OrangeBot.AI Digest — 2026-01-22

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An autonomous agent for deep financial research

Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, they struggle in open-ended and dynamic environments. Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction. In this survey, we organize agentic reasoning along three complementary dimensions. First, we characterize environmental dynamics through three layers: foundational agentic reasoning, which establishes core single-agent capabilities including planning, tool use, and search in stable environments; self-evolving agentic reasoning, which studies how agents refine these capabilities through feedback, memory, and adaptation; and collective multi-agent reasoning, which extends intelligence to collaborative settings involving coordination, knowledge sharing, and shared goals. Across these layers, we distinguish in-context reasoning, which scales test-time interaction through structured orchestration, from post-training reasoning, which optimizes behaviors via reinforcement learning and supervised fine-tuning. We further review representative agentic reasoning frameworks across real-world applications and benchmarks, including science, robotics, healthcare, autonomous research, and mathematics. This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance for real-world deployment.

Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA, missing end-to-end multimodal evidence use. We introduce MMDeepResearch-Bench (MMDR-Bench), a benchmark of 140 expert-crafted tasks across 21 domains, where each task provides an image-text bundle to evaluate multimodal understanding and citation-grounded report generation. Compared to prior setups, MMDR-Bench emphasizes report-style synthesis with explicit evidence use, where models must connect visual artifacts to sourced claims and maintain consistency across narrative, citations, and visual references. We further propose a unified, interpretable evaluation pipeline: Formula-LLM Adaptive Evaluation (FLAE) for report quality, Trustworthy Retrieval-Aligned Citation Evaluation (TRACE) for citation-grounded evidence alignment, and Multimodal Support-Aligned Integrity Check (MOSAIC) for text-visual integrity, each producing fine-grained signals that support error diagnosis beyond a single overall score. Experiments across 25 state-of-the-art models reveal systematic trade-offs between generation quality, citation discipline, and multimodal grounding, highlighting that strong prose alone does not guarantee faithful evidence use and that multimodal integrity remains a key bottleneck for deep research agents.

Video generation models have significantly advanced embodied intelligence, unlocking new possibilities for generating diverse robot data that capture perception, reasoning, and action in the physical world. However, synthesizing high-quality videos that accurately reflect real-world robotic interactions remains challenging, and the lack of a standardized benchmark limits fair comparisons and progress. To address this gap, we introduce a comprehensive robotics benchmark, RBench, designed to evaluate robot-oriented video generation across five task domains and four distinct embodiments. It assesses both task-level correctness and visual fidelity through reproducible sub-metrics, including structural consistency, physical plausibility, and action completeness. Evaluation of 25 representative models highlights significant deficiencies in generating physically realistic robot behaviors. Furthermore, the benchmark achieves a Spearman correlation coefficient of 0.96 with human evaluations, validating its effectiveness. While RBench provides the necessary lens to identify these deficiencies, achieving physical realism requires moving beyond evaluation to address the critical shortage of high-quality training data. Driven by these insights, we introduce a refined four-stage data pipeline, resulting in RoVid-X, the largest open-source robotic dataset for video generation with 4 million annotated video clips, covering thousands of tasks and enriched with comprehensive physical property annotations. Collectively, this synergistic ecosystem of evaluation and data establishes a robust foundation for rigorous assessment and scalable training of video models, accelerating the evolution of embodied AI toward general intelligence.

Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Current solutions typically treat this as a direct-to-text generation problem, suffering from hallucination, overlooked critiques, and a lack of verifiable grounding. To address these limitations, we introduce RebuttalAgent, the first multi-agents framework that reframes rebuttal generation as an evidence-centric planning task. Our system decomposes complex feedback into atomic concerns and dynamically constructs hybrid contexts by synthesizing compressed summaries with high-fidelity text while integrating an autonomous and on-demand external search module to resolve concerns requiring outside literature. By generating an inspectable response plan before drafting, RebuttalAgent ensures that every argument is explicitly anchored in internal or external evidence. We validate our approach on the proposed RebuttalBench and demonstrate that our pipeline outperforms strong baselines in coverage, faithfulness, and strategic coherence, offering a transparent and controllable assistant for the peer review process. Code will be released.

Reinforcement learning (RL) is central to post-training, particularly for agentic models that require specialized reasoning behaviors. In this setting, model merging offers a practical mechanism for integrating multiple RL-trained agents from different tasks into a single generalist model. However, existing merging methods are designed for supervised fine-tuning (SFT), and they are suboptimal to preserve task-specific capabilities on RL-trained agentic models. The root is a task-vector mismatch between RL and SFT: on-policy RL induces task vectors that are highly sparse and heterogeneous, whereas SFT-style merging implicitly assumes dense and globally comparable task vectors. When standard global averaging is applied under this mismatch, RL's non-overlapping task vectors that encode critical task-specific behaviors are reduced and parameter updates are diluted. To address this issue, we propose Reinforced Agent Merging (RAM), a distribution-aware merging framework explicitly designed for RL-trained agentic models. RAM disentangles shared and task-specific unique parameter updates, averaging shared components while selectively preserving and rescaling unique ones to counteract parameter update dilution. Experiments across multiple agent domains and model architectures demonstrate that RAM not only surpasses merging baselines, but also unlocks synergistic potential among agents to achieve performance superior to that of specialized agents in their domains.

Chain-of-Thought (CoT) prompting has achieved remarkable success in unlocking the reasoning capabilities of Large Language Models (LLMs). Although CoT prompting enhances reasoning, its verbosity imposes substantial computational overhead. Recent works often focus exclusively on outcome alignment and lack supervision on the intermediate reasoning process. These deficiencies obscure the analyzability of the latent reasoning chain. To address these challenges, we introduce Render-of-Thought (RoT), the first framework to reify the reasoning chain by rendering textual steps into images, making the latent rationale explicit and traceable. Specifically, we leverage the vision encoders of existing Vision Language Models (VLMs) as semantic anchors to align the vision embeddings with the textual space. This design ensures plug-and-play implementation without incurring additional pre-training overhead. Extensive experiments on mathematical and logical reasoning benchmarks demonstrate that our method achieves 3-4x token compression and substantial inference acceleration compared to explicit CoT. Furthermore, it maintains competitive performance against other methods, validating the feasibility of this paradigm. Our code is available at https://github.com/TencentBAC/RoT

GutenOCR is a family of grounded OCR front-ends obtained by fine-tuning Qwen2.5-VL-3B and Qwen2.5-VL-7B. The resulting single-checkpoint vision-language models expose reading, detection, and grounding through a unified, prompt-based interface. Trained on business documents, scientific articles, and synthetic grounding data, the models support full-page and localized reading with line- and paragraph-level bounding boxes and conditional ``where is x?'' queries. We introduce a grounded OCR evaluation protocol and show that GutenOCR-7B more than doubles the composite grounded OCR score of its Qwen2.5-VL-7B backbone on 10.5K held-out business and scientific pages (0.40 to 0.82). On Fox and OmniDocBench v1.5, our approach substantially improves region- and line-level OCR as well as text-detection recall, but reveals trade-offs in page-level linearization, color-guided OCR, and formula-heavy layouts.

Document extraction is a core component of digital workflows, yet existing vision-language models (VLMs) predominantly favor high-resource languages. Thai presents additional challenges due to script complexity from non-latin letters, the absence of explicit word boundaries, and the prevalence of highly unstructured real-world documents, limiting the effectiveness of current open-source models. This paper presents Typhoon OCR, an open VLM for document extraction tailored for Thai and English. The model is fine-tuned from vision-language backbones using a Thai-focused training dataset. The dataset is developed using a multi-stage data construction pipeline that combines traditional OCR, VLM-based restructuring, and curated synthetic data. Typhoon OCR is a unified framework capable of text transcription, layout reconstruction, and document-level structural consistency. The latest iteration of our model, Typhoon OCR V1.5, is a compact and inference-efficient model designed to reduce reliance on metadata and simplify deployment. Comprehensive evaluations across diverse Thai document categories, including financial reports, government forms, books, infographics, and handwritten documents, show that Typhoon OCR achieves performance comparable to or exceeding larger frontier proprietary models, despite substantially lower computational cost. The results demonstrate that open vision-language OCR models can achieve accurate text extraction and layout reconstruction for Thai documents, reaching performance comparable to proprietary systems while remaining lightweight and deployable.

Large encoder-decoder models like Whisper achieve strong offline transcription but remain impractical for streaming applications due to high latency. However, due to the accessibility of pre-trained checkpoints, the open Thai ASR landscape remains dominated by these offline architectures, leaving a critical gap in efficient streaming solutions. We present Typhoon ASR Real-time, a 115M-parameter FastConformer-Transducer model for low-latency Thai speech recognition. We demonstrate that rigorous text normalization can match the impact of model scaling: our compact model achieves a 45x reduction in computational cost compared to Whisper Large-v3 while delivering comparable accuracy. Our normalization pipeline resolves systemic ambiguities in Thai transcription --including context-dependent number verbalization and repetition markers (mai yamok) --creating consistent training targets. We further introduce a two-stage curriculum learning approach for Isan (north-eastern) dialect adaptation that preserves Central Thai performance. To address reproducibility challenges in Thai ASR, we release the Typhoon ASR Benchmark, a gold-standard human-labeled datasets with transcriptions following established Thai linguistic conventions, providing standardized evaluation protocols for the research community.

Agentic systems have recently become the dominant paradigm for formal theorem proving, achieving strong performance by coordinating multiple models and tools. However, existing approaches often rely on task-specific pipelines and trained formal provers, limiting their flexibility and reproducibility. In this paper, we propose the paradigm that directly uses a general coding agent as a formal math reasoner. This paradigm is motivated by (1) A general coding agent provides a natural interface for diverse reasoning tasks beyond proving, (2) Performance can be improved by simply replacing the underlying base model, without training, and (3) MCP enables flexible extension and autonomous calling of specialized tools, avoiding complex design. Based on this paradigm, we introduce Numina-Lean-Agent, which combines Claude Code with Numina-Lean-MCP to enable autonomous interaction with Lean, retrieval of relevant theorems, informal proving and auxiliary reasoning tools. Using Claude Opus 4.5 as the base model, Numina-Lean-Agent solves all problems in Putnam 2025 (12 / 12), matching the best closed-source system. Beyond benchmark evaluation, we further demonstrate its generality by interacting with mathematicians to successfully formalize the Brascamp-Lieb theorem. We release Numina-Lean-Agent and all solutions at https://github.com/project-numina/numina-lean-agent.

Recent end-to-end spoken dialogue systems leverage speech tokenizers and neural audio codecs to enable LLMs to operate directly on discrete speech representations. However, these models often exhibit limited speaker identity preservation, hindering personalized voice interaction. In this work, we present Chroma 1.0, the first open-source, real-time, end-to-end spoken dialogue model that achieves both low-latency interaction and high-fidelity personalized voice cloning. Chroma achieves sub-second end-to-end latency through an interleaved text-audio token schedule (1:2) that supports streaming generation, while maintaining high-quality personalized voice synthesis across multi-turn conversations. Our experimental results demonstrate that Chroma achieves a 10.96% relative improvement in speaker similarity over the human baseline, with a Real-Time Factor (RTF) of 0.43, while maintaining strong reasoning and dialogue capabilities. Our code and models are publicly available at https://github.com/FlashLabs-AI-Corp/FlashLabs-Chroma and https://huggingface.co/FlashLabs/Chroma-4B .

Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision-making, where their abilities to plan, invoke tools, and manipulate mutable state introduce new security risks in high-stakes and highly regulated financial environments. However, existing safety evaluations largely focus on language-model-level content compliance or abstract agent settings, failing to capture execution-grounded risks arising from real operational workflows and state-changing actions. To bridge this gap, we propose FinVault, the first execution-grounded security benchmark for financial agents, comprising 31 regulatory case-driven sandbox scenarios with state-writable databases and explicit compliance constraints, together with 107 real-world vulnerabilities and 963 test cases that systematically cover prompt injection, jailbreaking, financially adapted attacks, as well as benign inputs for false-positive evaluation. Experimental results reveal that existing defense mechanisms remain ineffective in realistic financial agent settings, with average attack success rates (ASR) still reaching up to 50.0\% on state-of-the-art models and remaining non-negligible even for the most robust systems (ASR 6.7\%), highlighting the limited transferability of current safety designs and the need for stronger financial-specific defenses. Our code can be found at https://github.com/aifinlab/FinVault.

Retrieval is being redefined by agentic AI, demanding multimodal reasoning beyond conventional similarity-based paradigms. Composed Image Retrieval (CIR) exemplifies this shift as each query combines a reference image with textual modifications, requiring compositional understanding across modalities. While embedding-based CIR methods have achieved progress, they remain narrow in perspective, capturing limited cross-modal cues and lacking semantic reasoning. To address these limitations, we introduce XR, a training-free multi-agent framework that reframes retrieval as a progressively coordinated reasoning process. It orchestrates three specialized types of agents: imagination agents synthesize target representations through cross-modal generation, similarity agents perform coarse filtering via hybrid matching, and question agents verify factual consistency through targeted reasoning for fine filtering. Through progressive multi-agent coordination, XR iteratively refines retrieval to meet both semantic and visual query constraints, achieving up to a 38% gain over strong training-free and training-based baselines on FashionIQ, CIRR, and CIRCO, while ablations show each agent is essential. Code is available: https://01yzzyu.github.io/xr.github.io/.

We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle patterns in training data can degrade contextual privacy, including optimisation for helpfulness, exposure to user information, emotional and subjective dialogue, and debugging code printing internal variables, among others. Fine-tuned models lose their ability to reason about contextual privacy norms, share information inappropriately with tools, and violate memory boundaries across contexts. Privacy collapse is a ``silent failure'' because models maintain high performance on standard safety and utility benchmarks whilst exhibiting severe privacy vulnerabilities. Our experiments show evidence of privacy collapse across six models (closed and open weight), five fine-tuning datasets (real-world and controlled data), and two task categories (agentic and memory-based). Our mechanistic analysis reveals that privacy representations are uniquely fragile to fine-tuning, compared to task-relevant features which are preserved. Our results reveal a critical gap in current safety evaluations, in particular for the deployment of specialised agents.

We introduce RoboBrain 2.5, a next-generation embodied AI foundation model that advances general perception, spatial reasoning, and temporal modeling through extensive training on high-quality spatiotemporal supervision. Building upon its predecessor, RoboBrain 2.5 introduces two major capability upgrades. Specifically, it unlocks Precise 3D Spatial Reasoning by shifting from 2D pixel-relative grounding to depth-aware coordinate prediction and absolute metric constraint comprehension, generating complete 3D manipulation traces as ordered keypoint sequences under physical constraints. Complementing this spatial precision, the model establishes Dense Temporal Value Estimation that provides dense, step-aware progress prediction and execution state understanding across varying viewpoints, producing stable feedback signals for downstream learning. Together, these upgrades extend the framework toward more physically grounded and execution-aware embodied intelligence for complex, fine-grained manipulation. The code and checkpoints are available at project website: https://superrobobrain.github.io

气泡网捕食是一种群体捕猎技术，鲸鱼通过喷出气泡来围捕鱼群，然后一起向上猛冲，将鱼吞食。几十年来这种行为一直在美国阿拉斯加水域的座头鲸身上被观察到，研究人员最近开始在加拿大西部峡湾的座头鲸种群中发现了这种行为。研究人员利用 2004 年至 2023 年的野外观测数据，重点研究了生活在加拿大西部基蒂马特峡湾的526 头鲸鱼。研究团队利用每头鲸鱼独有的尾鳍图像来识别它们。数据显示，有 254 头鲸鱼至少进行过一次气泡网捕食，其中约 90% 的捕食行为是在合作环境下进行的。这种行为似乎在 2014 年之后开始增多，这与东北太平洋地区发生的一次重大海洋热浪事件相吻合，该热浪导致猎物数量减少。如果鲸鱼经常与已经使用气泡网捕食技术的同类群体互动，它们就更有可能学会这种捕食方式。气泡网捕食技术可能最初是由从东北太平洋其他地区迁徙而来的鲸鱼引入该地区的，但研究结果主要表明，这种行为是通过当地的社会网络传播开来的，由稳定的群体和具有影响力的个体传播。

内存价格飙升的今天你也别指望 SSD 会便宜。第二大 NAND 制造商铠侠存储器业务总经理中户俊介表示该公司的 2026 年产能已经售罄，企业级和消费级 SSD 市场将进入“高端且昂贵的阶段”。中户称，企业普遍感到一旦停止投资 AI 会被淘汰，因此他们别无选择只能继续投资。”如果对生成式 AI 数据中心的需求没有发生重大变化，那么在可预见的未来，这轮投资将使 SSD 价格居高不下。中户表示铠侠正试图提高产能以满足不断增长的需求。

白俄罗斯业余无线电（HAM Radio）社区紧急呼吁外界关注，白俄罗斯政府对该社区实施了突袭行动，至少逮捕了七人，威胁对其中三人判处死刑，理由是他们窃取国家机密。面临死刑惩罚的三人属于一个有 50 多名成员的业余无线电爱好者网络，他们被控“间谍罪”和“叛国罪”。政府查获了 500 多件无线电设备，国家电视台称他们利用无线电监视政府飞机的行踪，但未提供证据。

伊朗的互联网封锁已长达 14 天。美国网络监测公司 Kentik 根据其收集的数据分析了伊朗的互联网封锁。除了互联网封锁，国际语音通信也被封锁，国内通信服务也长时间中断，对伊朗 9000 万国民而言这是历史上最严重的通信封锁事件。此次大规模封锁的第一个征兆是该国国有电信公司 TIC 的自治系统 AS49666 于 1 月 8 日 11:42 UTC 撤回其 IPv6 BGP 路由，伊朗的 IPv6 流量不到其总流量的不到 1%，对普通用户基本没影响，但它预示之后几小时发生的事。当地时间晚上 7 点（UTC 16:30）伊朗的流量开始暴降，UTC 18:45 伊朗进出流量降至几乎为零。伊朗的 IPv4 路由仍然在线，政府启用了白名单制度，只允许经过批准的用户或服务访问互联网，还有非常少的流量进出伊朗，1 月 9 日 AS6736 还为伊朗大学短暂恢复了数小时联网服务。

根据《Carbon Majors》报告，2024 年 32 家化石燃料公司的排放量占到了全球二氧化碳排放量的一半。沙特阿美是最大的国家所有的污染企业，而埃克森美孚是最大的投资者所有的污染企业。排名前 20 的化石燃料公司中有 17 家是国有企业。沙特阿美排放了 17 亿吨二氧化碳，大部分来自出口的石油。如果沙特阿美是一个国家，它将是全球第五大碳排放国，位于俄罗斯之后。埃克森美孚的化石燃料生产排放了 6.1 亿吨二氧化碳，是第九大污染排放国，排名位于韩国之前。

2011 年的福岛核事故迫使日本关闭了所有核电站，本周三日本东京电力公司重启了世界最大核电站柏崎刈羽核电站 6 号机组反应堆。这是东电在福岛核事故后首次重启核电站。福岛核事故与乌克兰切尔诺贝利核事故同属国际核事件分级表中最严重的7级，许多老年人等在严酷的疏散生活中去世，大量居民被迫离开家园。为筹措报废及赔偿所需资金，东电希望重启柏崎刈羽 6 号和 7 号机组（各 135.6 万千瓦）并向原子能规制委员会提交了审查申请。尽管其运营核电站的资质受到质疑，但东电承诺切实完成报废工作等，最终通过了审查。柏崎刈羽核电站采取了多项措施，包括建设防波堤防备超过预料的海啸、新设可在电源丧失时向反应堆注水的泵机。因反恐设施尚未完工，6 号机组在设置宽限期结束的 2029 年 9 月之后将暂时停运。

GLP-1 减肥药如 Ozempic 正在重塑食品行业，改变了数百万人的生活。它还给航空公司带来了意想不到的好处：降低燃油成本，因为乘客体重的下降也降低了飞机的载重。根据 Jefferies 的一项研究，受益于 GLP-1 减肥药，美国四大航空公司——美国航空、达美航空、西南航空和联合航空——每年可节省高达 5.8 亿美元的燃油成本。根据健康研究组织 KFF 去年 11 月发布的调查，八分之一美国成年人表示在服用 GLP-1。燃油是航空公司最大的支出之一。乘客体重下降带来的燃油成本节省占总成本的 1.5%。投资者也可能从中受益：研究人员估计，飞机重量减轻 2% 可使每股收益提高约 4%。

Palantir CEO Alex Karp 在达沃斯世界经济论坛的一个讨论会上表示，AI 将取代大量工作，让大规模移民变得过时。他认为白领和蓝领的地位将出现翻转，“你们国家的公民将拥有充足的就业机会，尤其是接受过职业技能培训的人。我认为目前的趋势确实让人很难想象，除非具备非常专业的技能，否则为什么还需要大规模移民。”Karp 拥有博士学位，他以自己为例，认为此类“精英”白领正面临极高的被替代风险。他认为职业技能型劳动者将变得更重要，甚至“不可替代”。他批评了将高等教育视为衡量人才价值和就业能力终极标准的观点。Palantir 联合创始人、特朗普的支持者 Peter Thiel 阐述过类似观点。

联合国发表报告《Global Water Bankruptcy: Living Beyond Our Hydrological Means in the Post-Crisis Era》，警告地球进入水资源破产时代，数十亿人受困水资源短缺，呼吁世界各国的领导人采取行动紧急应对水资源过度使用和污染问题。报告基于一系列统计数据：1990 年代以来，世界各地的大型湖泊水量减少五成，而全球四分之一的人口依赖于这些湖泊；50% 的全球生活用水来自地下水；四成以上的灌溉用水来自含水层，但含水层正在枯竭；七成主要含水层长期水量下降；4.1 亿公顷天然湿地面积过去五十年消失；1970 年以来多地的冰川质量损失逾 30%，中低纬度的功能性冰川未来几十年预计将完全消失...导致的结果是：四分之三人口生活在被列为水资源不安全或极度不安全的国家；20 亿人生活在地面下沉的地区；部分城市每年下沉 25 厘米；40 亿人口每年至少有一个月面临严重缺水。

根据本周二发表在《Nature Food》期刊上的一项研究，取消对肉类的补贴或者取消减税，能显著减少碳足迹。食物消费尤其是肉类的生产会带来巨大的环境影响。德国研究人员发现，取消目前对肉类产品的增值税减免，有望将欧盟 27 国家庭食品消费相关的温室气体排放、水资源消耗、土地利用、生物多样性丧失以及氮磷足迹减少 3.5%-5.7%。对所有食品征收每吨二氧化碳当量约 52 欧元的温室气体排放费用，可实现同等的减排效果，同时有更高的环境协同效益。两项政策给每个家庭带来的代价是每年 12-26 欧元。

如果一切顺利，2027 年初 SpaceX 将把 LuSEE-Night 发射到月球背面。LuSEE-Night 代表 ​​Lunar Surface Electromagnetics Experiment–Night，它将利用 Firefly Aerospace 的着陆器 Blue Ghost Mission 2 在月之背面登陆。Firefly 的 Blue Ghost 1 去年 3 月完成了私人公司的首次成功月表着陆。月球射电望远镜有助于科学家解开宇宙中最著名的谜团。月球上将能更清晰的观测暗物质、暗能量、中子星和引力波。地球上的观测设备容易受到干扰，而月球可能是内太阳系最安静的地方。月球背面在长达 14 个地球日里可能是内太阳系最黑暗的电磁区域，没有太阳辐射，也没有来自地球的干扰信号。

为减少 AI Slop 报告数量，cURL 项目将在一月底终止 Bug 悬赏项目。cURL 维护者 Daniel Stenberg 表示，“为避免被拖下去我们必须努力阻止这股洪流。”cURL 是一个广泛使用的互联网基础工具，几乎被每一个联网的设备和系统使用。今年早些时候，cURL 项目披露收到了大量由 AI 生成的虚假漏洞报告，Stenberg 当时称至今没有看到一份 AI 帮助下完成的漏洞报告是有效的。

普华永道（PwC）调查了逾 4500 位 CEO ，发现尽管在 AI 上投入了大量资金，但大部分 CEO 表示 AI 投资未带来收入增长或成本降低。接受调查的 4454 位商界领袖中只有 12% 同时实现了成本降低和收入增长，56% 既没有降低成本也没有增加收入，26% 实现了成本降低，但类似比例的人增加了成本。AI 的普及度仍然有限，即使在需求生成（22%）、支持服务（20%）和产品开发（19%）等热门应用场景中，只有少数企业广泛部署 AI。从更宏观的角度，普华永道报告 CEO 们的信心跌至五年以来的最低点，仅 30% 的 CEO 对营收增长乐观（低于去年的 38%），表明地缘政治风险日益加剧，网络威胁升级，同时 AI 的利弊也存在不确定性。

IPv4 的 40 亿地址空间早就枯竭，但有 2^128 地址空间的 IPv6 普及速度并没有预想的快。一大原因是互联网从点对点架构转向了客户端/服务器架构，而 Network Address Translators (NATs)与此完全嵌合。客户端/服务器架构中，客户端共享一个较小的公共地址池，只在与远程服务器建立活动会话后才使用地址。在 NAT 帮助下逾 300 亿联网设备只使用 30 亿个已通告的 IPv4 地址池。但联网设备的持续增长意味着 NAT 无法完全解决问题，增长压力推动 IPv6 的加快部署。2017 年 IPv6 部署激增是受益于印度部署的移动 IPv6 服务，而中国的 IPv6 服务最近几年也在快速普及，IPv6 用户占比从 2024 年初的 32% 增长到 2025 年底的 54%，意味着两年内中国 IPv6 用户数量增加约 9400 万。但非洲、东欧和南欧以及西亚没有出现 IPv6 的大规模部署。

华硕董事长施崇棠证实该公司将逐步退出智能手机业务，将重心集中到 AI 产品如机器人和智能眼镜上。施崇棠表示，未来不会推出新智能手机机型。但他没有把话完全说死，只是表示公司将采取“无限期观望”态度。华硕的智能手机包括 Zenfone 和 ROG Phone 品牌，前者主打小巧廉价，后者则是主打游戏以高价著称，其价格甚至高于三星的旗舰手机。目前还没有哪家 Android 厂商在停止发布新机型后能重新恢复生产，LG 就是其中的典型代表，该公司在停止推出新机型后最终完全退出手机市场。