adjusts the data mixture, and guides agents to generate weakness focused data for targeted reinforcement. Experiments on Qwen3-VL-8B-Instruct and Qwen2.5-VL-7B-Instruct show stable, continual gains across eleven benchmarks, indicating DPE as a scalable paradigm for continual LMM training under open task distributions. Our code, models, and data are publicly available at https://github.com/hongruijia/DPE.

Route-planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through natural language interaction and tool-mediated decision making. However, systematic evaluation in real-world mobility settings is hindered by diverse routing demands, non-deterministic mapping services, and limited reproducibility. In this study, we introduce MobilityBench, a scalable benchmark for evaluating LLM-based route-planning agents in real-world mobility scenarios. MobilityBench is constructed from large-scale, anonymized real user queries collected from Amap and covers a broad spectrum of route-planning intents across multiple cities worldwide. To enable reproducible, end-to-end evaluation, we design a deterministic API-replay sandbox that eliminates environmental variance from live services. We further propose a multi-dimensional evaluation protocol centered on outcome validity, complemented by assessments of instruction understanding, planning, tool use, and efficiency. Using MobilityBench, we evaluate multiple LLM-based route-planning agents across diverse real-world mobility scenarios and provide an in-depth analysis of their behaviors and performance. Our findings reveal that current models perform competently on Basic information retrieval and Route Planning tasks, yet struggle considerably with Preference-Constrained Route Planning, underscoring significant room for improvement in personalized mobility applications. We publicly release the benchmark data, evaluation toolkit, and documentation at https://github.com/AMAP-ML/MobilityBench .

Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world. However, current multi-modal LLMs are primarily confined to bi-modal interactions (e.g., vision-language), lacking the unified cognitive capabilities required for general AI assistants. To bridge this gap, we introduce OmniGAIA, a comprehensive benchmark designed to evaluate omni-modal agents on tasks necessitating deep reasoning and multi-turn tool execution across video, audio, and image modalities. Constructed via a novel omni-modal event graph approach, OmniGAIA synthesizes complex, multi-hop queries derived from real-world data that require cross-modal reasoning and external tool integration. Furthermore, we propose OmniAtlas, a native omni-modal foundation agent under tool-integrated reasoning paradigm with active omni-modal perception. Trained on trajectories synthesized via a hindsight-guided tree exploration strategy and OmniDPO for fine-grained error correction, OmniAtlas effectively enhances the tool-use capabilities of existing open-source models. This work marks a step towards next-generation native omni-modal AI assistants for real-world scenarios.

Latent visual reasoning aims to mimic human's imagination process by meditating through hidden states of Multimodal Large Language Models. While recognized as a promising paradigm for visual reasoning, the underlying mechanisms driving its effectiveness remain unclear. Motivated to demystify the true source of its efficacy, we investigate the validity of latent reasoning using Causal Mediation Analysis. We model the process as a causal chain: the input as the treatment, the latent tokens as the mediator, and the final answer as the outcome. Our findings uncover two critical disconnections: (a) Input-Latent Disconnect: dramatic perturbations on the input result in negligible changes to the latent tokens, suggesting that latent tokens do not effectively attend to the input sequence. (b) Latent-Answer Disconnect: perturbations on the latent tokens yield minimal impact on the final answer, indicating the limited causal effect latent tokens imposing on the outcome. Furthermore, extensive probing analysis reveals that latent tokens encode limited visual information and exhibit high similarity. Consequently, we challenge the necessity of latent reasoning and propose a straightforward alternative named CapImagine, which teaches the model to explicitly imagine using text. Experiments on vision-centric benchmarks show that CapImagine significantly outperforms complex latent-space baselines, highlighting the superior potential of visual reasoning through explicit imagination.

Exploration remains the key bottleneck for large language model agents trained with reinforcement learning. While prior methods exploit pretrained knowledge, they fail in environments requiring the discovery of novel states. We propose Exploratory Memory-Augmented On- and Off-Policy Optimization (EMPO^2), a hybrid RL framework that leverages memory for exploration and combines on- and off-policy updates to make LLMs perform well with memory while also ensuring robustness without it. On ScienceWorld and WebShop, EMPO^2 achieves 128.6% and 11.3% improvements over GRPO, respectively. Moreover, in out-of-distribution tests, EMPO^2 demonstrates superior adaptability to new tasks, requiring only a few trials with memory and no parameter updates. These results highlight EMPO^2 as a promising framework for building more exploratory and generalizable LLM-based agents.

While Multi-Agent Systems (MAS) excel in complex reasoning, they suffer from the cascading impact of erroneous information generated by individual participants. Current solutions often resort to rigid structural engineering or expensive fine-tuning, limiting their deployability and adaptability. We propose AgentDropoutV2, a test-time rectify-or-reject pruning framework designed to dynamically optimize MAS information flow without retraining. Our approach acts as an active firewall, intercepting agent outputs and employing a retrieval-augmented rectifier to iteratively correct errors based on a failure-driven indicator pool. This mechanism allows for the precise identification of potential errors using distilled failure patterns as prior knowledge. Irreparable outputs are subsequently pruned to prevent error propagation, while a fallback strategy preserves system integrity. Empirical results on extensive math benchmarks show that AgentDropoutV2 significantly boosts the MAS's task performance, achieving an average accuracy gain of 6.3 percentage points on math benchmarks. Furthermore, the system exhibits robust generalization and adaptivity, dynamically modulating rectification efforts based on task difficulty while leveraging context-aware indicators to resolve a wide spectrum of error patterns. Our code and dataset are released at https://github.com/TonySY2/AgentDropoutV2.

Recent deep research agents primarily improve performance by scaling reasoning depth, but this leads to high inference cost and latency in search-intensive scenarios. Moreover, generalization across heterogeneous research settings remains challenging. In this work, we propose Search More, Think Less (SMTL), a framework for long-horizon agentic search that targets both efficiency and generalization. SMTL replaces sequential reasoning with parallel evidence acquisition, enabling efficient context management under constrained context budgets. To support generalization across task types, we further introduce a unified data synthesis pipeline that constructs search tasks spanning both deterministic question answering and open-ended research scenarios with task appropriate evaluation metrics. We train an end-to-end agent using supervised fine-tuning and reinforcement learning, achieving strong and often state of the art performance across benchmarks including BrowseComp (48.6\%), GAIA (75.7\%), Xbench (82.0\%), and DeepResearch Bench (45.9\%). Compared to Mirothinker-v1.0, SMTL with maximum 100 interaction steps reduces the average number of reasoning steps on BrowseComp by 70.7\%, while improving accuracy.

We introduce MediX-R1, an open-ended Reinforcement Learning (RL) framework for medical multimodal large language models (MLLMs) that enables clinically grounded, free-form answers beyond multiple-choice formats. MediX-R1 fine-tunes a baseline vision-language backbone with Group Based RL and a composite reward tailored for medical reasoning: an LLM-based accuracy reward that judges semantic correctness with a strict YES/NO decision, a medical embedding-based semantic reward to capture paraphrases and terminology variants, and lightweight format and modality rewards that enforce interpretable reasoning and modality recognition. This multi-signal design provides stable, informative feedback for open-ended outputs where traditional verifiable or MCQ-only rewards fall short. To measure progress, we propose a unified evaluation framework for both text-only and image+text tasks that uses a Reference-based LLM-as-judge in place of brittle string-overlap metrics, capturing semantic correctness, reasoning, and contextual alignment. Despite using only sim51K instruction examples, MediX-R1 achieves excellent results across standard medical LLM (text-only) and VLM (image + text) benchmarks, outperforming strong open-source baselines and delivering particularly large gains on open-ended clinical tasks. Our results demonstrate that open-ended RL with comprehensive reward signals and LLM-based evaluation is a practical path toward reliable medical reasoning in multimodal models. Our trained models, curated datasets and source code are available at https://medix.cvmbzuai.com

We present a scalable 3D reconstruction model that addresses a critical limitation in offline feed-forward methods: their computational and memory requirements grow quadratically w.r.t. the number of input images. Our approach is built on the key insight that this bottleneck stems from the varying-length Key-Value (KV) space representation of scene geometry, which we distill into a fixed-size Multi-Layer Perceptron (MLP) via test-time training. VGG-T^3 (Visual Geometry Grounded Test Time Training) scales linearly w.r.t. the number of input views, similar to online models, and reconstructs a 1k image collection in just 54 seconds, achieving a 11.6times speed-up over baselines that rely on softmax attention. Since our method retains global scene aggregation capability, our point map reconstruction error outperforming other linear-time methods by large margins. Finally, we demonstrate visual localization capabilities of our model by querying the scene representation with unseen images.

Diffusion models have achieved remarkable progress in high-fidelity image, video, and audio generation, yet inference remains computationally expensive. Nevertheless, current diffusion acceleration methods based on distributed parallelism suffer from noticeable generation artifacts and fail to achieve substantial acceleration proportional to the number of GPUs. Therefore, we propose a hybrid parallelism framework that combines a novel data parallel strategy, condition-based partitioning, with an optimal pipeline scheduling method, adaptive parallelism switching, to reduce generation latency and achieve high generation quality in conditional diffusion models. The key ideas are to (i) leverage the conditional and unconditional denoising paths as a new data-partitioning perspective and (ii) adaptively enable optimal pipeline parallelism according to the denoising discrepancy between these two paths. Our framework achieves 2.31times and 2.07times latency reductions on SDXL and SD3, respectively, using two NVIDIA RTX~3090 GPUs, while preserving image quality. This result confirms the generality of our approach across U-Net-based diffusion models and DiT-based flow-matching architectures. Our approach also outperforms existing methods in acceleration under high-resolution synthesis settings. Code is available at https://github.com/kaist-dmlab/Hybridiff.

Human behaviors in the real world naturally encode rich, long-term contextual information that can be leveraged to train embodied agents for perception, understanding, and acting. However, existing capture systems typically rely on costly studio setups and wearable devices, limiting the large-scale collection of scene-conditioned human motion data in the wild. To address this, we propose EmbodMocap, a portable and affordable data collection pipeline using two moving iPhones. Our key idea is to jointly calibrate dual RGB-D sequences to reconstruct both humans and scenes within a unified metric world coordinate frame. The proposed method allows metric-scale and scene-consistent capture in everyday environments without static cameras or markers, bridging human motion and scene geometry seamlessly. Compared with optical capture ground truth, we demonstrate that the dual-view setting exhibits a remarkable ability to mitigate depth ambiguity, achieving superior alignment and reconstruction performance over single iphone or monocular models. Based on the collected data, we empower three embodied AI tasks: monocular human-scene-reconstruction, where we fine-tune on feedforward models that output metric-scale, world-space aligned humans and scenes; physics-based character animation, where we prove our data could be used to scale human-object interaction skills and scene-aware motion tracking; and robot motion control, where we train a humanoid robot via sim-to-real RL to replicate human motions depicted in videos. Experimental results validate the effectiveness of our pipeline and its contributions towards advancing embodied AI research.

Rigorously evaluating machine intelligence against the broad spectrum of human general intelligence has become increasingly important and challenging in this era of rapid technological advance. Conventional AI benchmarks typically assess only narrow capabilities in a limited range of human activity. Most are also static, quickly saturating as developers explicitly or implicitly optimize for them. We propose that a more promising way to evaluate human-like general intelligence in AI systems is through a particularly strong form of general game playing: studying how and how well they play and learn to play all conceivable human games, in comparison to human players with the same level of experience, time, or other resources. We define a "human game" to be a game designed by humans for humans, and argue for the evaluative suitability of this space of all such games people can imagine and enjoy -- the "Multiverse of Human Games". Taking a first step towards this vision, we introduce the AI GameStore, a scalable and open-ended platform that uses LLMs with humans-in-the-loop to synthesize new representative human games, by automatically sourcing and adapting standardized and containerized variants of game environments from popular human digital gaming platforms. As a proof of concept, we generated 100 such games based on the top charts of Apple App Store and Steam, and evaluated seven frontier vision-language models (VLMs) on short episodes of play. The best models achieved less than 10\% of the human average score on the majority of the games, and especially struggled with games that challenge world-model learning, memory and planning. We conclude with a set of next steps for building out the AI GameStore as a practical way to measure and drive progress toward human-like general intelligence in machines.

Recent advances in motion diffusion models have substantially improved the realism of human motion synthesis. However, existing approaches either rely on full-sequence diffusion models with bidirectional generation, which limits temporal causality and real-time applicability, or autoregressive models that suffer from instability and cumulative errors. In this work, we present Causal Motion Diffusion Models (CMDM), a unified framework for autoregressive motion generation based on a causal diffusion transformer that operates in a semantically aligned latent space. CMDM builds upon a Motion-Language-Aligned Causal VAE (MAC-VAE), which encodes motion sequences into temporally causal latent representations. On top of this latent representation, an autoregressive diffusion transformer is trained using causal diffusion forcing to perform temporally ordered denoising across motion frames. To achieve fast inference, we introduce a frame-wise sampling schedule with causal uncertainty, where each subsequent frame is predicted from partially denoised previous frames. The resulting framework supports high-quality text-to-motion generation, streaming synthesis, and long-horizon motion generation at interactive rates. Experiments on HumanML3D and SnapMoGen demonstrate that CMDM outperforms existing diffusion and autoregressive models in both semantic fidelity and temporal smoothness, while substantially reducing inference latency.

Open-vocabulary segmentation (OVS) extends the zero-shot recognition capabilities of vision-language models (VLMs) to pixel-level prediction, enabling segmentation of arbitrary categories specified by text prompts. Despite recent progress, OVS lags behind fully supervised approaches due to two challenges: the coarse image-level supervision used to train VLMs and the semantic ambiguity of natural language. We address these limitations by introducing a few-shot setting that augments textual prompts with a support set of pixel-annotated images. Building on this, we propose a retrieval-augmented test-time adapter that learns a lightweight, per-image classifier by fusing textual and visual support features. Unlike prior methods relying on late, hand-crafted fusion, our approach performs learned, per-query fusion, achieving stronger synergy between modalities. The method supports continually expanding support sets, and applies to fine-grained tasks such as personalized segmentation. Experiments show that we significantly narrow the gap between zero-shot and supervised segmentation while preserving open-vocabulary ability.

根据 IDC 的预测，因内存短缺 2026 年智能手机出货量预计下滑 12.9% 至 11 亿部。内存和 SSD 短缺对面向中低端市场的 Android 手机厂商影响最大，它们本就利润微薄，零部件成本的不断上涨将挤压利润空间，迫使其将成本转嫁给消费者。大厂如苹果和三星预计能安然度过这场危机，甚至还可能扩大市场份额。IDC 认为智能手机行业的小厂将会退出市场，行业整合将会出现，低端厂商面临供应受限和需求下降的双重压力，出货量将大幅下滑。在智能手机出货量将创历史新低的同时，其平均售价预计今年将上涨 14% 达到创纪录的 523 美元。对厂商和消费者而言，回到过去不再可能。

汉堡王（Burger King）即将推出内置于员工所佩戴耳机中的 AI 语音机器人 Patty。Patty 是 BK Assistant 平台的一部分，不仅能协助员工备餐，还能评估他们与顾客互动时的“友好度”。汉堡王首席数字官 Thibault Roux 表示，他们根据收集的相关数据训练 AI 系统识别特定词语和短语，如 welcome to Burger King、please 和 thank you。经理之后可以询问 AI 系统门店员工的友好度表现。Roux 表示 AI 是辅助员工的培训工具，公司会不断迭代 AI 助手以更好的捕捉对话语气。

根据发表在《科学》期刊上的一项研究，当尼安德特人与远古现代人发生杂交时，配对双方大多是尼安德特人男性与现代人女性。这一发现有助于解释存在于大多数现代人体内的尼安德特人血统为何分布不均。尼安德特人基因含量异常稀少的现象在 X 染色体上尤为明显。这种现象有两种解释：要么是 X 染色体上的尼安德特人的基因变异对现代人不利，导致因自然选择而逐渐淘汰；或是早期的杂交主要发生在男性尼安德特人与女性现代人之间，导致极少的尼安德特人的 X 染色体 DNA 进入现代人的基因库。研究人员追溯了古代基因的流动模式，揭示尼安德特人 X 染色体中的现代人血统的相对过剩量达 62%，表明杂交主要发生在男性尼安德特人与女性现代人之间。

在派拉蒙再次提高收购报价之后，Netflix 退出了竞价，放弃了收购华纳（Warner Bros Discovery）的计划。华纳去年宣布了出售计划，该公司本周四表示派拉蒙的最新报价是比 Netflix 更优的方案，给 Netflix 几天时间匹配新报价。Netflix 在数小时后就宣布放弃，称溢价让这笔交易不再具有财务吸引力。派拉蒙此前的报价是每股 30 美元，最新报价提高 1 美元至 31 美元，且还同意如果交易失败将支付 70 亿美元。

微软释出了可选更新 KB5077241，正式向 Windows 11 v25H2 和 24H2 用户提供了知名系统监控工具 Sysmon。Sysmon 是 Sysinternals 套件的一部分，它能监控系统活动，记录到事件日志中。它能提供进程创建、网络连接以及文件创建时间变化的详细信息，能用于识别恶意或异常活动。如果用户已经安装了 Sysmon，需要先卸载，安装后用管理员权限运行 sysmon -i 命令启用该功能。

在建国 250 周年之际，移民目标之国正首次变成人口迁出之国。2025 年美国经历了大萧条以来首次迁出人口超过迁入人口。特朗普政府则视人口外流为其一大政绩。布鲁金斯学会估计 2025 年美国人口流失约 15 万人。而 2025 年的外来移民总人数从 2023 年的接近 600 万降至 260-270 万。WSJ 分析了公布了部分或完整 2025 年数据的 15 个国家，发现至少 18 万美国人移居到这些国家。根据美国官方的数据，有 400-900 万美国人居住在海外，2022 年有 160 万美国人居住在墨西哥，逾 25 万人居住在加拿大，逾 32.5 万人居住在英国，居住在欧洲的美国人则超过 150 万。爱尔兰在 2025 年新增美国移民超过 1 万，移居德国的美国人超过了移居美国的德国人。以前移居海外的美国人被认为是富有冒险精神的人，但如今离开美国的很多都是普通居民。移民德国的 Chris Ford 表示，美国薪水更高，但欧洲生活质量更高，幼儿园儿童不再需要参如何应对枪手袭击的演习了。美国上一次人口净流出超过净流入是在 1935 年，当时正值大萧条，很多美国人去苏联寻求工作。

日本厚生劳动省公布的人口动态统计（初值）显示，包含外国人的 2025 年新生儿数为 705,809 人，跌至 1899 年开始统计以来的新低。较上年减少 2.1%（15,179人）。这是连续 10 年刷新最少纪录，反映了少子化的加剧。减幅较上年有所收窄。死亡人数减去出生人数所得的人口“自然减少”为 899,845 人，创历史新高，人口减少势头未得到抑制。

Nala Ginrut 写道：版权之所以具有商业价值，并不单纯因为“法律授予”，而是因为它通常标记着一段真实发生过的创造成本。它是对投入、试错、风险承担的一种制度化确认。当创造成本真实存在，版权自然有价值；当创造过程被外包给生成模型，而人类没有承担验证与责任的成本，版权就可能变成空壳。

纽约州总检察长 Letitia James 提起诉讼，指控 Valve 的战利品箱系统助长非法赌博，导致儿童成瘾。Valve 开发了热门网络游戏《反恐精英》、《军团要塞》和《Dota 2》，这些都是免费游戏，通过微交易和战利品箱获利。诉状指控 Valve 的战利品箱系统本质上是赌博，违反了纽约的州宪法和州刑法。Letitia James 称，Valve 通过出售开启战利品箱的钥匙赚取了数十亿美元的收入，其中一款游戏的开锁过程类似老虎机，转盘旋转会显示各种物品。诉状称 Valve 的战利品箱尤其有害，因为它们在儿童和青少年中很受欢迎。纽约州寻求对 Valve 处以其非法所得三倍的罚款。

DeepSeek 未向英伟达和 AMD 提供其下一代 V4 模型进行测试，此举打破了行业惯例。与此同时 DeepSeek 向华为等国内公司提供了新模型进行测试。AI 公司通常会与主要 AI 芯片制造商如英伟达和 AMD 分享模型的预发布版本，以确保其软件在广泛使用的硬件上高效运行。DeepSeek 此前曾与英伟达的技术人员密切合作，但 DeepSeek 即将推出的模型新版本未提供给英伟达。

篮球鞋在光滑球场上滑动时会发出的“嘎吱”声，根据发表在《自然》期刊上的一项研究，这种声音源于软质材料表面的波浪状形变。哈佛大学研究人员拍摄了篮球鞋与光滑玻璃板接触时发出的嘎吱声，通过高速成像捕捉到橡胶鞋底在表面脉冲式爆发变形的过程。他们发现，嘎吱声的音调与脉冲频率相匹配，而频率由鞋底的硬度和厚度决定。他们还发现，若柔软表面光滑，脉冲则呈不规则分布且不会产生尖锐声响；而带纹理的表面（如运动鞋的防滑纹路）能产生稳定的脉冲频率，从而形成高音调的嘎吱声。

管理办公软件 LibreOffice 项目的基金会 The Document Foundation（TDF）宣布重启 Web 版本 LibreOffice Online。LibreOffice Online（LOOL）可以托管在任何人的基础设施上，但因为 TDF 与主要商业合作伙伴 Collabora 之间的紧张关系而于 2022 年暂停开发。Collabora 为 LibreOffice 以及 LOOL 项目贡献了大量代码，该公司希望通过基于 LibreOffice 的 Collabora Office 以及 Web 版 Collabora Online 获得商业收入支持开发，它认为完全免费的 LOOL 影响其收入，因此于 2020 年底撤回了在 LOOL 项目上工作的开发者，专注于开发 Collabora Online。LOOL 的开发因此陷入了停滞，TDF 只能搁置该项目，如今重启该项目，可能会再次加剧与 Collabora 的紧张关系。

韩国国家数据处发布的统计数据显示，韩国去年出生人口为 25.45 万人，同比增加 1.61 万人（6.8%），继2024年后连续两年保持增势。韩国 2025 年总和生育率为 0.8，较前年的 0.75 增加 0.05，为近四年最高水平。总和生育率是指一名育龄妇女一生中平均生育子女数。该指标自 2015 年的 1.24 持续下滑，至 2023 年跌至 0.72，2024年 首次止跌回升至 0.75。分析认为，出生人口增加主要受婚姻登记累计增加、生育年龄段人口增加以及生育观念变化等因素影响。

随着 Z 世代再次青睐物理媒介，过去几年销量大幅下滑的 DVD 和蓝光光盘的销量出现反弹，下滑速度显著放缓。Digital Entertainment Group 的数据显示，去年光盘总销量下滑了 9%，而 2023 年和 2024 年的降幅均超过 20%。美国消费者在 2025 年购买 4K 蓝光光盘上的支出比上一年增长 12%。蓝光光盘发行商 Criterion Collection 认为这一趋势要归功于年轻一代对物理媒介的青睐。洛杉矶光盘租赁店 Vidiots 在 2026 年 1 月平均每天出租 170 张光盘，创历史新高；该店在 2023 年共出租了约 22,000 张光盘，2024 年出租了约 50,000 张。

日本公平交易委员会以美国微软（MS）涉嫌在其他公司的云服务中对使用“微软365”等该公司软件的企业征收高额使用费、妨碍了云市场竞争为由，25 日以违反《反垄断法》嫌疑启动了对微软的审查。当天，对其东京的日本法人进行了入内检查。云服务使企业和个人即便没有自己的服务器和设备，也可通过互联网利用软件或保存数据，近年来市场急速扩大。亚马逊、微软、谷歌正在全球争夺市场份额，公平交易委认为微软有可能利用在软件市场的优势，在云市场也试图揽客，因此将展开调查。