OrangeBot.AI Digest — 2025-08-02

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LLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving due to the lack of clear supervision signals when solely using natural language. Dedicated domain-specific languages like Lean provide clear supervision via formal verification of proofs, enabling effective training through reinforcement learning. In this work, we propose Seed-Prover, a lemma-style whole-proof reasoning model. Seed-Prover can iteratively refine its proof based on Lean feedback, proved lemmas, and self-summarization. To solve IMO-level contest problems, we design three test-time inference strategies that enable both deep and broad reasoning. Seed-Prover proves 78.1% of formalized past IMO problems, saturates MiniF2F, and achieves over 50\% on PutnamBench, outperforming the previous state-of-the-art by a large margin. To address the lack of geometry support in Lean, we introduce a geometry reasoning engine Seed-Geometry, which outperforms previous formal geometry engines. We use these two systems to participate in IMO 2025 and fully prove 5 out of 6 problems. This work represents a significant advancement in automated mathematical reasoning, demonstrating the effectiveness of formal verification with long chain-of-thought reasoning.

With the development of multimodal reasoning models, Computer Use Agents (CUAs), akin to Jarvis from "Iron Man", are becoming a reality. GUI grounding is a core component for CUAs to execute actual actions, similar to mechanical control in robotics, and it directly leads to the success or failure of the system. It determines actions such as clicking and typing, as well as related parameters like the coordinates for clicks. Current end-to-end grounding models still achieve less than 65\% accuracy on challenging benchmarks like ScreenSpot-pro and UI-Vision, indicating they are far from being ready for deployment. % , as a single misclick can result in unacceptable consequences. In this work, we conduct an empirical study on the training of grounding models, examining details from data collection to model training. Ultimately, we developed the Phi-Ground model family, which achieves state-of-the-art performance across all five grounding benchmarks for models under 10B parameters in agent settings. In the end-to-end model setting, our model still achieves SOTA results with scores of \textbf{43.2} on ScreenSpot-pro and \textbf{27.2} on UI-Vision. We believe that the various details discussed in this paper, along with our successes and failures, not only clarify the construction of grounding models but also benefit other perception tasks. Project homepage: https://zhangmiaosen2000.github.io/Phi-Ground/{https://zhangmiaosen2000.github.io/Phi-Ground/}

Spoken Dialogue Models (SDMs) have recently attracted significant attention for their ability to generate voice responses directly to users' spoken queries. Despite their increasing popularity, there exists a gap in research focused on comprehensively understanding their practical effectiveness in comprehending and emulating human conversations. This is especially true compared to text-based Large Language Models (LLMs), which benefit from extensive benchmarking. Human voice interactions are inherently more complex than text due to characteristics unique to spoken dialogue. Ambiguity poses one challenge, stemming from semantic factors like polysemy, as well as phonological aspects such as heterograph, heteronyms, and stress patterns. Additionally, context-dependency, like omission, coreference, and multi-turn interaction, adds further complexity to human conversational dynamics. To illuminate the current state of SDM development and to address these challenges, we present a benchmark dataset in this paper, which comprises 1,079 instances in English and Chinese. Accompanied by an LLM-based evaluation method that closely aligns with human judgment, this dataset facilitates a comprehensive exploration of the performance of SDMs in tackling these practical challenges.

Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, most current industrial systems remain heavily reliant on historical co-occurrence patterns and log-fitting objectives, i.e., optimizing for past user interactions without explicitly modeling user intent. This log-fitting approach often leads to overfitting to narrow historical preferences, failing to capture users' evolving and latent interests. As a result, it reinforces filter bubbles and long-tail phenomena, ultimately harming user experience and threatening the sustainability of the whole recommendation ecosystem. To address these challenges, we rethink the overall design paradigm of recommender systems and propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline. By integrating large language models (LLMs) into key stages of user interest mining, item retrieval, and explanation generation, RecGPT transforms log-fitting recommendation into an intent-centric process. To effectively align general-purpose LLMs to the above domain-specific recommendation tasks at scale, RecGPT incorporates a multi-stage training paradigm, which integrates reasoning-enhanced pre-alignment and self-training evolution, guided by a Human-LLM cooperative judge system. Currently, RecGPT has been fully deployed on the Taobao App. Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders: users benefit from increased content diversity and satisfaction, merchants and the platform gain greater exposure and conversions. These comprehensive improvement results across all stakeholders validates that LLM-driven, intent-centric design can foster a more sustainable and mutually beneficial recommendation ecosystem.

Visual-Language-Action (VLA) models have emerged as a popular paradigm for learning robot manipulation policies that can follow language instructions and generalize to novel scenarios. Recent work has begun to explore the incorporation of latent actions, an abstract representation of visual change between two frames, into VLA pre-training. In this paper, we introduce villa-X, a novel Visual-Language-Latent-Action (ViLLA) framework that advances latent action modeling for learning generalizable robot manipulation policies. Our approach improves both how latent actions are learned and how they are incorporated into VLA pre-training. Together, these contributions enable villa-X to achieve superior performance across simulated environments including SIMPLER and LIBERO, as well as on two real-world robot setups including gripper and dexterous hand manipulation. We believe the ViLLA paradigm holds significant promise, and that our villa-X provides a strong foundation for future research.

Feed-forward 3D modeling has emerged as a promising approach for rapid and high-quality 3D reconstruction. In particular, directly generating explicit 3D representations, such as 3D Gaussian splatting, has attracted significant attention due to its fast and high-quality rendering, as well as numerous applications. However, many state-of-the-art methods, primarily based on transformer architectures, suffer from severe scalability issues because they rely on full attention across image tokens from multiple input views, resulting in prohibitive computational costs as the number of views or image resolution increases. Toward a scalable and efficient feed-forward 3D reconstruction, we introduce an iterative Large 3D Reconstruction Model (iLRM) that generates 3D Gaussian representations through an iterative refinement mechanism, guided by three core principles: (1) decoupling the scene representation from input-view images to enable compact 3D representations; (2) decomposing fully-attentional multi-view interactions into a two-stage attention scheme to reduce computational costs; and (3) injecting high-resolution information at every layer to achieve high-fidelity reconstruction. Experimental results on widely used datasets, such as RE10K and DL3DV, demonstrate that iLRM outperforms existing methods in both reconstruction quality and speed. Notably, iLRM exhibits superior scalability, delivering significantly higher reconstruction quality under comparable computational cost by efficiently leveraging a larger number of input views.

Large language models interact with users through a simulated 'Assistant' persona. While the Assistant is typically trained to be helpful, harmless, and honest, it sometimes deviates from these ideals. In this paper, we identify directions in the model's activation space-persona vectors-underlying several traits, such as evil, sycophancy, and propensity to hallucinate. We confirm that these vectors can be used to monitor fluctuations in the Assistant's personality at deployment time. We then apply persona vectors to predict and control personality shifts that occur during training. We find that both intended and unintended personality changes after finetuning are strongly correlated with shifts along the relevant persona vectors. These shifts can be mitigated through post-hoc intervention, or avoided in the first place with a new preventative steering method. Moreover, persona vectors can be used to flag training data that will produce undesirable personality changes, both at the dataset level and the individual sample level. Our method for extracting persona vectors is automated and can be applied to any personality trait of interest, given only a natural-language description.

While Reinforcement Learning (RL) has achieved remarkable success in language modeling, its triumph hasn't yet fully translated to visuomotor agents. A primary challenge in RL models is their tendency to overfit specific tasks or environments, thereby hindering the acquisition of generalizable behaviors across diverse settings. This paper provides a preliminary answer to this challenge by demonstrating that RL-finetuned visuomotor agents in Minecraft can achieve zero-shot generalization to unseen worlds. Specifically, we explore RL's potential to enhance generalizable spatial reasoning and interaction capabilities in 3D worlds. To address challenges in multi-task RL representation, we analyze and establish cross-view goal specification as a unified multi-task goal space for visuomotor policies. Furthermore, to overcome the significant bottleneck of manual task design, we propose automated task synthesis within the highly customizable Minecraft environment for large-scale multi-task RL training, and we construct an efficient distributed RL framework to support this. Experimental results show RL significantly boosts interaction success rates by 4times and enables zero-shot generalization of spatial reasoning across diverse environments, including real-world settings. Our findings underscore the immense potential of RL training in 3D simulated environments, especially those amenable to large-scale task generation, for significantly advancing visuomotor agents' spatial reasoning.

Multimodal large language models (MLLMs) enable vision-language reasoning, yet often generate plausible outputs that are factually incorrect or visually ungrounded, thereby compromising their reliability. Direct preference optimization (DPO) is a common strategy for correcting hallucinations by aligning model outputs with human preferences. Existing DPO strategies typically treat hallucination-related preferences as fixed targets, relying on static supervision signals during training. This approach tends to overfit to superficial linguistic cues in preference data, leading to distributional rigidity and spurious correlations that impair grounding in causally relevant visual information. To overcome this limitation, we propose TARS, a token-adaptive preference strategy that reformulates DPO as a min-max optimization problem. TARS maximizes token-level distributional shifts under semantic constraints to simulate alignment uncertainty, and simultaneously minimizes the expected preference loss under these controlled perturbations. This joint objective preserves causal grounding while mitigating overfitting to preference patterns, thereby reducing hallucinations in multimodal reasoning. We evaluate TARS on multiple hallucination benchmarks and find consistently strong performance. Using only 4.8k preference samples and no expert feedback, TARS reduces hallucination rates from 26.4% to 13.2% and decreases cognition value from 2.5 to 0.4. It outperforms standard DPO and matches GPT-4o on several key metrics.

We introduce NeRF-GS, a novel framework that jointly optimizes Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). This framework leverages the inherent continuous spatial representation of NeRF to mitigate several limitations of 3DGS, including sensitivity to Gaussian initialization, limited spatial awareness, and weak inter-Gaussian correlations, thereby enhancing its performance. In NeRF-GS, we revisit the design of 3DGS and progressively align its spatial features with NeRF, enabling both representations to be optimized within the same scene through shared 3D spatial information. We further address the formal distinctions between the two approaches by optimizing residual vectors for both implicit features and Gaussian positions to enhance the personalized capabilities of 3DGS. Experimental results on benchmark datasets show that NeRF-GS surpasses existing methods and achieves state-of-the-art performance. This outcome confirms that NeRF and 3DGS are complementary rather than competing, offering new insights into hybrid approaches that combine 3DGS and NeRF for efficient 3D scene representation.

Precise automated understanding of agricultural tasks such as disease identification is essential for sustainable crop production. Recent advances in vision-language models (VLMs) are expected to further expand the range of agricultural tasks by facilitating human-model interaction through easy, text-based communication. Here, we introduce AgroBench (Agronomist AI Benchmark), a benchmark for evaluating VLM models across seven agricultural topics, covering key areas in agricultural engineering and relevant to real-world farming. Unlike recent agricultural VLM benchmarks, AgroBench is annotated by expert agronomists. Our AgroBench covers a state-of-the-art range of categories, including 203 crop categories and 682 disease categories, to thoroughly evaluate VLM capabilities. In our evaluation on AgroBench, we reveal that VLMs have room for improvement in fine-grained identification tasks. Notably, in weed identification, most open-source VLMs perform close to random. With our wide range of topics and expert-annotated categories, we analyze the types of errors made by VLMs and suggest potential pathways for future VLM development. Our dataset and code are available at https://dahlian00.github.io/AgroBenchPage/ .

Art style classification remains a formidable challenge in computational aesthetics due to the scarcity of expertly labeled datasets and the intricate, often nonlinear interplay of stylistic elements. While recent dual-teacher self-supervised frameworks reduce reliance on labeled data, their linear projection layers and localized focus struggle to model global compositional context and complex style-feature interactions. We enhance the dual-teacher knowledge distillation framework to address these limitations by replacing conventional MLP projection and prediction heads with Kolmogorov-Arnold Networks (KANs). Our approach retains complementary guidance from two teacher networks, one emphasizing localized texture and brushstroke patterns, the other capturing broader stylistic hierarchies while leveraging KANs' spline-based activations to model nonlinear feature correlations with mathematical precision. Experiments on WikiArt and Pandora18k demonstrate that our approach outperforms the base dual teacher architecture in Top-1 accuracy. Our findings highlight the importance of KANs in disentangling complex style manifolds, leading to better linear probe accuracy than MLP projections.

Data arrives at our senses as a continuous stream, smoothly transforming from one instant to the next. These smooth transformations can be viewed as continuous symmetries of the environment that we inhabit, defining equivalence relations between stimuli over time. In machine learning, neural network architectures that respect symmetries of their data are called equivariant and have provable benefits in terms of generalization ability and sample efficiency. To date, however, equivariance has been considered only for static transformations and feed-forward networks, limiting its applicability to sequence models, such as recurrent neural networks (RNNs), and corresponding time-parameterized sequence transformations. In this work, we extend equivariant network theory to this regime of `flows' -- one-parameter Lie subgroups capturing natural transformations over time, such as visual motion. We begin by showing that standard RNNs are generally not flow equivariant: their hidden states fail to transform in a geometrically structured manner for moving stimuli. We then show how flow equivariance can be introduced, and demonstrate that these models significantly outperform their non-equivariant counterparts in terms of training speed, length generalization, and velocity generalization, on both next step prediction and sequence classification. We present this work as a first step towards building sequence models that respect the time-parameterized symmetries which govern the world around us.

Since its introduction, softmax attention has become the backbone of modern transformer architectures due to its expressiveness and scalability across a wide range of tasks. However, the main drawback of softmax attention is the quadratic memory requirement and computational complexity with respect to the sequence length. By replacing the softmax nonlinearity, linear attention and similar methods have been introduced to avoid the quadratic bottleneck of softmax attention. Despite these linear forms of attention being derived from the original softmax formulation, they typically lag in terms of downstream accuracy. While strong intuition of the softmax nonlinearity on the query and key inner product suggests that it has desirable properties compared to other nonlinearities, the question of why this discrepancy exists still remains unanswered. This work demonstrates that linear attention is an approximation of softmax attention by deriving the recurrent form of softmax attention. Using this form, each part of softmax attention can be described in the language of recurrent neural networks (RNNs). Describing softmax attention as an RNN allows for the ablation of the components of softmax attention to understand the importance of each part and how they interact. In this way, our work helps explain why softmax attention is more expressive than its counterparts.

Arabic poses a particular challenge for natural language processing (NLP) and information retrieval (IR) due to its complex morphology, optional diacritics and the coexistence of Modern Standard Arabic (MSA) and various dialects. Despite the growing global significance of Arabic, it is still underrepresented in NLP research and benchmark resources. In this paper, we present an enhanced Dense Passage Retrieval (DPR) framework developed specifically for Arabic. At the core of our approach is a novel Attentive Relevance Scoring (ARS) that replaces standard interaction mechanisms with an adaptive scoring function that more effectively models the semantic relevance between questions and passages. Our method integrates pre-trained Arabic language models and architectural refinements to improve retrieval performance and significantly increase ranking accuracy when answering Arabic questions. The code is made publicly available at https://github.com/Bekhouche/APR{GitHub}.

Google 宣布将利用 AI 技术估算美国用户年龄是否年满 18 岁。年龄估算将在未来几周内推出，一开始将只会影响少数用户，之后它计划进一步扩大范围。Google 称，它将使用用户搜索过的信息或观看过的 YouTube 视频类型去判断用户的年龄。如果 Google 认为用户年龄未满 18 岁，它将对其采取对未成年人用户实施的相同限制。

苹果公布了2021 年以来最强劲的季度营收增长，iPhone 销量增长 13%，总营收增长 10%。CEO 库克(Tim Cook)表示，约 1% 的营收增长可归因于消费者为应对潜在关税而购买更多产品。苹果最重要的产品仍然是 iPhone，销售额同比增长 13% 至 445.8 亿美元。苹果在中国市场的销售额同比增长 4% 达到 153.7 亿美元。库克表示一大原因是中国的国补政策，对该公司产品非常有帮助。

EA 雄心勃勃的希望《战地6》能吸引千万玩家，能长线运营，游戏将提供免费大逃杀模式，以及被称为 Battlefield Portal 的门户关卡编辑器，允许玩家创建自定义地图和玩法，该编辑器使用了开源游戏引擎 Godot。用户使用 Godot 创建的数据会通过翻译层翻译到《战地6》使用的私有引擎 Frostbite 4。此前 Blender 基金会曾使用 Godot 引擎和 Blender 3D 软件开发了一款小游戏，暂时不清楚 EA 或 DICE 是否会向 Godot 项目捐款。

为阻止蚊子传播登革热病毒，巴西将释放数百万只实验室培育的蚊子，这些蚊子携带了沃尔巴克氏体细菌（Wolbachia bacteria），通过传播沃尔巴克氏体细菌阻止蚊子携带登革热病毒。该项目旨在未来十年内保护 40 个城市的 1.4 亿居民。巴西此前已在尼特罗伊（Niteroi）市测试了释放携带沃尔巴克氏体细菌的蚊子，效果显著，登革热病例下降了约 90%。现在该市几乎所有蚊子都携带沃尔巴克氏体细菌，Chikungunya(基孔肯雅热)病例和寨卡（Zika）病例也分别下降超过 96% 和 99%。沃尔巴克氏细菌天然存在于约半数的昆虫物种中，它让登革热病毒无法在蚊子体内复制，从而有效遏制登革热病毒传播。

英伟达宣布，自 2025 年 10 月起新 Game Ready 驱动更新将不再支持 Maxwell、Pascal 或 Volta GPU 架构。这意味着 GeForce GTX 1060 之类的旧显卡将不再获得针对新游戏进行优化的驱动版本。英伟达还表示将于 2026 年 10 月停止所有 Windows 10 驱动支持，比微软官方的 Windows 10 终止支持时间晚一年。此后如果 Windows 10 用户希望继续获得较新型号显卡的新驱动，他们需要升级到 Windows 11。英伟达表示会在 2028 年 10 月之前为 Maxwell、Pascal 和 Volta 系列显卡发布季度安全更新。

睡眠呼吸暂停是一种在睡眠期间，暂停呼吸或呼吸减弱症状导致的睡眠紊乱。每一次的暂停期间可从数秒钟到数分钟不等，而且整晚发生好几次。在一般情况下，这个症状会产生吵杂的打鼾声。几十年来，睡眠呼吸暂停的主要治疗方法是持续正压气道通气治疗（CPAP Therapy），患者睡前需戴上面罩，面罩连接到 CPAP 机器，它通过压入空气保持呼吸道畅通。CPAP 虽然有效，但对很多人而言非常不方便。现在专注治疗睡眠呼吸暂停的制药公司 Apnimed 发布的新闻稿，宣布一种口服药物即将面世，它公布了第二轮三期临床试验的积极结果，这种口服药物可在睡前服用，帮助保持呼吸道畅通。如果最终获得批准，这种药物可能会改变许多人的生活。

西南财经大学的研究人员在《Regional Science and Urban Economics》期刊上发表论文，分析了中国第一条高铁的环境影响。中国第一条主要高铁——京沪高铁——于 2011 年 6 月 30 日开通，高铁连接了两大最具有经济活力的地区-环渤海都市圈和长三角，两大地区的人口总数占到了全国总人口的四分之一。研究人员利用 NASA 高分辨卫星数据（中国在 2013 年前缺乏可靠的地面空气污染监测数据）发现，高铁开通半年内沿路各县的颗粒物浓度下降了 6.2%，这一影响随后两年持续加强，表明随着城际出行者逐渐转变出行方式，环境效益不断增强。汽车尾气是城市空气污染的主要来源，占到了北京和上海两大超级城市 PM2.5 排放的 45–52%，其中很大一部分是区域间交通。研究人员估计，高铁开通每年产生的外部健康效益价值约 210 亿元人民币，相当于建设成本的很大一部分。

根据发表在《PLOS One》期刊上的一项研究，人类每天在室内环境吸入逾 7 万个微塑料。塑料是当代最严重的环境问题之一，其中纳米大小的颗粒能吸入肺部。法国图卢兹大学的科学家量化了每天可能吸入的塑料粉尘量。研究小组从自家公寓和汽车中采集了 16 个室内空气样本，使用拉曼光谱学(Raman Spectroscopy)测量微塑料浓度。结果显示我们每天的塑料颗粒吸入量非常大。公寓空气样本的中值浓度为每立方米 528 个微塑料颗粒，汽车内微塑料颗粒浓度则高达每立方米 2238 个。这些颗粒 94% 直径小于 10 微米，足以吸入后深入肺组织。研究团队估计，成年人每天从室内环境中吸入大约 7.1 万个微塑料颗粒，其中 6.8 万个小于 10 微米。人类平均 90% 的时间处于室内，包括家、工作场所、商店、交通工具等，会在不自觉中吸入微塑料污染物。

一项长达 14 年的跟踪研究发现，人工甜味剂饮料相比含糖饮料显著增加了罹患 2 型糖尿病风险。饮料用人工甜味剂降低含糖量但保持甜味，此举被认为产生更健康的饮料，但越来越多的研究显示人工甜味剂可能存在代谢风险。为评估含糖饮料和人工甜味饮料如何影响健康，研究人员追踪了 36,608 名参与者，平均随访时间为 13.9 年。研究发现，相比完全不饮用含糖饮料的人，饮用一罐人工甜味饮料会使得罹患 2 型糖尿病的风险增加 38%。饮用相同量含糖饮料的人的风险增加 23%。此前的研究发现，人工甜味剂阿斯巴甜（aspartame）会引发类似于蔗糖的餐后胰岛素反应，其它人工甜味剂糖精（saccharin）和三氯蔗糖（sucralose）则与肠道菌群紊乱和糖耐量受损有关。

Google 被指控在十几年里向苹果和三星等公司支付了数十亿美元，成为智能手机和浏览器的默认搜索引擎。默认位置让 Google 成为全球使用率第一的搜索引擎，获取每年逾 3000 亿美元的广告收入。根据《American Economic Journal:Microeconomics》期刊上的一项研究，部分国家的政策禁止 Google 付费成为默认搜索引擎，这种干预措施有效降低了 Google 在当地的市场份额，其中俄罗斯和土耳其的结果尤为明显。

网信办官网发表声明：英伟达算力芯片被曝出存在严重安全问题。此前，美议员呼吁要求美出口的先进芯片必须配备“追踪定位”功能。美人工智能领域专家透露，英伟达算力芯片“追踪定位”“远程关闭”技术已成熟。为维护中国用户网络安全、数据安全，依据《网络安全法》《数据安全法》《个人信息保护法》有关规定，国家互联网信息办公室于 2025 年 7 月 31 日约谈了英伟达公司，要求英伟达公司就对华销售的 H20 算力芯片漏洞后门安全风险问题进行说明并提交相关证明材料。

AI 也许是软件开发的未来，但人类尚未做好把手从方向盘上移开的准备。Veracode 发布了 AI 生成代码的安全性报告《2025 GenAI Code Security Report》，逾百个大模型完成了 80 项编程任务，但 AI 生成的代码有约 45% 存在安全漏洞。这些安全漏洞很多都属于 OWASP（Open Worldwide Application Security Project）Top 10 漏洞。报告发现，当 AI 给予选项写安全或不安全代码时，几乎一半的时间它选择了错误的路径。

维沃移动通信有限公司(Vivo)宣布了用 Rust 开发的蓝河操作系统 BlueOS。Rust 开发的蓝河内核支持 POSIX 接口和 Rust std（标准库），支持 ARM32、ARM64、RISCV32、RISCV64 芯片架构。Vivo 称 BlueOS 是首个从内核到系统框架全栈由 Rust 语言编写的操作系统，Rust 语言的一系列安全特性，在编译阶段就可以发现内存使用不当导致的安全漏洞，从源头实现天生更安全。

澳大利亚周三表示，针对 16 岁以下儿童的社交媒体禁令覆盖的网站将包含 YouTube，不再豁免 Google 旗下的视频共享平台。这一决定是在监管机构的调查发现 37% 的未成年人报告了 YouTube 的有害内容，在所有社媒平台之间比例最高。儿童社媒禁令将于今年 12 月生效。YouTube 表示，澳大利亚 13 至 15 岁的儿童有近四分之三使用该平台，它不应该被归类为社媒平台，因为它主要托管视频，是一个视频共享平台，有大量高质量的内容库，用户越来越多的通过电视观看内容，它不是社交媒体。禁令覆盖的社媒平台 Meta Platforms、TikTok 和 Snap 则都表示 YouTube 和它们一样都使用算法根据用户活动推荐内容。