OrangeBot.AI Digest — 2026-01-27

AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, vLLM pods

Memory engine and app that is extremely fast, scalable. The Memory API for the AI era.

A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library built on Apple's MLX framework, providing efficient speech analysis on Apple Silicon.

M3U Playlist for free TV channels

A tool for secrets management, encryption as a service, and privileged access management

Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.

Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications. Driven by (i) rising demands for application-ready data (e.g., for analytics, visualization, decision-making), (ii) increasingly powerful LLM techniques, and (iii) the emergence of infrastructures that facilitate flexible agent construction (e.g., using Databricks Unity Catalog), LLM-enhanced methods are rapidly becoming a transformative and potentially dominant paradigm for data preparation. By investigating hundreds of recent literature works, this paper presents a systematic review of this evolving landscape, focusing on the use of LLM techniques to prepare data for diverse downstream tasks. First, we characterize the fundamental paradigm shift, from rule-based, model-specific pipelines to prompt-driven, context-aware, and agentic preparation workflows. Next, we introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning (e.g., standardization, error processing, imputation), data integration (e.g., entity matching, schema matching), and data enrichment (e.g., data annotation, profiling). For each task, we survey representative techniques, and highlight their respective strengths (e.g., improved generalization, semantic understanding) and limitations (e.g., the prohibitive cost of scaling LLMs, persistent hallucinations even in advanced agents, the mismatch between advanced methods and weak evaluation). Moreover, we analyze commonly used datasets and evaluation metrics (the empirical part). Finally, we discuss open research challenges and outline a forward-looking roadmap that emphasizes scalable LLM-data systems, principled designs for reliable agentic workflows, and robust evaluation protocols.

Recently, the frontier of Large Language Model (LLM) capabilities has shifted from single-turn code generation to agentic software engineering-a paradigm where models autonomously navigate, edit, and test complex repositories. While post-training methods have become the de facto approach for code agents, **agentic mid-training**-mid-training (MT) on large-scale data that mirrors authentic agentic workflows-remains critically underexplored due to substantial resource requirements, despite offering a more scalable path to instilling foundational agentic behaviors than relying solely on expensive reinforcement learning. A central challenge in realizing effective agentic mid-training is the distribution mismatch between static training data and the dynamic, feedback-rich environment of real development. To address this, we present a systematic study of agentic mid-training, establishing both the data synthesis principles and training methodology for effective agent development at scale. Central to our approach is **agent-native data**-supervision comprising two complementary types of trajectories: **contextually-native trajectories** that preserve the complete information flow an agent experiences, offering broad coverage and diversity; and **environmentally-native trajectories** collected from executable repositories where observations stem from actual tool invocations and test executions, providing depth and interaction authenticity. We verify the model's agentic capabilities on `SWE-Bench Verified`. We demonstrate our superiority over the previous open software engineering mid-training recipe `Kimi-Dev` under two post-training settings with an aligned base model and agentic scaffold, while using less than half mid-training tokens (73.1B). Besides relative advantage, our best performing 32B and 72B models achieve **56.1%** and **58.5%** resolution rates, respectively, which are ...

Recent advances in video generation have produced models capable of synthesizing stunning visual content from simple text prompts. However, these models struggle to generate long-form, coherent narratives from high-level concepts like dialogue, revealing a ``semantic gap'' between a creative idea and its cinematic execution. To bridge this gap, we introduce a novel, end-to-end agentic framework for dialogue-to-cinematic-video generation. Central to our framework is ScripterAgent, a model trained to translate coarse dialogue into a fine-grained, executable cinematic script. To enable this, we construct ScriptBench, a new large-scale benchmark with rich multimodal context, annotated via an expert-guided pipeline. The generated script then guides DirectorAgent, which orchestrates state-of-the-art video models using a cross-scene continuous generation strategy to ensure long-horizon coherence. Our comprehensive evaluation, featuring an AI-powered CriticAgent and a new Visual-Script Alignment (VSA) metric, shows our framework significantly improves script faithfulness and temporal fidelity across all tested video models. Furthermore, our analysis uncovers a crucial trade-off in current SOTA models between visual spectacle and strict script adherence, providing valuable insights for the future of automated filmmaking.

While synthetic data has proven effective for improving scientific reasoning in the text domain, multimodal reasoning remains constrained by the difficulty of synthesizing scientifically rigorous images. Existing Text-to-Image (T2I) models often produce outputs that are visually plausible yet scientifically incorrect, resulting in a persistent visual-logic divergence that limits their value for downstream reasoning. Motivated by recent advances in next-generation T2I models, we conduct a systematic study of scientific image synthesis across generation paradigms, evaluation, and downstream use. We analyze both direct pixel-based generation and programmatic synthesis, and propose ImgCoder, a logic-driven framework that follows an explicit "understand - plan - code" workflow to improve structural precision. To rigorously assess scientific correctness, we introduce SciGenBench, which evaluates generated images based on information utility and logical validity. Our evaluation reveals systematic failure modes in pixel-based models and highlights a fundamental expressiveness-precision trade-off. Finally, we show that fine-tuning Large Multimodal Models (LMMs) on rigorously verified synthetic scientific images yields consistent reasoning gains, with potential scaling trends analogous to the text domain, validating high-fidelity scientific synthesis as a viable path to unlocking massive multimodal reasoning capabilities.

The quadratic complexity of standard attention mechanisms poses a significant scalability bottleneck for large language models (LLMs) in long-context scenarios. While hybrid attention strategies that combine sparse and full attention within a single model offer a viable solution, they typically employ static computation ratios (i.e., fixed proportions of sparse versus full attention) and fail to adapt to the varying sparsity sensitivities of downstream tasks during inference. To address this issue, we propose Elastic Attention, which allows the model to dynamically adjust its overall sparsity based on the input. This is achieved by integrating a lightweight Attention Router into the existing pretrained model, which dynamically assigns each attention head to different computation modes. Within only 12 hours of training on 8xA800 GPUs, our method enables models to achieve both strong performance and efficient inference. Experiments across three long-context benchmarks on widely-used LLMs demonstrate the superiority of our method.

The field of image generation is currently bifurcated into autoregressive (AR) models operating on discrete tokens and diffusion models utilizing continuous latents. This divide, rooted in the distinction between VQ-VAEs and VAEs, hinders unified modeling and fair benchmarking. Finite Scalar Quantization (FSQ) offers a theoretical bridge, yet vanilla FSQ suffers from a critical flaw: its equal-interval quantization can cause activation collapse. This mismatch forces a trade-off between reconstruction fidelity and information efficiency. In this work, we resolve this dilemma by simply replacing the activation function in original FSQ with a distribution-matching mapping to enforce a uniform prior. Termed iFSQ, this simple strategy requires just one line of code yet mathematically guarantees both optimal bin utilization and reconstruction precision. Leveraging iFSQ as a controlled benchmark, we uncover two key insights: (1) The optimal equilibrium between discrete and continuous representations lies at approximately 4 bits per dimension. (2) Under identical reconstruction constraints, AR models exhibit rapid initial convergence, whereas diffusion models achieve a superior performance ceiling, suggesting that strict sequential ordering may limit the upper bounds of generation quality. Finally, we extend our analysis by adapting Representation Alignment (REPA) to AR models, yielding LlamaGen-REPA. Codes is available at https://github.com/Tencent-Hunyuan/iFSQ

Can a model learn to escape its own learning plateau? Reinforcement learning methods for finetuning large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigate a fundamental question: Can a pretrained LLM leverage latent knowledge to generate an automated curriculum for problems it cannot solve? To explore this, we design SOAR: A self-improvement framework designed to surface these pedagogical signals through meta-RL. A teacher copy of the model proposes synthetic problems for a student copy, and is rewarded with its improvement on a small subset of hard problems. Critically, SOAR grounds the curriculum in measured student progress rather than intrinsic proxy rewards. Our study on the hardest subsets of mathematical benchmarks (0/128 success) reveals three core findings. First, we show that it is possible to realize bi-level meta-RL that unlocks learning under sparse, binary rewards by sharpening a latent capacity of pretrained models to generate useful stepping stones. Second, grounded rewards outperform intrinsic reward schemes used in prior LLM self-play, reliably avoiding the instability and diversity collapse modes they typically exhibit. Third, analyzing the generated questions reveals that structural quality and well-posedness are more critical for learning progress than solution correctness. Our results suggest that the ability to generate useful stepping stones does not require the preexisting ability to actually solve the hard problems, paving a principled path to escape reasoning plateaus without additional curated data.

Modern video generators still struggle with complex physical dynamics, often falling short of physical realism. Existing approaches address this using external verifiers or additional training on augmented data, which is computationally expensive and still limited in capturing fine-grained motion. In this work, we present self-refining video sampling, a simple method that uses a pre-trained video generator trained on large-scale datasets as its own self-refiner. By interpreting the generator as a denoising autoencoder, we enable iterative inner-loop refinement at inference time without any external verifier or additional training. We further introduce an uncertainty-aware refinement strategy that selectively refines regions based on self-consistency, which prevents artifacts caused by over-refinement. Experiments on state-of-the-art video generators demonstrate significant improvements in motion coherence and physics alignment, achieving over 70\% human preference compared to the default sampler and guidance-based sampler.

This report presents VibeVoice-ASR, a general-purpose speech understanding framework built upon VibeVoice, designed to address the persistent challenges of context fragmentation and multi-speaker complexity in long-form audio (e.g., meetings, podcasts) that remain despite recent advancements in short-form speech recognition. Unlike traditional pipelined approaches that rely on audio chunking, VibeVoice-ASRsupports single-pass processing for up to 60 minutes of audio. It unifies Automatic Speech Recognition, Speaker Diarization, and Timestamping into a single end-to-end generation task. In addition, VibeVoice-ASR supports over 50 languages, requires no explicit language setting, and natively handles code-switching within and across utterances. Furthermore, we introduce a prompt-based context injection mechanism that allows users to supply customized conetxt, significantly improving accuracy on domain-specific terminology and polyphonic character disambiguation.

General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly structured content leads to semantic compression and query-table mismatch. Recent LLM-based retrieval augmentation methods mitigate this issue by generating synthetic queries, yet they often rely on heuristic partial-table selection and seldom leverage these synthetic queries as supervision to improve the embedding model. We introduce CGPT, a training framework that enhances table retrieval through LLM-generated supervision. CGPT constructs semantically diverse partial tables by clustering table instances using K-means and sampling across clusters to broaden semantic coverage. An LLM then generates synthetic queries for these partial tables, which are used in hard-negative contrastive fine-tuning to refine the embedding model. Experiments across four public benchmarks (MimoTable, OTTQA, FetaQA, and E2E-WTQ) show that CGPT consistently outperforms retrieval baselines, including QGpT, with an average R@1 improvement of 16.54 percent. In a unified multi-domain corpus setting, CGPT further demonstrates strong cross-domain generalization and remains effective even when using smaller LLMs for synthetic query generation. These results indicate that semantically guided partial-table construction, combined with contrastive training from LLM-generated supervision, provides an effective and scalable paradigm for large-scale table retrieval. Our code is available at https://github.com/yumeow0122/CGPT.

While agent evaluation has shifted toward long-horizon tasks, most benchmarks still emphasize local, step-level reasoning rather than the global constrained optimization (e.g., time and financial budgets) that demands genuine planning ability. Meanwhile, existing LLM planning benchmarks underrepresent the active information gathering and fine-grained local constraints typical of real-world settings. To address this, we introduce DeepPlanning, a challenging benchmark for practical long-horizon agent planning. It features multi-day travel planning and multi-product shopping tasks that require proactive information acquisition, local constrained reasoning, and global constrained optimization. Evaluations on DeepPlanning show that even frontier agentic LLMs struggle with these problems, highlighting the importance of reliable explicit reasoning patterns and parallel tool use for achieving better effectiveness-efficiency trade-offs. Error analysis further points to promising directions for improving agentic LLMs over long planning horizons. We open-source the code and data to support future research.

Table retrieval is the task of retrieving the most relevant tables from large-scale corpora given natural language queries. However, structural and semantic discrepancies between unstructured text and structured tables make embedding alignment particularly challenging. Recent methods such as QGpT attempt to enrich table semantics by generating synthetic queries, yet they still rely on coarse partial-table sampling and simple fusion strategies, which limit semantic diversity and hinder effective query-table alignment. We propose STAR (Semantic Table Representation), a lightweight framework that improves semantic table representation through semantic clustering and weighted fusion. STAR first applies header-aware K-means clustering to group semantically similar rows and selects representative centroid instances to construct a diverse partial table. It then generates cluster-specific synthetic queries to comprehensively cover the table's semantic space. Finally, STAR employs weighted fusion strategies to integrate table and query embeddings, enabling fine-grained semantic alignment. This design enables STAR to capture complementary information from structured and textual sources, improving the expressiveness of table representations. Experiments on five benchmarks show that STAR achieves consistently higher Recall than QGpT on all datasets, demonstrating the effectiveness of semantic clustering and adaptive weighted fusion for robust table representation. Our code is available at https://github.com/adsl135789/STAR.

Generalist LLM agents are often post-trained on a narrow set of environments but deployed across far broader, unseen domains. In this work, we investigate the challenge of agentic post-training when the eventual test domains are unknown. Specifically, we analyze which properties of reinforcement learning (RL) environments and modeling choices have the greatest influence on out-of-domain performance. First, we identify two environment axes that strongly correlate with cross-domain generalization: (i) state information richness, i.e., the amount of information for the agent to process from the state, and (ii) planning complexity, estimated via goal reachability and trajectory length under a base policy. Notably, domain realism and text-level similarity are not the primary factors; for instance, the simple grid-world domain Sokoban leads to even stronger generalization in SciWorld than the more realistic ALFWorld. Motivated by these findings, we further show that increasing state information richness alone can already effectively improve cross-domain robustness. We propose a randomization technique, which is low-overhead and broadly applicable: add small amounts of distractive goal-irrelevant features to the state to make it richer without altering the task. Beyond environment-side properties, we also examine several modeling choices: (a) SFT warmup or mid-training helps prevent catastrophic forgetting during RL but undermines generalization to domains that are not included in the mid-training datamix; and (b) turning on step-by-step thinking during RL, while not always improving in-domain performance, plays a crucial role in preserving generalization.

Real-world perception and interaction are inherently multimodal, encompassing not only language but also vision and speech, which motivates the development of "Omni" MLLMs that support both multimodal inputs and multimodal outputs. While a sequence of omni MLLMs has emerged, most existing systems still rely on additional expert components to achieve multimodal generation, limiting the simplicity of unified training and inference. Autoregressive (AR) modeling, with a single token stream, a single next-token objective, and a single decoder, is an elegant and scalable foundation in the text domain. Motivated by this, we present AR-Omni, a unified any-to-any model in the autoregressive paradigm without any expert decoders. AR-Omni supports autoregressive text and image generation, as well as streaming speech generation, all under a single Transformer decoder. We further address three practical issues in unified AR modeling: modality imbalance via task-aware loss reweighting, visual fidelity via a lightweight token-level perceptual alignment loss for image tokens, and stability-creativity trade-offs via a finite-state decoding mechanism. Empirically, AR-Omni achieves strong quality across three modalities while remaining real-time, achieving a 0.88 real-time factor for speech generation.

The advent of always-on personal AI assistants, enabled by all-day wearable devices such as smart glasses, demands a new level of contextual understanding, one that goes beyond short, isolated events to encompass the continuous, longitudinal stream of egocentric video. Achieving this vision requires advances in long-horizon video understanding, where systems must interpret and recall visual and audio information spanning days or even weeks. Existing methods, including large language models and retrieval-augmented generation, are constrained by limited context windows and lack the ability to perform compositional, multi-hop reasoning over very long video streams. In this work, we address these challenges through EGAgent, an enhanced agentic framework centered on entity scene graphs, which represent people, places, objects, and their relationships over time. Our system equips a planning agent with tools for structured search and reasoning over these graphs, as well as hybrid visual and audio search capabilities, enabling detailed, cross-modal, and temporally coherent reasoning. Experiments on the EgoLifeQA and Video-MME (Long) datasets show that our method achieves state-of-the-art performance on EgoLifeQA (57.5%) and competitive performance on Video-MME (Long) (74.1%) for complex longitudinal video understanding tasks.

TikTok 美国宣布调查为何用户无法在私信中提及 Epstein。TikTok 美国官员否认审查 Epstein 一词，但很多人认为这是显而易见的。TikTok 美国业务的主要股东之一是甲骨文，而甲骨文董事长 Larry Ellison 是美国总统特朗普的盟友，而特朗普过去几个月受困于与 Jeffrey Epstein 的关系。NPR 等媒体的调查显示，对 Epstein 的审查并不连续，部分用户可以在私信中提及 Epstein 但另一部分用户不行。TikTok 美国应用过去一天还遭遇了服务宕机的事故。

Valve 在英国面临 6.56 亿英镑的集体诉讼。起诉者代表了 自 2018 年以来在 Valve 的 PC 游戏平台 Steam 购买游戏或 DLC 的 1400 万英国用户，诉讼指控 Steam 收取了过高的佣金。Steam 和其它数字平台的佣金比例类似，大约为 30%。起诉者指控 Steam 通过设置条件，阻止游戏发行商在竞争对手平台上以更低的价格或更早的时间销售游戏。此外一旦玩家在 Steam 上购买了游戏，那么游戏后续的 DLC 或附加内容也都必须通过 Steam 购买，事实上将用户“锁定”在 Steam 平台，从中收取“不公平且过高”的佣金。Valve 主张驳回诉讼，但伦敦竞争上诉法庭裁定可以继续推进。

科学家预测到 2050 年全球有 37.9 亿人面临极端高温。热带国家将首当其冲，而气候较凉快的地区也需要适应。研究发现，如果全球平均气温比工业化前水平升高 2C，到 2050 年经历极端高温天气的人口预计将翻番。大部分影响将在本十年内显现，因为全球气温正迅速逼近 1.5C 的临界点。高温常被称为“无声杀手”，因为大多数中暑死亡是缓慢发生的，高温和其它环境因素共同作用破坏了人体内部的体温调节机制。气候变化导致热浪持续时间更长、强度更大，空调等降温设备将变得至关重要。

沙特准备大幅缩减其雄心勃勃的未来城市项目 Neom 的规模。Neom 意思是新未来城市，沙特原计划斥资 5000 亿美元建造。Neom 位于沙特西北部的 Tabuk，总面积 26500 平方公里，其核心组成部分是名叫 The Line 的线条城市，高 500 米，长 170 公里，能容纳 900 万居民，城市被封闭在两座距离约 200 米的平行高墙之内，表面覆盖镜子。The Line 有三层，地面一层供行人使用，地下两层一层为基础设施一层为地下交通。它还有一条高铁，时速能达到 512 公里，从线条城市的一头到另一头只需要 20 分钟。 但如今 The Line 准备彻底的重新设计，方案将更为精简，Neom 可能将变成数据中心枢纽，利用其沿海位置的海水冷却服务器。

一项研究发现，Google AI Overviews 回答健康问题时引用 YouTube 的次数超过了任何医学网站。Google 曾表示，AI Overviews 生成的摘要是可靠的，会引用权威医疗机构如疾控中心（CDC）等作为信息来源。SE Ranking 研究人员分析了柏林地区逾 5 万次健康查询结果，发现最主要的信息源是 YouTube。YouTube 占到总引用次数的 4.43%，没有医疗机构或医学机构的引用接近这一比例。在总共 465,823 次引用中，YouTube 高达 20,621 次。 Google 对此表示，研究使用了德语进行搜索，因此其结果并不能推广到其它地区。

72 岁的 RMS（Richard Stallman）在佐治亚理工学院演讲时谈及了 DRM 并回答了观众有关盗版的问题。RMS 说，他鄙视 DRM，不想拥有任何包含 DRM 的东西的拷贝，不会为了任何东西而屈服于 DRM，所以他不使用 Spotify 或 Netflix。对于观众提出的是否有道德义务避免盗版的问题，RMS 表示他不会用盗版这个词去指代分享，分享是善行，应该是合法的。版权法是错误的，事实上是非正义的。他会毫不犹豫的分享任何东西，但因为鄙视 DRM 他没有任何非自由软件的副本。仅仅因为一项法律赋予某些人不公正的权力，并不意味着违反这项法律是错误的，“禁止人们互相帮助，分裂他们，这是卑鄙的。”他表示自己是通过他人的分享观看电影的。

中国迄今为止发布了 1509 种大模型，以国家计算排名第一。其中评价提高的是阿里巴巴的 Qwen（千问）模型。 Hugging Face的数据，截至 2026 年 1 月千问系列的累计下载量超过 7 亿次，成为平台上下载量最多的开源 AI 模型。中国企业的战斗方式与美国企业不同的情况也很明显。美国 AI 以最尖端 GPU 和巨额投资为前提，而中国则以效率化、轻量化为核心确保竞争力。

你与外部世界的界限在哪里？大脑如何判断这条界线？根据发表在《Nature Communications》期刊上的研究，瑞典和法国的研究人员让 106 名参与者体验“橡胶手错觉（rubber hand illusion）”，监测和刺激大脑活动，观察其产生的影响。实验将参与者的一只手藏起来，用橡胶手代替。当参与者的真手和假手同时被反复触摸时，会产生橡胶手是自己身体一部分的错觉。实验用脑电图（EEG）记录大脑活动，结果显示，身体所有权感似乎源于顶叶皮层——负责绘制身体地图、处理感觉输入和构建自我意识的大脑区域——的 α 波频率。加快 α 波会收缩受试者的身体所有权感，对细微的时间差异更加敏感。减慢 α 波则会产生相反的效果，更难区分自身身体和外部世界。

OnePlus 一月固件更新 ColorOS 16.0.3.501 引入了硬件级防回滚机制，阻止用户降低设备运行的固件版本或安装自制 ROM，受影响的型号包括 OnePlus 13、OnePlus 13T 和 OnePlus 15，任何尝试安装旧版本固件的行为都会导致设备永久“变砖”——设备将无法使用，因设备上的高通处理器内部电子熔断器熔断。升级到 ColorOS 16.0.3.501 的用户上周开始报告设备无法回滚到之前的版本。OnePlus 已移除所有地区 OnePlus 13 降级固件的下载链接，OnePlus 12 的降级软件包也已被移除。

根据微软的支持文档，Windows 11 一月安全更新可能导致部分 PC 无法启动，它正在收集用户和 IT 管理员的反馈。用户无需卸载此更新，因为该问题“仅限于”特定 PC。目前不清楚有多少用户受到影响，微软列出了两个受影响的平台：KB5074109 – Windows 11 25H2 和 KB5074109 – Windows 11 24H2。微软称，受影响的 PC 可能会突然停止启动，出现黑屏死机 (BSOD)错误，错误代码为 UNMOUNTABLE_BOOT_VOLUME。

RMS（Richard Stallman）在佐治亚理工学院（Georgia Institute of Technology）发表演讲，促进自由软件的同时也谈论了当今的新技术。RMS 认为 AI 太频繁的被用于形容没有智能的东西，大语言模型只是一种生成器，能生成文本但并不理解内容。它们也经常犯错。称大模型是 AI 就相当于认可它们有智能，他认为应当少用。RMS 建议应该将大模型称为是伪智能（Pretend Intelligence）或简写 PI，多用 PI 有助于克服对大模型的炒作，这些炒作试图说服人们信任大模型，好将他们的一切都交给开发和控制大模型的大公司。RMS 也对联网和能上传数据的汽车表示反感，认为它们含有恶意功能。他也拒绝拥有智能手机，认为智能手机是“奥威尔式的追踪和监控设备”。他仍然使用一台旧型号的 ThinkPad 笔记本电脑，最喜欢的自由发行版是 Trisquel，对 Rust 的商标使用条款有些看法，但认为只要是自由软件都支持。所以使用 Emacs 或使用 Vi 都没关系，只要是自由软件，但 Emacs 爱每一位用户，不用它会伤心。

外科医生早就注意到一个令人费解的现象：同样的手术切口，脸上的往往愈合得更好，疤痕更不明显，而身体其他部位则容易留下显著的疤痕。 发表在《细胞》（Cell）上的一项研究揭示了这一现象背后的分子机制。斯坦福大学的研究团队通过小鼠模型发现，面部皮肤之所以具有“无痕愈合”的潜力，是因为其深层的成纤维细胞能抑制疤痕组织的过度生成。基于这一发现研制的药物有望让任何伤口都能不留疤痕地愈合。人体皮肤真皮层的主要细胞类型是成纤维细胞。研究指出，面部和头皮的成纤维细胞起源于胚胎发育早期的“神经嵴”（neural crest），而身体其他部位的成纤维细胞则源自“中胚层”（mesoderm）。这决定了这些细胞在成熟后的行为模式。研究团队发现，源自神经嵴的面部成纤维细胞高表达一种名为 ROBO2 的蛋白及其下游因子 EID1。这一信号通路就像一个分子层面的“抑制器”，它能有效阻断一种名为 EP300 的蛋白质的功能。在身体其他部位的成纤维细胞中，EP300 能够打开 DNA 的折叠结构，让负责制造胶原蛋白等疤痕成分的基因变得活跃。而在面部细胞中，由于 ROBO2 和 EID1 的存在，EP300 受到抑制，DNA 保持在一种相对“沉默”和紧密的状态，使得促纤维化基因无法被轻易读取和表达。这种状态让面部细胞更接近其原始的干细胞形态，从而倾向于再生修复而非填补式的疤痕修复。研究人员指出，这在进化上是非常合理的。对于躯干上的伤口，生物体的首要任务是活下去，因此需要快速封闭伤口以防失血过多或感染，哪怕代价是形成功能较差的疤痕组织。然而，面部承载着视觉、听觉、嗅觉和进食等关键功能，如果形成僵硬的疤痕，将严重影响生物的基本生存能力，因此进化赋予了面部更精细的再生能力。

根据解封的法庭文件，12 月 29 日 Spotify 与环球唱片（UMG）、索尼、华纳等唱片公司向纽约南区地方法院提起诉讼，指控安娜的档案（Anna’s Archive）大规模侵权。法庭于 1 月 2 日发布了一项临时限制令，限制托管安娜的档案网站和为其提供域名服务，法庭的命令导致了安娜的档案的 .org 和 .se 域名先后被封。此事发生在 安娜的档案发布 300TB 抓取的 Spotify 音乐文件和元数据之后，安娜的档案背后的运营者一度以为其域名被封与 Spotify 无关，但法庭文件显示并非如此。

伊朗的互联网目前处于严格的白名单模式下，绝大多数人都无法访问国际互联网。该系统被称为 Barracks Internet。这一网络分级制可能至少始于 2013 年，伊朗向约 1.6 万人提供了白色 SIM 卡，允许他们不受限制的访问国际互联网。2025 年 11 月 X/Twitter 的位置功能显示，包括通信信息技术部长在内的官员被发现是直接从伊朗境内访问该平台的，而 X/Twitter 早在 2009 年就被封锁了。自 2022 年拜登政府将星链服务排除在制裁外以来，活动人士估计已向伊朗走私了 5 万个星链卫星终端。伊朗政府称它切断了 4 万个星链接入，干扰了部分终端，但一部分终端在固件更新后绕过了封锁，还能正常运行。尽管如此，卫星宽带技术容易受到信号干扰，意味着伊朗政府掌握着最终的制衡力量。

根据 NetBlocks 的监测，伊朗断网 17 天超过 400 小时。过去几天伊朗的网络处于严格的白名单模式，只有极少数人可以访问国际互联网，流量在短时间会出现峰值，但对绝大多数伊朗人而言，国际网络遥不可及而且非常不稳定。路由跟踪显示，访问伊朗网站的流量会路由经过俄罗斯 (AS8631) 和阿塞拜疆 (AS29049)的自治系统。