OrangeBot.AI Digest — 2025-12-29

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Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior knowledge, interpret new information, and integrate evidence dispersed across a document, as revealed by the Mindscape-Aware Capability of humans in psychology. Current Retrieval-Augmented Generation (RAG) systems lack such guidance and therefore struggle with long-context tasks. In this paper, we propose Mindscape-Aware RAG (MiA-RAG), the first approach that equips LLM-based RAG systems with explicit global context awareness. MiA-RAG builds a mindscape through hierarchical summarization and conditions both retrieval and generation on this global semantic representation. This enables the retriever to form enriched query embeddings and the generator to reason over retrieved evidence within a coherent global context. We evaluate MiA-RAG across diverse long-context and bilingual benchmarks for evidence-based understanding and global sense-making. It consistently surpasses baselines, and further analysis shows that it aligns local details with a coherent global representation, enabling more human-like long-context retrieval and reasoning.

Recent advances in diffusion-based video generation have opened new possibilities for controllable video editing, yet realistic video object insertion (VOI) remains challenging due to limited 4D scene understanding and inadequate handling of occlusion and lighting effects. We present InsertAnywhere, a new VOI framework that achieves geometrically consistent object placement and appearance-faithful video synthesis. Our method begins with a 4D aware mask generation module that reconstructs the scene geometry and propagates user specified object placement across frames while maintaining temporal coherence and occlusion consistency. Building upon this spatial foundation, we extend a diffusion based video generation model to jointly synthesize the inserted object and its surrounding local variations such as illumination and shading. To enable supervised training, we introduce ROSE++, an illumination aware synthetic dataset constructed by transforming the ROSE object removal dataset into triplets of object removed video, object present video, and a VLM generated reference image. Through extensive experiments, we demonstrate that our framework produces geometrically plausible and visually coherent object insertions across diverse real world scenarios, significantly outperforming existing research and commercial models.

Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks such as visual grounding, segmentation, and captioning. However, their ability to perceive perceptual-level image features remains limited. In this work, we present UniPercept-Bench, a unified framework for perceptual-level image understanding across three key domains: Aesthetics, Quality, Structure and Texture. We establish a hierarchical definition system and construct large-scale datasets to evaluate perceptual-level image understanding. Based on this foundation, we develop a strong baseline UniPercept trained via Domain-Adaptive Pre-Training and Task-Aligned RL, enabling robust generalization across both Visual Rating (VR) and Visual Question Answering (VQA) tasks. UniPercept outperforms existing MLLMs on perceptual-level image understanding and can serve as a plug-and-play reward model for text-to-image generation. This work defines Perceptual-Level Image Understanding in the era of MLLMs and, through the introduction of a comprehensive benchmark together with a strong baseline, provides a solid foundation for advancing perceptual-level multimodal image understanding.

The development of GUI agents could revolutionize the next generation of human-computer interaction. Motivated by this vision, we present MAI-UI, a family of foundation GUI agents spanning the full spectrum of sizes, including 2B, 8B, 32B, and 235B-A22B variants. We identify four key challenges to realistic deployment: the lack of native agent-user interaction, the limits of UI-only operation, the absence of a practical deployment architecture, and brittleness in dynamic environments. MAI-UI addresses these issues with a unified methodology: a self-evolving data pipeline that expands the navigation data to include user interaction and MCP tool calls, a native device-cloud collaboration system routes execution by task state, and an online RL framework with advanced optimizations to scale parallel environments and context length. MAI-UI establishes new state-of-the-art across GUI grounding and mobile navigation. On grounding benchmarks, it reaches 73.5% on ScreenSpot-Pro, 91.3% on MMBench GUI L2, 70.9% on OSWorld-G, and 49.2% on UI-Vision, surpassing Gemini-3-Pro and Seed1.8 on ScreenSpot-Pro. On mobile GUI navigation, it sets a new SOTA of 76.7% on AndroidWorld, surpassing UI-Tars-2, Gemini-2.5-Pro and Seed1.8. On MobileWorld, MAI-UI obtains 41.7% success rate, significantly outperforming end-to-end GUI models and competitive with Gemini-3-Pro based agentic frameworks. Our online RL experiments show significant gains from scaling parallel environments from 32 to 512 (+5.2 points) and increasing environment step budget from 15 to 50 (+4.3 points). Finally, the native device-cloud collaboration system improves on-device performance by 33%, reduces cloud model calls by over 40%, and preserves user privacy.

Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the sampling process to maintain editing consistency. However, this sampling strategy overly relies on source information, which negatively affects the edits in the target image (e.g., failing to change the subject's atributes like pose, number, or color as instructed). In this work, we propose ProEdit to address this issue both in the attention and the latent aspects. In the attention aspect, we introduce KV-mix, which mixes KV features of the source and the target in the edited region, mitigating the influence of the source image on the editing region while maintaining background consistency. In the latent aspect, we propose Latents-Shift, which perturbs the edited region of the source latent, eliminating the influence of the inverted latent on the sampling. Extensive experiments on several image and video editing benchmarks demonstrate that our method achieves SOTA performance. In addition, our design is plug-and-play, which can be seamlessly integrated into existing inversion and editing methods, such as RF-Solver, FireFlow and UniEdit.

Large Language Models (LLMs) are increasingly deployed in time-critical systems, such as robotics, autonomous driving, embodied intelligence, and industrial automation, where generating accurate responses within a given time budget is crucial for decision-making, control, or safety-critical tasks. However, the auto-regressive generation process of LLMs makes it challenging to model and estimate the end-to-end execution time. Furthermore, existing efficient inference methods based on a fixed key-value (KV) cache eviction ratio struggle to adapt to varying tasks with diverse time budgets, where an improper eviction ratio may lead to incomplete inference or a drop in response performance. In this paper, we propose TimeBill, a novel time-budgeted inference framework for LLMs that balances the inference efficiency and response performance. To be more specific, we propose a fine-grained response length predictor (RLP) and an execution time estimator (ETE) to accurately predict the end-to-end execution time of LLMs. Following this, we develop a time-budgeted efficient inference approach that adaptively adjusts the KV cache eviction ratio based on execution time prediction and the given time budget. Finally, through extensive experiments, we demonstrate the advantages of TimeBill in improving task completion rate and maintaining response performance under various overrun strategies.

Large vision-language models (VLMs) often benefit from intermediate visual cues, either injected via external tools or generated as latent visual tokens during reasoning, but these mechanisms still overlook fine-grained visual evidence (e.g., polylines in charts), generalize poorly across domains, and incur high inference-time cost. In this paper, we propose Bi-directional Perceptual Shaping (BiPS), which transforms question-conditioned masked views into bidirectional where-to-look signals that shape perception during training. BiPS first applies a KL-consistency constraint between the original image and an evidence-preserving view that keeps only question-relevant regions, encouraging coarse but complete coverage of supporting pixels. It then applies a KL-separation constraint between the original and an evidence-ablated view where critical pixels are masked so the image no longer supports the original answer, discouraging text-only shortcuts (i.e., answering from text alone) and enforcing fine-grained visual reliance. Across eight benchmarks, BiPS boosts Qwen2.5-VL-7B by 8.2% on average and shows strong out-of-domain generalization to unseen datasets and image types.

Weather modeling requires both accurate prediction and mechanistic interpretation, yet existing methods treat these goals in isolation, separating generation from understanding. To address this gap, we present Omni-Weather, the first multimodal foundation model that unifies weather generation and understanding within a single architecture. Omni-Weather integrates a radar encoder for weather generation tasks, followed by unified processing using a shared self-attention mechanism. Moreover, we construct a Chain-of-Thought dataset for causal reasoning in weather generation, enabling interpretable outputs and improved perceptual quality. Extensive experiments show Omni-Weather achieves state-of-the-art performance in both weather generation and understanding. Our findings further indicate that generative and understanding tasks in the weather domain can mutually enhance each other. Omni-Weather also demonstrates the feasibility and value of unifying weather generation and understanding.

The ability for AI agents to "think with images" requires a sophisticated blend of reasoning and perception. However, current open multimodal agents still largely fall short on the reasoning aspect crucial for real-world tasks like analyzing documents with dense charts/diagrams and navigating maps. To address this gap, we introduce O3-Bench, a new benchmark designed to evaluate multimodal reasoning with interleaved attention to visual details. O3-Bench features challenging problems that require agents to piece together subtle visual information from distinct image areas through multi-step reasoning. The problems are highly challenging even for frontier systems like OpenAI o3, which only obtains 40.8% accuracy on O3-Bench. To make progress, we propose InSight-o3, a multi-agent framework consisting of a visual reasoning agent (vReasoner) and a visual search agent (vSearcher) for which we introduce the task of generalized visual search -- locating relational, fuzzy, or conceptual regions described in free-form language, beyond just simple objects or figures in natural images. We then present a multimodal LLM purpose-trained for this task via reinforcement learning. As a plug-and-play agent, our vSearcher empowers frontier multimodal models (as vReasoners), significantly improving their performance on a wide range of benchmarks. This marks a concrete step towards powerful o3-like open systems. Our code and dataset can be found at https://github.com/m-Just/InSight-o3 .

Automatic presentation slide generation can greatly streamline content creation. However, since preferences of each user may vary, existing under-specified formulations often lead to suboptimal results that fail to align with individual user needs. We introduce a novel task that conditions paper-to-slides generation on user-specified preferences. We propose a human behavior-inspired agentic framework, SlideTailor, that progressively generates editable slides in a user-aligned manner. Instead of requiring users to write their preferences in detailed textual form, our system only asks for a paper-slides example pair and a visual template - natural and easy-to-provide artifacts that implicitly encode rich user preferences across content and visual style. Despite the implicit and unlabeled nature of these inputs, our framework effectively distills and generalizes the preferences to guide customized slide generation. We also introduce a novel chain-of-speech mechanism to align slide content with planned oral narration. Such a design significantly enhances the quality of generated slides and enables downstream applications like video presentations. To support this new task, we construct a benchmark dataset that captures diverse user preferences, with carefully designed interpretable metrics for robust evaluation. Extensive experiments demonstrate the effectiveness of our framework.

Recent text-to-video generation models exhibit remarkable progress in visual realism, motion fidelity, and text-video alignment, yet they remain fundamentally limited in their ability to generate socially coherent behavior. Unlike humans, who effortlessly infer intentions, beliefs, emotions, and social norms from brief visual cues, current models tend to render literal scenes without capturing the underlying causal or psychological logic. To systematically evaluate this gap, we introduce the first benchmark for social reasoning in video generation. Grounded in findings from developmental and social psychology, our benchmark organizes thirty classic social cognition paradigms into seven core dimensions, including mental-state inference, goal-directed action, joint attention, social coordination, prosocial behavior, social norms, and multi-agent strategy. To operationalize these paradigms, we develop a fully training-free agent-based pipeline that (i) distills the reasoning mechanism of each experiment, (ii) synthesizes diverse video-ready scenarios, (iii) enforces conceptual neutrality and difficulty control through cue-based critique, and (iv) evaluates generated videos using a high-capacity VLM judge across five interpretable dimensions of social reasoning. Using this framework, we conduct the first large-scale study across seven state-of-the-art video generation systems. Our results reveal substantial performance gaps: while modern models excel in surface-level plausibility, they systematically fail in intention recognition, belief reasoning, joint attention, and prosocial inference.

Execution-based feedback like unit testing is widely used in the development of coding agents through test-time scaling (TTS) and reinforcement learning (RL). This paradigm requires scalable and reliable collection of unit test cases to provide accurate feedback, and the resulting feedback is often sparse and cannot effectively distinguish between trajectories that are both successful or both unsuccessful. In contrast, execution-free feedback from reward models can provide more fine-grained signals without depending on unit test cases. Despite this potential, execution-free feedback for realistic software engineering (SWE) agents remains underexplored. Aiming to develop versatile reward models that are effective across TTS and RL, however, we observe that two verifiers with nearly identical TTS performance can nevertheless yield very different results in RL. Intuitively, TTS primarily reflects the model's ability to select the best trajectory, but this ability does not necessarily generalize to RL. To address this limitation, we identify two additional aspects that are crucial for RL training: classification accuracy and calibration. We then conduct comprehensive controlled experiments to investigate how to train a robust reward model that performs well across these metrics. In particular, we analyze the impact of various factors such as training data scale, policy mixtures, and data source composition. Guided by these investigations, we introduce SWE-RM, an accurate and robust reward model adopting a mixture-of-experts architecture with 30B total parameters and 3B activated during inference. SWE-RM substantially improves SWE agents on both TTS and RL performance. For example, it increases the accuracy of Qwen3-Coder-Flash from 51.6% to 62.0%, and Qwen3-Coder-Max from 67.0% to 74.6% on SWE-Bench Verified using TTS, achieving new state-of-the-art performance among open-source models.

This paper presents a new state-of-the-art algorithm for exact 3times3 matrix multiplication over general non-commutative rings, achieving a rank-23 scheme with only 58 scalar additions. This improves the previous best additive complexity of 60 additions without a change of basis. The result was discovered through an automated search combining ternary-restricted flip-graph exploration with greedy intersection reduction for common subexpression elimination. The resulting scheme uses only coefficients from {-1, 0, 1}, ensuring both efficiency and portability across arbitrary fields. The total scalar operation count is reduced from 83 to 81.

Large reasoning models (LRMs) are typically trained using reinforcement learning with verifiable reward (RLVR) to enhance their reasoning abilities. In this paradigm, policies are updated using both positive and negative self-generated rollouts, which correspond to distinct sample polarities. In this paper, we provide a systematic investigation into how these sample polarities affect RLVR training dynamics and behaviors. We find that positive samples sharpen existing correct reasoning patterns, while negative samples encourage exploration of new reasoning paths. We further explore how adjusting the advantage values of positive and negative samples at both the sample level and the token level affects RLVR training. Based on these insights, we propose an Adaptive and Asymmetric token-level Advantage shaping method for Policy Optimization, namely A3PO, that more precisely allocates advantage signals to key tokens across different polarities. Experiments across five reasoning benchmarks demonstrate the effectiveness of our approach.

5102 人参加了 Blender 基金会的年度调查。结果显示：大部分参与者的年龄在 19-35 岁之间；16% 的参与者来自美国，德国是 7.26%，中国是 5.61%，印度是 5.46%；三分之一参与者是美术师，17% 的参与者是设计师；半数参与者每天使用 Blender；逾半数参与者是因为免费或有趣或开源而使用 Blender；用户会长时间一直使用一个 LTS 版本；大部分参与者不使用 AI，只有 7% 的用户经常使用 AI。

Sarah Sajedi 在泰国披披岛（Phi Phi Island）旅游时为壮观的海景所吸引，但她低头一看，发现海滩上遍地是塑料瓶。她在攻读博士学位期间分析了逾 140 篇论文以判断塑料瓶对人体的影响。她发现，人平均每年从食物和饮用水中摄入 39,000-52,000 个微塑料颗粒，而每天饮用瓶装水的人每年摄入的微塑料颗粒要多 90,000 个。Sajedi 建议人们在紧急情况下饮用塑料瓶装水，不应该日常饮用。微塑料是 1-5 毫米之间的塑料颗粒，而纳米塑料则小于 1 微米。塑料颗粒肉眼不可见，但在瓶子的生产、储存、运输和分解过程中会不断产生。与其它通过食物链进入人体的塑料颗粒不同，塑料瓶中的微塑料更令人担忧，因为它们会随饮用水直接摄入体内。一旦进入人体微塑料颗粒能进入血液循环，到达重要器官，引发慢性炎症反应，使细胞暴露于氧化应激之下，进而导致激素系统紊乱、生殖功能受损和神经系统损伤。

今年六月与以色列爆发冲突期间伊朗一度断网数天，它也加强了网络审查。Tor 项目开发的 Snowflake 是伊朗使用最广泛的网络流量混淆工具。为更好的应对伊朗对网桥——不公开的 Tor 中继但可以通过各种方法获取——的封锁，Tor 项目开发了可插拔传输协议 Conjure——其功能类似为避免垃圾邮件而生成的临时邮件地址，一个网桥地址被封锁不影响用户获取新网桥地址。俄罗斯也加强了对网络的审查，Tor 项目去年推出的模拟 HTTPS 流量的新可插拔传输协议 WebTunnel 在俄罗斯很受欢迎。俄罗斯在 6 月加强了对 WebTunnel 网桥地址的封锁，Tor 项目开始通过 Telegram 分发 WebTunnel 网桥。Tor 项目计划明年部署 Conjure 和持续改进 WebTunnel，更好的应对封锁。

模仿超级马里奥兄弟的开源游戏《超级企鹅（SuperTux》在时隔多年之后释出了下一个大版本 v0.7 的首个 Beta 版本。《超级企鹅》游戏主角是 Linux 吉祥物企鹅，游戏玩法是类似超级马力欧兄弟的横版过关。游戏于 2003 年开始开发，上一个大版本 v0.6 是在 2019 年发布的。v0.7 版本是一次重大更新，重做了多个世界，引入了全新的美术和音乐等内容，核心玩法不变，但游戏体验可能和以前完全不同。游戏提供了 Flatpak 打包的版本。

可汗学院（Khan Academy）创始人 Sal Khan 建议受益于自动化的企业捐出 1% 的利润帮助被 AI 取代的工人接受重就业培训。他认为这不是慈善，而是符合公司的自身利益，因为如果企业利润飙升的同时失业率增加，可能促使公众支持加强监管和增税，或支持禁止自动化。资助工人重新接受培训对大企业而言是微不足道的，几乎没有任何压力，但对公众而言却具有重大意义。全球最大的十几家公司年利润逾万亿美元，捐出百分之一利润就能创办一个每年有百亿美元的基金，拿出一部分就足以打造一个中心化的技能培训平台。基金可由独立非营利组织运营，通过与企业协调，确保所培训的技能符合市场需求。

耶鲁大学医学院的科学家发现自闭症患者大脑与神经正常者大脑之间的分子差异。根据发表在《The American Journal of Psychiatry》期刊上的研究，自闭症患者大脑一种特定类型的谷氨酸受体数量较少，谷氨酸是大脑中最常见的兴奋性神经递质。减少的谷氨酸受体数量可能与自闭症多种特征相关。大脑神经元通过电信号和称为神经递质的化学信使相互沟通。当电流在神经元中传递时，会促使释放神经递质，进而将信号传递给其它神经元。这种信号传递可以是兴奋性的，也可以是抑制性的。兴奋性信号主要触发神经递质谷氨酸的释放，起到绿灯作用，告诉其它神经元激发；抑制性信号则起到刹车作用抑制神经活动。大脑需要两种信号保持精确平衡才能正常运作。自闭症病因的主要假说之一是大脑中兴奋性和抑制性信号失衡。

市场研究咨询公司 TrendForce 高级研究副总裁 Avril Wu 建议如果需要什么电子产品现在就去购买。AI 热导致了全世界出现内存短缺，而内存短缺将会影响各种电子产品的价格。TrendForce 的数据显示，RAM 芯片的需求量比供应量高 10%，且需求的增长速度是如此之快以至于厂商每月不得不支付更高的价格采购。最常见的内存芯片 DRAM 本季度的支付价比上季度高 50%。如果厂商需要提前拿到芯片则需要支付两到三倍的价格。DRAM 的价格在下一季度还将上涨 40%，她预计 2026 年价格不会下降。随着内存厂商如美光将生产重心转移到 AI 相关的高端内存领域，那么 PC、手机、游戏机和电视等消费电子产品的内存芯片供应量将会减少。Wu 女士表示在可预计的未来电子产品价格会继续上升。

Google 的 AI Overview 错误指控加拿大的一位知名音乐家是性犯罪者。Ashley MacIsaac 曾三次获得加拿大的最高音乐奖项 Juno 奖。他最近表示 Halifax 北部的一个原居民部落向他出示了 Google 的 AI Overview，取消了原定于 12 月 19 日的音乐会，他才得知这一网络虚假信息。他认为这构成了诽谤，如果问题是在过国境时发生难以想象会发生什么。Google 的不正确信息来自于一篇与他同姓的男子的网文。

美国德州的一名父亲利用女儿手机的家长控制功能，在女儿圣诞节遛狗时遭持刀绑架后，成功找到并解救了她。案发地点是休斯顿郊区 Porter，15 岁女孩外出遛狗后没有及时回家，其父随后通过家长控制功能定位了女儿手机，发现她位于距离家 3.2 公里外的临县林区。父亲赶到后发现女儿及狗在一辆皮卡内，车内还有一名半裸男子。女儿在父亲帮助下成功逃脱后父亲立即报警。绑架嫌疑人被确认为 23 岁的 Giovanni Rosales Espinoza，警方以绑架重罪和猥亵儿童罪对其提起诉讼。

GNOME 开发者分享了该项目的代码和语言统计数据：截至 2025 年底，总代码行数 6,692,516 行，其中应用 1,611,526 行。44.8% 的 GNOME Core 应用用 C 语言开发，之后是 20.7% 的 Vala，13.8% 的 JavaScript，Rust 语言开发的应用占 10.3%，Python 占 6.9%，C++ 占 3.45%。GNOME Circle Apps 中最流行的语言是 Rust，占比高达 41.7%，C 仅占 5.56%，Python 29.2%，Vala 12.5%，JavaScript 9.72%。C 语言在 GNOME 组件/库中占主导位置，占比 76%，其次是 Rust 的 10.3% 和 Vala 的 3.77%。

一项研究发现，YouTube 向新用户推荐的视频信息流逾五分之一是 AI 垃圾。Kapwing 调查了全球 15000 个最受欢迎的 YouTube 频道——每个国家排名前 100 的频道——发现其中 278 个频道的内容全是 AI 垃圾——AI 生成的低质量视频。据估计，这些 AI 内容频道累计播放量逾 630 亿次，总订阅用户 2.21 亿，每年创造约 1.17 亿美元的收入。研究人员还创建了一个新 YouTube 账号，发现 YouTube 推荐算法推荐的前 500 个视频有 104 个 是 AI 垃圾。社交平台如 X 和 Meta 也都存在类似的情况。此前的一项分析发现，YouTube 增长最快的频道中近十分之一是 AI 垃圾。Kapwing 的研究还发现，西班牙人可能最热衷此类内容，该国人口中的半数约 2000 万人关注这些 AI 频道，埃及有 1800 万人关注，美国 1450 万，巴西 1350 万。

据报道，山东录用公务员要“深入挖掘考察对象报考动机”“立体掌握考察人选‘8小时内外’表现”“核查重点人员网络社交平台言论”等相关内容。检索发现，今年早些时候，新疆阿勒泰地区、湖北两地也曾明确“公务员录用增加网络行为审查”“延伸了解考生网络日常言行和表现”。中央党校（国家行政学院）教授竹立家表示，“对公务员的这种政治要求、价值观要求、道德要求，过去一直是我们公务员考试的重点内容，在政治理论考试等部分均有涉及。”考察考生“网上网下”的言行则比之前更细致了一些。

Calibre 本月初释出了受争议的更新，引入 AI“讨论”功能。该功能由 Amir Tehrani 于今年 8 月提出，Calibre 作者兼维护者 Kovid Goyal 欣然接受，首个包含 AI 功能的版本于 12 月初释出。他承诺 Calibre 绝对不会未经用户明确同意选择加入的情况下使用第三方 AI 服务。对于用户的反对，他强调不会移除。在更新之后用户会在“视图”菜单下看到 AI 功能“Discuss selected books with AI”。如果没有配置 GitHub AI 的访问令牌或 Google AI 的 API 密钥，或者通过 LM Studio 或 Ollama 本地运行模型，该功能实际上没什么用。对大多数用户而言，它就是几个没什么用的菜单项。对于用户对 AI 的强烈反对，Goyal 也提供了选择，他放出了 Calibre 的所有版本提供下载，用户可以从 0.6.x-8.x 选择任意一个版本使用。目前开源社区还不存在功能上能替代 Calibre 的电子书管理软件。Calibre 加入 AI 凸显出无论用户是否想要，AI 都在缓慢的渗透到我们的生活之中。

在 FFmpeg 开发者发出 DMCA 通知后，GitHub 下架了瑞芯（Rockchip）的开源媒体处理库 Media Process Platform。FFmpeg 是在 2024 年 2 月首次指控瑞芯违反了该开源项目使用的 LGPL 许可证。瑞芯拷贝了 FFmpeg 的源代码库，删除了原始作者声明，宣称自己拥有这些代码库的所有权，然后使用了不兼容于 LGPL 的 Apache 许可证重新发布代码。FFmpeg 项目等待了差不多两年时间，但瑞芯的最后回应显示它无意解决问题。DMCA 通知要求删除侵权文件，或恢复正确的署名以及使用与 LGPL 兼容的许可证。