OrangeBot.AI Digest — 2025-06-22

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

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This paper presents improved native unified multimodal models, i.e., Show-o2, that leverage autoregressive modeling and flow matching. Built upon a 3D causal variational autoencoder space, unified visual representations are constructed through a dual-path of spatial (-temporal) fusion, enabling scalability across image and video modalities while ensuring effective multimodal understanding and generation. Based on a language model, autoregressive modeling and flow matching are natively applied to the language head and flow head, respectively, to facilitate text token prediction and image/video generation. A two-stage training recipe is designed to effectively learn and scale to larger models. The resulting Show-o2 models demonstrate versatility in handling a wide range of multimodal understanding and generation tasks across diverse modalities, including text, images, and videos. Code and models are released at https://github.com/showlab/Show-o.

Recent Large Language Models (LLMs) have reported high accuracy on reasoning benchmarks. However, it is still unclear whether the observed results arise from true reasoning or from statistical recall of the training set. Inspired by the ladder of causation (Pearl, 2009) and its three levels (associations, interventions and counterfactuals), this paper introduces RE-IMAGINE, a framework to characterize a hierarchy of reasoning ability in LLMs, alongside an automated pipeline to generate problem variations at different levels of the hierarchy. By altering problems in an intermediate symbolic representation, RE-IMAGINE generates arbitrarily many problems that are not solvable using memorization alone. Moreover, the framework is general and can work across reasoning domains, including math, code, and logic. We demonstrate our framework on four widely-used benchmarks to evaluate several families of LLMs, and observe reductions in performance when the models are queried with problem variations. These assessments indicate a degree of reliance on statistical recall for past performance, and open the door to further research targeting skills across the reasoning hierarchy.

The advancement of text-to-speech and audio generation models necessitates robust benchmarks for evaluating the emotional understanding capabilities of AI systems. Current speech emotion recognition (SER) datasets often exhibit limitations in emotional granularity, privacy concerns, or reliance on acted portrayals. This paper introduces EmoNet-Voice, a new resource for speech emotion detection, which includes EmoNet-Voice Big, a large-scale pre-training dataset (featuring over 4,500 hours of speech across 11 voices, 40 emotions, and 4 languages), and EmoNet-Voice Bench, a novel benchmark dataset with human expert annotations. EmoNet-Voice is designed to evaluate SER models on a fine-grained spectrum of 40 emotion categories with different levels of intensities. Leveraging state-of-the-art voice generation, we curated synthetic audio snippets simulating actors portraying scenes designed to evoke specific emotions. Crucially, we conducted rigorous validation by psychology experts who assigned perceived intensity labels. This synthetic, privacy-preserving approach allows for the inclusion of sensitive emotional states often absent in existing datasets. Lastly, we introduce Empathic Insight Voice models that set a new standard in speech emotion recognition with high agreement with human experts. Our evaluations across the current model landscape exhibit valuable findings, such as high-arousal emotions like anger being much easier to detect than low-arousal states like concentration.

Reinforcement learning (RL) has emerged as a promising approach to improve large language model (LLM) reasoning, yet most open efforts focus narrowly on math and code, limiting our understanding of its broader applicability to general reasoning. A key challenge lies in the lack of reliable, scalable RL reward signals across diverse reasoning domains. We introduce Guru, a curated RL reasoning corpus of 92K verifiable examples spanning six reasoning domains--Math, Code, Science, Logic, Simulation, and Tabular--each built through domain-specific reward design, deduplication, and filtering to ensure reliability and effectiveness for RL training. Based on Guru, we systematically revisit established findings in RL for LLM reasoning and observe significant variation across domains. For example, while prior work suggests that RL primarily elicits existing knowledge from pretrained models, our results reveal a more nuanced pattern: domains frequently seen during pretraining (Math, Code, Science) easily benefit from cross-domain RL training, while domains with limited pretraining exposure (Logic, Simulation, and Tabular) require in-domain training to achieve meaningful performance gains, suggesting that RL is likely to facilitate genuine skill acquisition. Finally, we present Guru-7B and Guru-32B, two models that achieve state-of-the-art performance among open models RL-trained with publicly available data, outperforming best baselines by 7.9% and 6.7% on our 17-task evaluation suite across six reasoning domains. We also show that our models effectively improve the Pass@k performance of their base models, particularly on complex tasks less likely to appear in pretraining data. We release data, models, training and evaluation code to facilitate general-purpose reasoning at: https://github.com/LLM360/Reasoning360

Detailed captions that accurately reflect the characteristics of a music piece can enrich music databases and drive forward research in music AI. This paper introduces a multi-task music captioning model, SonicVerse, that integrates caption generation with auxiliary music feature detection tasks such as key detection, vocals detection, and more, so as to directly capture both low-level acoustic details as well as high-level musical attributes. The key contribution is a projection-based architecture that transforms audio input into language tokens, while simultaneously detecting music features through dedicated auxiliary heads. The outputs of these heads are also projected into language tokens, to enhance the captioning input. This framework not only produces rich, descriptive captions for short music fragments but also directly enables the generation of detailed time-informed descriptions for longer music pieces, by chaining the outputs using a large-language model. To train the model, we extended the MusicBench dataset by annotating it with music features using MIRFLEX, a modular music feature extractor, resulting in paired audio, captions and music feature data. Experimental results show that incorporating features in this way improves the quality and detail of the generated captions.

Recently, multimodal large language models (MLLMs) have attracted increasing research attention due to their powerful visual understanding capabilities. While they have achieved impressive results on various vision tasks, their performance on chart-to-code generation remains suboptimal. This task requires MLLMs to generate executable code that can reproduce a given chart, demanding not only precise visual understanding but also accurate translation of visual elements into structured code. Directly prompting MLLMs to perform this complex task often yields unsatisfactory results. To address this challenge, we propose {ChartIR}, an iterative refinement method based on structured instruction. First, we distinguish two tasks: visual understanding and code translation. To accomplish the visual understanding component, we design two types of structured instructions: description and difference. The description instruction captures the visual elements of the reference chart, while the difference instruction characterizes the discrepancies between the reference chart and the generated chart. These instructions effectively transform visual features into language representations, thereby facilitating the subsequent code translation process. Second, we decompose the overall chart generation pipeline into two stages: initial code generation and iterative refinement, enabling progressive enhancement of the final output. Experimental results show that, compared to other method, our method achieves superior performance on both the open-source model Qwen2-VL and the closed-source model GPT-4o.

Training LLMs on decentralized and wimpy computation nodes, e.g., multiple on-spot instances, lowers the training cost and enables model democratization. The inevitable challenge here is the churn of nodes due to failures and the operator's scheduling policies, leading to losing a stage - a part of the model. The conventional approaches to recover from failures are to either use checkpointing, where periodically a copy of the entire model is sent to an additional storage, or redundant computation. These approaches yield significant communication and/or computation overhead even in non-failure cases and scale poorly in settings with large models. In this paper, we propose, CheckFree, an efficient recovery method where a failing stage is substituted by a weighted average of the closest neighboring stages. In contrast to the state of the art, CheckFree requires no additional computation or storage. However, because of the nature of averaging neighbouring stages, it can only recover failures of intermediate stages. We further extend our method to CheckFree+ with out-of-order pipeline execution to tolerate crashes of the first and last stages. Thanks to out-of-order pipelining, behaviour of those stages is mimicked by their neighboring ones, which allows CheckFree+ to recover them by simply copying the weights from the immediate neighbour. To be able to recover the (de)embedding layers, CheckFree+ copies those layers to the neighboring stages, which requires relatively small storage overhead. We extensively evaluate our method on LLaMa models of model sizes from 124M to 1.5B with varying failure frequencies. In the case of low and medium failure rates (5-10%), CheckFree and CheckFree+ outperform both checkpointing and redundant computation in terms of convergence in wall-clock time by over 12%. Both of our proposals can be run via our code available at: https://github.com/gensyn-ai/CheckFree.

Diffusion-based image generation models excel at producing high-quality synthetic content, but suffer from slow and computationally expensive inference. Prior work has attempted to mitigate this by caching and reusing features within diffusion transformers across inference steps. These methods, however, often rely on rigid heuristics that result in limited acceleration or poor generalization across architectures. We propose Evolutionary Caching to Accelerate Diffusion models (ECAD), a genetic algorithm that learns efficient, per-model, caching schedules forming a Pareto frontier, using only a small set of calibration prompts. ECAD requires no modifications to network parameters or reference images. It offers significant inference speedups, enables fine-grained control over the quality-latency trade-off, and adapts seamlessly to different diffusion models. Notably, ECAD's learned schedules can generalize effectively to resolutions and model variants not seen during calibration. We evaluate ECAD on PixArt-alpha, PixArt-Sigma, and FLUX-1.dev using multiple metrics (FID, CLIP, Image Reward) across diverse benchmarks (COCO, MJHQ-30k, PartiPrompts), demonstrating consistent improvements over previous approaches. On PixArt-alpha, ECAD identifies a schedule that outperforms the previous state-of-the-art method by 4.47 COCO FID while increasing inference speedup from 2.35x to 2.58x. Our results establish ECAD as a scalable and generalizable approach for accelerating diffusion inference. Our project website is available at https://aniaggarwal.github.io/ecad and our code is available at https://github.com/aniaggarwal/ecad.

Recent benchmarks have probed factual consistency and rhetorical robustness in Large Language Models (LLMs). However, a knowledge gap exists regarding how directional framing of factually true statements influences model agreement, a common scenario for LLM users. AssertBench addresses this by sampling evidence-supported facts from FEVEROUS, a fact verification dataset. For each (evidence-backed) fact, we construct two framing prompts: one where the user claims the statement is factually correct, and another where the user claims it is incorrect. We then record the model's agreement and reasoning. The desired outcome is that the model asserts itself, maintaining consistent truth evaluation across both framings, rather than switching its evaluation to agree with the user. AssertBench isolates framing-induced variability from the model's underlying factual knowledge by stratifying results based on the model's accuracy on the same claims when presented neutrally. In doing so, this benchmark aims to measure an LLM's ability to "stick to its guns" when presented with contradictory user assertions about the same fact. The complete source code is available at https://github.com/achowd32/assert-bench.

The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the temporal and kinematic diversity of the motions, the policy's capability, and the difficulty of coordination of the upper and lower bodies. To address these issues, we propose GMT, a general and scalable motion-tracking framework that trains a single unified policy to enable humanoid robots to track diverse motions in the real world. GMT is built upon two core components: an Adaptive Sampling strategy and a Motion Mixture-of-Experts (MoE) architecture. The Adaptive Sampling automatically balances easy and difficult motions during training. The MoE ensures better specialization of different regions of the motion manifold. We show through extensive experiments in both simulation and the real world the effectiveness of GMT, achieving state-of-the-art performance across a broad spectrum of motions using a unified general policy. Videos and additional information can be found at https://gmt-humanoid.github.io.

Building image classification models remains cumbersome in data-scarce domains, where collecting large labeled datasets is impractical. In-context learning (ICL) has emerged as a promising paradigm for few-shot image classification (FSIC), enabling models to generalize across domains without gradient-based adaptation. However, prior work has largely overlooked a critical component of ICL-based FSIC pipelines: the role of image embeddings. In this work, we present PictSure, an ICL framework that places the embedding model -- its architecture, pretraining, and training dynamics -- at the center of analysis. We systematically examine the effects of different visual encoder types, pretraining objectives, and fine-tuning strategies on downstream FSIC performance. Our experiments show that the training success and the out-of-domain performance are highly dependent on how the embedding models are pretrained. Consequently, PictSure manages to outperform existing ICL-based FSIC models on out-of-domain benchmarks that differ significantly from the training distribution, while maintaining comparable results on in-domain tasks. Code can be found at https://github.com/PictSure/pictsure-library.

Automatic creation of 3D scenes for immersive VR presence has been a significant research focus for decades. However, existing methods often rely on either high-poly mesh modeling with post-hoc simplification or massive 3D Gaussians, resulting in a complex pipeline or limited visual realism. In this paper, we demonstrate that such exhaustive modeling is unnecessary for achieving compelling immersive experience. We introduce ImmerseGen, a novel agent-guided framework for compact and photorealistic world modeling. ImmerseGen represents scenes as hierarchical compositions of lightweight geometric proxies, i.e., simplified terrain and billboard meshes, and generates photorealistic appearance by synthesizing RGBA textures onto these proxies. Specifically, we propose terrain-conditioned texturing for user-centric base world synthesis, and RGBA asset texturing for midground and foreground scenery. This reformulation offers several advantages: (i) it simplifies modeling by enabling agents to guide generative models in producing coherent textures that integrate seamlessly with the scene; (ii) it bypasses complex geometry creation and decimation by directly synthesizing photorealistic textures on proxies, preserving visual quality without degradation; (iii) it enables compact representations suitable for real-time rendering on mobile VR headsets. To automate scene creation from text prompts, we introduce VLM-based modeling agents enhanced with semantic grid-based analysis for improved spatial reasoning and accurate asset placement. ImmerseGen further enriches scenes with dynamic effects and ambient audio to support multisensory immersion. Experiments on scene generation and live VR showcases demonstrate that ImmerseGen achieves superior photorealism, spatial coherence and rendering efficiency compared to prior methods. Project webpage: https://immersegen.github.io.

We present a two-speaker automatic speech recognition (ASR) system that combines DiCoW -- a diarization-conditioned variant of Whisper -- with DiariZen, a diarization pipeline built on top of Pyannote. We first evaluate both systems in out-of-domain (OOD) multilingual scenarios without any fine-tuning. In this scenario, DiariZen consistently outperforms the baseline Pyannote diarization model, demonstrating strong generalization. Despite being fine-tuned on English-only data for target-speaker ASR, DiCoW retains solid multilingual performance, indicating that encoder modifications preserve Whisper's multilingual capabilities. We then fine-tune both DiCoW and DiariZen on the MLC-SLM challenge data. The fine-tuned DiariZen continues to outperform the fine-tuned Pyannote baseline, while DiCoW sees further gains from domain adaptation. Our final system achieves a micro-average tcpWER/CER of 16.75% and ranks second in Task 2 of the MLC-SLM challenge. Lastly, we identify several labeling inconsistencies in the training data -- such as missing speech segments and incorrect silence annotations -- which can hinder diarization fine-tuning. We propose simple mitigation strategies to address these issues and improve system robustness.

Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought (Long CoT) reasoning have demonstrated remarkable cross-domain generalization capabilities. However, the underlying mechanisms supporting such transfer remain poorly understood. We hypothesize that cross-domain generalization arises from shared abstract reasoning prototypes -- fundamental reasoning patterns that capture the essence of problems across domains. These prototypes minimize the nuances of the representation, revealing that seemingly diverse tasks are grounded in shared reasoning structures.Based on this hypothesis, we propose ProtoReasoning, a framework that enhances the reasoning ability of LLMs by leveraging scalable and verifiable prototypical representations (Prolog for logical reasoning, PDDL for planning).ProtoReasoning features: (1) an automated prototype construction pipeline that transforms problems into corresponding prototype representations; (2) a comprehensive verification system providing reliable feedback through Prolog/PDDL interpreters; (3) the scalability to synthesize problems arbitrarily within prototype space while ensuring correctness. Extensive experiments show that ProtoReasoning achieves 4.7% improvement over baseline models on logical reasoning (Enigmata-Eval), 6.3% improvement on planning tasks, 4.0% improvement on general reasoning (MMLU) and 1.0% on mathematics (AIME24). Significantly, our ablation studies confirm that learning in prototype space also demonstrates enhanced generalization to structurally similar problems compared to training solely on natural language representations, validating our hypothesis that reasoning prototypes serve as the foundation for generalizable reasoning in large language models.

Computer use agents are LLM-based agents that can directly interact with a graphical user interface, by processing screenshots or accessibility trees. While these systems are gaining popularity, their safety has been largely overlooked, despite the fact that evaluating and understanding their potential for harmful behavior is essential for widespread adoption. To address this gap, we introduce OS-Harm, a new benchmark for measuring safety of computer use agents. OS-Harm is built on top of the OSWorld environment and aims to test models across three categories of harm: deliberate user misuse, prompt injection attacks, and model misbehavior. To cover these cases, we create 150 tasks that span several types of safety violations (harassment, copyright infringement, disinformation, data exfiltration, etc.) and require the agent to interact with a variety of OS applications (email client, code editor, browser, etc.). Moreover, we propose an automated judge to evaluate both accuracy and safety of agents that achieves high agreement with human annotations (0.76 and 0.79 F1 score). We evaluate computer use agents based on a range of frontier models - such as o4-mini, Claude 3.7 Sonnet, Gemini 2.5 Pro - and provide insights into their safety. In particular, all models tend to directly comply with many deliberate misuse queries, are relatively vulnerable to static prompt injections, and occasionally perform unsafe actions. The OS-Harm benchmark is available at https://github.com/tml-epfl/os-harm.

地球正在快速变暖，但你知道吗？早在 3 亿多年前，类似的气候剧变就曾引发海洋生命的巨大波动。南京大学研究团队在《Science Advances》发表研究报告：在晚古生代（约3.4亿至2.5亿年前），地球缓慢变冷时，海洋生物加速演化、种类剧增；而一旦气候急剧变暖，尤其是火山喷发带来的升温，便引发大规模物种灭绝。研究主角是一群叫做䗴类有孔虫（fusuline）的远古单细胞海洋生物，它们个体虽小，但数量惊人（图1），曾主宰海底世界，被誉为“碳酸盐岩工厂”。团队发现，9180 多万年间，这些生物经历了两次“多样化爆发”和四次“灭绝危机”。尤其在 2.6亿 年前峨眉山大规模火山喷发前后，体型较大的䗴类几乎绝迹；而 2.52 亿年前的二叠纪末期超级火山事件，更彻底终结了这个庞大家族的演化历程。值得警惕的是，人类活动引发的现代全球变暖，其速度已远超古代峨眉山玄武岩和二叠纪末火山事件带来的变暖速率。当前的海洋生态系统或正经历类似䗴类曾遭遇的命运考验。

在人类的演化过程中，寄生虫如头虱、跳蚤和绦虫一直伴随左右。但现代最强大的寄生物并非是吸血的无脊椎动物，而是智能手机。智能手机寄生于我们的时间、注意力和个人信息，为科技公司及其广告商谋利。从演化和寄生的角度看，智能手机对社会构成了独一无二的风险。寄生虫的生存依赖于宿主，离开宿主会很快死亡，以头虱为例，它给人类带来的代价主要是痒。智能手机改变了我们的生活，以至于很多人都离不开它。它带来的代价是一部分人沦为其奴隶，导致睡眠不足、线下关系薄弱以及情绪紊乱。人类与智能手机的关系一开始是互利共生（mutualism），但逐渐的演变为寄生关系。它提供的流行应用不是为了用户的利益，而是通过操纵我们的行为和情绪为其开发商和广告商谋利。用户是宿主，而智能手机就是寄生物。我们需要对其进行限制，至少能恢复部分互利共生的关系，但科技寡头们的实力非普通人能抵挡。

中科院的研究人员报告成功培育出一只来自双父亲的小鼠，并且健康成年。生物铭印（Biological imprinting）是一种基因表达的遗传模式，涉及到特定基因的激活或关闭，取决于它们来自父亲还是母亲。研究人员发现，如果调整铭印基因的选择，能为独特的生殖能力开启大门，如完全来自父系的胚胎。当小鼠胚胎以正常方式形成时，父系和母系 DNA 结合在一起。此种结合产生了铭印基因的精确平衡。仅有父系的胚胎中，与生长相关的特定基因可能会被过度刺激，研究团队选择性地修改这些基因，使得纯父系胚胎能成熟。研究负责人表示，该研究提供了强有力的证据，表明铭印基因异常是哺乳动物单性生殖的主要障碍。

数十个公民社会组织呼吁欧盟立即采取行动打破科技寡头对数字世界的控制，认为通过控制数字世界，科技寡头不只是支配着市场，还支配着民主。欧洲的数字经济应该服务于欧洲公民的需求，而不是科技寡头亿万富翁 CEO 的需求。欧盟委员会需要强有力的执行欧盟的数字法规和竞争法律对抗科技寡头。少数科技寡头集中控制着我们的核心数字基础设施——包括搜索引擎、社交媒体、应用商店和云计算服务。寡头们对其数字帝国拥有不受约束的权力，使其能对公民滥用权利、剥削企业并压制竞争对手。当少数亿万富翁和科技寡头控制互联网时，他们会利用其权力——以及巨额利润——去影响政治话语，干涉民主法律。打破科技寡头的垄断首先从拆分 Google 开始。

在试验发现验证用户年龄在技术可行且可集成到现有服务后，澳大利亚针对 16 岁以下儿童的社交媒体禁令准备继续推进。这一结果对反对禁令的主要社平台 Meta Platforms、TikTok 和 Snap 而言是一次沉重的打击。由政府支持的试验结果为法律在今年年底前生效铺平道路。其它国家可能会效仿澳大利亚的做法，世界各国都面临寻找方法保护儿童免受网络有害内容的伤害。

负责苹果软件工程的高级副总裁 Craig Federighi 接受采访时表示无意统一桌面和平板的操作系统，不会将 macOS 引入到 iPad。他解释说，苹果不想设计不伦不类的东西，如创造出一种船与车的结合体，或者勺子和叉子的结合体叉勺。以叉勺为例，它既不是一把好的勺子也不是一柄好的叉子。苹果的平板电脑 iPad Pro 被认为已经模糊了平板和笔记本电脑之间的界限，但 Craig Federighi 坚称，Mac 和 iPad 的用途截然不同，苹果为 iPad 引入 Mac 上的部分元素不是为了让 iPad 变成 Mac，而是互相借鉴同时保持独立。

Sailfish OS 开发商 Jolla 放弃了它的强制性软件更新付费订阅制商业模式，宣布改为自愿付费制。Sailfish OS 是在诺基亚 MeeGo 基础上开发的移动操作系统，目前运行 Sailfish OS 的智能手机主要是 Jolla C2 社区版。此前 Jolla 对 Jolla C2 的软件更新收取 25 欧元的年费（第一年的费用包含在售价内），这一付费模式显然不太受欢迎，Jolla 宣布所有 Jolla C2 智能手机都将获得至少五年的更新。

根据 Counterpoint Research 的初步数据，2025 年 4 月和 5 月 iPhone 销量同比增长 15%，为疫情爆发以来最高。推动销量增长的主要来自苹果的两大市场美国和中国，其中中国的销量尤为突出（可能和苹果系列产品进入各地的国补有关），此前 iPhone 在华销量被华为等本土厂商的手机产品超越，而 5 月 iPhone 再次跃居榜首。

塑料污染已成为普遍存在的环境问题；全球海洋垃圾中的大部分是塑料碎片，后者已被证实对海洋生物、生态系统及沿海经济构成严重威胁。大部分塑料污染源自陆地，它们是通过河流、污水或刮风进入海洋的。在进入海洋系统的塑料制品中，一次性塑料购物袋的危害尤为严重；为遏制此类污染，各地已实施从收费到全面禁用等多种政策措施。研究人员就美国塑料袋禁令和收费政策对减少海岸线塑料袋垃圾数量的影响进行了评估。作者发现：与未实施此类法规的地区相比，塑料袋管制法规使得清理过程中收集的塑料袋在垃圾中的占比下降了 25% 至47%。塑料袋管制政策或能使野生动物缠绕事件减少 30% 至 37%。研究结果显示，对消费者收取塑料袋使用费的政策可能是减少塑料垃圾最有效的手段。

对美国科罗拉多州恐龙岭恐龙脚印的分析显示，恐龙求偶时可能也会跳舞。研究报告发表在《Cretaceous Research》期刊上。恐龙岭出土了大量标志性恐龙化石。研究人员利用航拍照片分析了恐龙留下的大量痕迹。总的来说，这些痕迹可分为两大类。第一类呈碗状，形状模糊不清；第二类占大多数，它们更长、更细，有时还会相互重叠。Buntin和同事发现，这些刮痕分布密集且出现在不同的地层中，这表明该地点曾被同一种类的恐龙多次光顾。这些痕迹也呈现出独特的模式。一些印记显示，恐龙在用爪子刮擦地面时可能顺时针转体，这表明它们在跳一种独特而重复的舞蹈。圆形的凹痕表明，这些刮痕后来可能被改造成了巢穴，筑巢行为在现代某些鸟类中很常见。跳舞的恐龙可能是似鸟龙—— 一种类似鸵鸟的食草动物，或者是棘龙—— 一种类似霸王龙的恐龙。

任天堂游戏机 Switch 2 尚未在中国大陆正式发售，但多个大型电商平台上都有香港版出售，价格多在 4000 元人民币以上。与香港的售价以及在日本出售的多语言版相比，价格普遍偏高。任天堂解释称：“在中国大陆尚未销售 Switch 2。在香港是通过子公司在进行官方销售”。京东社媒官方账号介绍，Switch 2 与全球销售同步，在 6 月 5 日上线，称这是“任天堂官方授权首批货源”。分析师表示“在中国大陆仍有增长空间。虽然大陆的游戏机爱好者有限，但热度很高，消费者愿意为进口版支付溢价”。第一代 Switch 由腾讯代理，对于是否代理第二代 Switch，腾讯未正面回应。