Weekly Digest — 2026-W05

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.

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

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

an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM

🎥 Make videos programmatically with React

FinRobot: An Open-Source AI Agent Platform for Financial Analysis using LLMs 🚀 🚀 🚀

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

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.

Collection of extracted System Prompts from popular chatbots like ChatGPT, Claude & Gemini

Memory for 24/7 proactive agents like moltbot (clawdbot).

We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thinking-2601 achieves state-of-the-art performance among open-source models on a wide range of agentic benchmarks, including agentic search, agentic tool use, and tool-integrated reasoning. Beyond benchmark performance, the model demonstrates strong generalization to complex tool interactions and robust behavior under noisy real-world environments. Its advanced capability stems from a unified training framework that combines domain-parallel expert training with subsequent fusion, together with an end-to-end co-design of data construction, environments, algorithms, and infrastructure spanning from pre-training to post-training. In particular, the model's strong generalization capability in complex tool-use are driven by our in-depth exploration of environment scaling and principled task construction. To optimize long-tailed, skewed generation and multi-turn agentic interactions, and to enable stable training across over 10,000 environments spanning more than 20 domains, we systematically extend our asynchronous reinforcement learning framework, DORA, for stable and efficient large-scale multi-environment training. Furthermore, recognizing that real-world tasks are inherently noisy, we conduct a systematic analysis and decomposition of real-world noise patterns, and design targeted training procedures to explicitly incorporate such imperfections into the training process, resulting in improved robustness for real-world applications. To further enhance performance on complex reasoning tasks, we introduce a Heavy Thinking mode that enables effective test-time scaling by jointly expanding reasoning depth and width through intensive parallel thinking.

LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency. While various context compression approaches such as LongLLMLingua have emerged to tackle this challenge, they typically rely on fixed metrics such as PPL, ignoring the task-specific nature of code understanding. As a result, they frequently disrupt syntactic and logical structure and fail to retain critical implementation details. In this paper, we propose SWE-Pruner, a self-adaptive context pruning framework tailored for coding agents. Drawing inspiration from how human programmers "selectively skim" source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts. Given the current task, the agent formulates an explicit goal (e.g., "focus on error handling") as a hint to guide the pruning targets. A lightweight neural skimmer (0.6B parameters) is trained to dynamically select relevant lines from the surrounding context given the goal. Evaluations across four benchmarks and multiple models validate SWE-Pruner's effectiveness in various scenarios, achieving 23-54% token reduction on agent tasks like SWE-Bench Verified and up to 14.84x compression on single-turn tasks like LongCodeQA with minimal performance impact.

Standard Vision-Language-Action (VLA) models typically fine-tune a monolithic Vision-Language Model (VLM) backbone explicitly for robotic control. However, this approach creates a critical tension between maintaining high-level general semantic understanding and learning low-level, fine-grained sensorimotor skills, often leading to "catastrophic forgetting" of the model's open-world capabilities. To resolve this conflict, we introduce TwinBrainVLA, a novel architecture that coordinates a generalist VLM retaining universal semantic understanding and a specialist VLM dedicated to embodied proprioception for joint robotic control. TwinBrainVLA synergizes a frozen "Left Brain", which retains robust general visual reasoning, with a trainable "Right Brain", specialized for embodied perception, via a novel Asymmetric Mixture-of-Transformers (AsyMoT) mechanism. This design allows the Right Brain to dynamically query semantic knowledge from the frozen Left Brain and fuse it with proprioceptive states, providing rich conditioning for a Flow-Matching Action Expert to generate precise continuous controls. Extensive experiments on SimplerEnv and RoboCasa benchmarks demonstrate that TwinBrainVLA achieves superior manipulation performance compared to state-of-the-art baselines while explicitly preserving the comprehensive visual understanding capabilities of the pre-trained VLM, offering a promising direction for building general-purpose robots that simultaneously achieve high-level semantic understanding and low-level physical dexterity.

Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long horizons. We introduce VisGym, a gymnasium of 17 environments for evaluating and training VLMs. The suite spans symbolic puzzles, real-image understanding, navigation, and manipulation, and provides flexible controls over difficulty, input representation, planning horizon, and feedback. We also provide multi-step solvers that generate structured demonstrations, enabling supervised finetuning. Our evaluations show that all frontier models struggle in interactive settings, achieving low success rates in both the easy (46.6%) and hard (26.0%) configurations. Our experiments reveal notable limitations: models struggle to effectively leverage long context, performing worse with an unbounded history than with truncated windows. Furthermore, we find that several text-based symbolic tasks become substantially harder once rendered visually. However, explicit goal observations, textual feedback, and exploratory demonstrations in partially observable or unknown-dynamics settings for supervised finetuning yield consistent gains, highlighting concrete failure modes and pathways for improving multi-step visual decision-making. Code, data, and models can be found at: https://visgym.github.io/.

Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. While the majority of existing efforts focus on enhancing policy capabilities via post-training, we propose an alternative paradigm: self-evolving the agent's ability by iteratively verifying the policy model's outputs, guided by meticulously crafted rubrics. This approach gives rise to the inference-time scaling of verification, wherein an agent self-improves by evaluating its generated answers to produce iterative feedback and refinements. We derive the rubrics based on an automatically constructed DRA Failure Taxonomy, which systematically classifies agent failures into five major categories and thirteen sub-categories. We present DeepVerifier, a rubrics-based outcome reward verifier that leverages the asymmetry of verification and outperforms vanilla agent-as-judge and LLM judge baselines by 12%-48% in meta-evaluation F1 score. To enable practical self-evolution, DeepVerifier integrates as a plug-and-play module during test-time inference. The verifier produces detailed rubric-based feedback, which is fed back to the agent for iterative bootstrapping, refining responses without additional training. This test-time scaling delivers 8%-11% accuracy gains on challenging subsets of GAIA and XBench-DeepResearch when powered by capable closed-source LLMs. Finally, to support open-source advancement, we release DeepVerifier-4K, a curated supervised fine-tuning dataset of 4,646 high-quality agent steps focused on DRA verification. These examples emphasize reflection and self-critique, enabling open models to develop robust verification capabilities.

Recent foundational video-to-video diffusion models have achieved impressive results in editing user provided videos by modifying appearance, motion, or camera movement. However, real-world video editing is often an iterative process, where users refine results across multiple rounds of interaction. In this multi-turn setting, current video editors struggle to maintain cross-consistency across sequential edits. In this work, we tackle, for the first time, the problem of cross-consistency in multi-turn video editing and introduce Memory-V2V, a simple, yet effective framework that augments existing video-to-video models with explicit memory. Given an external cache of previously edited videos, Memory-V2V employs accurate retrieval and dynamic tokenization strategies to condition the current editing step on prior results. To further mitigate redundancy and computational overhead, we propose a learnable token compressor within the DiT backbone that compresses redundant conditioning tokens while preserving essential visual cues, achieving an overall speedup of 30%. We validate Memory-V2V on challenging tasks including video novel view synthesis and text-conditioned long video editing. Extensive experiments show that Memory-V2V produces videos that are significantly more cross-consistent with minimal computational overhead, while maintaining or even improving task-specific performance over state-of-the-art baselines. Project page: https://dohunlee1.github.io/MemoryV2V

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

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。

一项研究发现，预期未来负面结果比正面结果带来的情绪冲击力更大，这有助于解释为什么人们倾向于避开不确定性，尽快做出决策。研究人员发现，畏惧损失带来的情绪影响六倍于预期同等收益带来的愉悦。英国研究人员分析了 1991-2024 年间近 14000 名参与者的数据，追踪对未来财务状况的预期所产生的情绪反应，以及预期情绪如何影响风险和延迟相关的决策。研究证实，损失带来的冲击力大于收益。简而言之，预期损失 10 英镑带来的痛苦远比享受获得 10 英镑的喜悦强烈得多。研究人员发现，个人之间的情绪反应存在差异，部分人群对预期结果的情绪比其他人更强烈。

Google DeepMind CEO、诺贝尔奖得主、负责开发 Google Gemini 大模型的 Demis Hassabis 以及图灵奖得主 Yann LeCun 都认为大模型虽然备受瞩目，但并非通往 AGI(人类通用智能)之路。Hassabis 表示今天的 AI 虽然令人印象深刻，但距离 AGI 还很遥远。他预测 AGI 在十年内实现的概率是 50%。Yann LeCun 则更悲观，认为今天基于大模型的 AI 永远也无法实现 AGI，需要完全不同的方法。他认为大型语言模型之所以成功是因为语言简单。Anthropic CEO Dario Amodei 则要乐观的多，认为 AI 模型能在一年内取代所有程序员的工作，两年内实现诺奖级别的研究成果，五年内五成白领工作将消失。OpenAI CEO Sam Altman 此前表达过类似观点。对大部分企业领袖而言关键问题还是 AI 何时能带来巨大经济价值。

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。