OrangeBot.AI Digest — 2026-10-05

Video model that turns script into viral social media videos

A React library for morphable particle interfaces

Route requests to the right LLM for cost, latency & quality

A third place for people & their agents, starting with inbox

Trade, bridge and move stablecoins across Arc

The review app for code your agent writes

An AI cursor companion that shows you what to click

Ship AI agents within minutes. Infrastructure for Agents

Translate, shorten, fix or rewrite any text near your cursor

A free and open-source resume builder

Answer Engine Optimization Audit

Save X Articles and threads as PDF, Markdown or EPUB

Live interview & meeting copilot for Mac, from your resume

A native control room for your Claude Code agents

The First Video Editor built for Agents

A companion that talks with a person for months should come to understand them. It should remember what they said, infer who they are, and know when the past bears on the message in front of it. Testing this requires a real person's record, and such records are private, so benchmarks generate the person and the questions and settle in advance what matters. We release \bench, ten real relationships with an AI companion: 27,218 messages over up to 120 days, released as the conversation and four files derived from it, a profile, a persona, a chat ground truth and a question set, each citing the messages it rests on. Every chat label carries the reasoning trace that produced it, checked stage by stage against the conversation. Three findings follow. First, the past is rarely needed and far away. Pooled measures mislead: a recency window finds the required message for 95.9\% of probes and 2.2\% of those that need memory, and at the natural rate 96\% of the gain from supplying recorded evidence comes from messages that need none. Second, no detector we tried can tell when memory is needed on real messages, authored questions over the same histories leak the cue, and labeling the same messages as memories raises their use by ten to fourteen points. Third, three agent systems reconstruct the persona with the same F1 at a 31-fold difference in cost.

Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natural testbed, because one solved structure yields thousands of exactly checkable spatial and topological statements. We ask: can learning to fold proteins teach general models reusable reasoning capabilities? To answer this, we build FoldingCorpus, a protein-derived question-answer dataset, and Fold2Reason, a recipe that post-trains on it through two complementary signals: discrete structural answers predicted via the model's native language head, and continuous 3D geometry decoded from the same shared representations. On FoldBench, Fold2Reason achieves structure prediction scores 2.7 to 3.5 times those of Qwen3.5-9B. Beyond protein structure prediction, it improves performance on all 10 benchmarks spanning spatial, graph, scientific, and general reasoning, raising macro-average accuracy from 45.09% to 48.33% (+3.23 pp), with positive gains on all 10 benchmarks, while matched controls built from random, synthetic, and shuffled structure yield substantially smaller or negative gains. Our work shows that non-linguistic, structure-dense scientific data can systematically improve broad reasoning in language models, making a solved scientific problem a practical source of post-training supervision.

Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training keeps them from benefiting directly from rapidly advancing general-purpose vision-language models (VLMs). In parallel, recent agentic robotic systems leverage VLMs for high-level reasoning or coding agents for robot control, but often depend on extensive external models and tools, introducing additional complexity and cost. This motivates us to ask: Can a general-purpose VLM itself operate a robot more like the human teleoperator by reasoning directly from observations, issuing actions, and continuously adapting to execution feedback, without relying on external models such as learned action experts, coding agents or grounding tools like SAM3? In this work, we introduce MotorMind, a robot manipulation harness that connects VLM-proposed mid-level actions to deterministic robot control and feedback, with asynchronous monitoring and background memory updates. Without task-specific policy training, coding agents, or additional grounding tools such as SAM3, MotorMind achieves 66.7% success on the base LIBERO-PRO suites and 53.8% under perturbations, compared with at most 13.3% and 19.2%, respectively, for the prior zero-shot methods we evaluate. The same interface reaches 95% average success on a real xArm6 robot across direct manipulation and human-perturbation settings. Replacing the backbone with a stronger VLM further improves performance, while the remaining failures - primarily due to visual grounding, embodied reasoning, and action knowledge - decrease as VLM capability improves. These results show that a general-purpose VLM, when equipped with an appropriate mid-level action representation and asynchronous execution harness, can perform effective zero-shot robotic manipulation.

World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an initial observation and instruction. However, existing WAMs struggle with long-horizon prediction, as generating dense video rollouts is highly inefficient. Some recent WAMs address this by predicting a single future frame without generating the full video, but this approach neglects how to progress toward the goal. We present ProWAM, a progressive world action model that jointly predicts actions and an ordered sequence of sparse visual sub-goals, providing explicit visual guidance to anchor action generation throughout task execution. This design scales naturally, as sub-goal prediction can be learned from large-scale action-free videos, allowing the video backbone to offload complex visual planning from the action policy. For efficient action generation, ProWAM executes a single video-backbone forward pass to cache sparse sub-goal features, eliminating iterative full-video generation and requiring only lightweight action denoising during replanning. Across extensive evaluations, ProWAM achieves superior out-of-distribution robustness. On simulation benchmarks, it sets new state-of-the-art results on LIBERO-Plus (85.8%) and randomized RoboTwin (75.7%), outperforming the strongest baseline with relative gains of up to +35.9%. On RoboCasa365, ProWAM achieves a 48.1% success rate and 18.2% on the challenging Composite-Unseen split, ranking 4th overall. Crucially, in zero-shot real-world experiments, ProWAM achieves 70.0% success, outperforming the strongest baseline by +15.0 (from 55.0% to 70.0%, a +27.3% relative gain) in novel scenes. These results demonstrate the value of progress-indexed visual foresight for closed-loop control. Our program is in https://sii-ferenas.github.io/ProWAM-page.

The strong generalization performance of on-policy post-training paradigms has motivated studies of their parameter update behaviors. However, these studies treat the observed behaviors only as byproducts in on-policy training, overlooking their potential to serve as optimization principles for improving the generalization of other paradigms such as supervised fine-tuning (SFT). To address this limitation, we investigate whether there exists a specific on-policy update behavior that can achieve such improvements. First, our analyses reveal that SFT updates parameters along consistent directions, while the on-policy paradigm continuously adjusts the direction during training. This difference inspires us to focus on the cumulative update direction of each parameter as a promising behavior. Then, we evaluate its effectiveness for improving generalization by proposing On-Policy direction-constrained Supervised Fine-Tuning (OPSFT), which constrains SFT updates to the direction identified by on-policy paradigms. The strong performance of OPSFT indicates that the generalization advantage of on-policy paradigms can be transferred to SFT through the parameter update direction. Once such a direction is identified, even SFT can generalize with its updates constrained to this direction. This finding offers two practical benefits by combining the strong generalization of on-policy paradigms with the advantages of SFT, including the high training efficiency and ability to leverage high-quality trajectories. For efficiency, we identify update directions that support strong generalization using a few on-policy training steps, and subsequently apply OPSFT to achieve high training efficiency. For leveraging high-quality trajectories, OPSFT can utilize these trajectories to continue improving a post-trained model along its update direction without disrupting the ability learned from on-policy training.

World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotated robot trajectories. Observation-only videos contain rich evidence about interaction dynamics, but existing approaches typically use them either to pretrain visual representations that must later be adapted for control, or to infer latent actions that are subsequently grounded to robot commands. We present NAVA-WAM, which introduces native action-prior learning by directly pretraining the action policy from observation-only videos, avoiding indirect representation-to-control transfer or a separate latent-action model. Our training consists of two stages. First, we pretrain on observation-only videos, where future-video flow-matching supervision over visual transitions is propagated through transition-structured joint attention to optimize the Action-DiT and learn action-relevant priors. Second, we use action-labeled demonstrations to post-train the Action-DiT for robot control through joint video--action flow matching, while asymmetric attention decouples the visual branch from iterative action denoising and enables efficient action-only inference. Extensive experiments show that NAVA-WAM consistently outperforms prior approaches under both in-distribution and out-of-distribution settings, while demonstrating strong action-label efficiency and effective real-robot generalization. These results establish native action-prior learning as an effective approach to directly pretrain action policies from observation-only videos, providing a scalable path beyond action-labeled robot data.

We introduce HyperBrowseComp, a multilingual and multimodal browsing benchmark comprising 423 manually authored and human-validated questions across 13 languages, written by native or highly proficient speakers. Questions are designed to be extremely challenging. Each question targets a concise, publicly verifiable answer whose discovery requires locating obscure evidence, following multi-step clue chains, or inspecting heterogeneous sources such as videos, scanned documents, images, or maps. Easier questions are filtered out by evaluating them with models without internet access to reduce the likelihood that they can be answered with parametric knowledge alone. We evaluate several models using provider-native search and a shared external retrieval harness under a common agent protocol. To contextualize model performance and effort, we also conduct a human evaluation on a sample of the questions. HyperBrowseComp provides a challenging testbed for persistent information seeking across languages and evidence modalities, with difficulty arising from discovering and connecting evidence on the open web.

Physical trajectories contain more than snapshots of a system: they also reveal how its states evolve under governing conditions. However, representation learning for parametric partial differential equations (PDEs) has largely relied on reconstruction-based objectives that emphasize recovering observed physical fields. In this paper, we investigate predictive representation pretraining as an alternative to reconstruction-based learning. We find that predictive representations preserve rich physical information, yet this advantage alone does not ensure accurate field evolution. Based on these observations, we introduce PDE-JEPA for parametric PDE dynamics. Specifically, we first train an encoder using a masked-latent prediction to capture the underlying regularities of PDE dynamics. To explicitly adapt the pretrained representation toward a more dynamics-aligned state space, we then introduce a geometry projector that aligns latent trajectory geometry with the evolution geometry of physical fields. Finally, building on this geometry-aligned latent space, we further develop a physics-structured latent predictor that decomposes the dynamics into parameter-independent evolution and parameter-dependent response components. Extensive experiments on nine widely used PDE benchmarks demonstrate that our framework outperforms existing state-of-the-art methods by an average of 33.4\% in-distribution, while achieving an average improvement of 51.4\% when extrapolating to unseen governing parameters. The project page is available https://tanpig-x.github.io/PDE-JEPA/{here}.

Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment challenge, since a few commitments during denoising sharply reduce the uncertainty over the remaining masked positions and shape much of the response. Most post-training recipes for dLMs do not use this signal to decide which tokens to train on: they typically train on the final text or assign rewards to whole denoising steps, rather than selecting the individual commitments that shape the response. We introduce Pivot-SD, an efficient offline self-distillation framework that supervises only these high-impact commitments (pivots). Pivot-SD selects pivots using an information-gain metric measuring uncertainty reduction over the remaining masked positions. Pivots from successful trajectories are trained with cross-entropy, and pivots from failed trajectories with targeted unlikelihood, leaving the rest of the failed trajectory untouched. Using only 200 questions and four rollouts each, Pivot-SD improves LLaDA-8B-Instruct over full-sequence SFT and budget-matched diffusion RL baselines across math and code benchmarks.

On-policy distillation (OPD) trains a student on the responses it generates. Existing LLM multi-teacher OPD transfers what specialists predict through their output distributions. We introduce Latent-MOPD, to our knowledge the first representation-level multi-teacher OPD method for LLMs. It integrates existing specialists through both their predictions and the hidden states used to compute them, without additional teacher training. To coordinate representation supervision from multiple specialists, we select late-layer targets according to the teacher-student relationship, bridge unequal hidden widths with a shared projection, and group updates by domain. Each teacher's supervision gradually shifts from hidden states to token predictions, with both channels using the same routed specialist. In our main same-family setting, Latent-MOPD outperforms the token-only, representation-only and uniform-averaging baselines on all nine benchmarks across math, code and logic. With the same parameter count as each teacher, the student also surpasses the per-benchmark best teacher on a majority of these benchmarks. With larger, separately developed cross-family teachers, Latent-MOPD outperforms both single-channel baselines on all benchmarks. A same-family all-layer representation-only control remains stable with domain-pure updates but collapses when teacher domains are interleaved within an update. Our results show that a single student can integrate capabilities from several specialists through both their output distributions and internal representations.

Existing Simulink benchmarks mainly evaluate whether generated models compile, execute, or resemble a reference model. These criteria do not establish whether a model satisfies its engineering requirements. We introduce SimuVerity, a benchmark of 101 text-to-executable Simulink model-generation tasks across ten engineering domains. For each task, executable-system profiles ground the engineering specification and four families of native simulation scenarios. A hierarchical evaluator first checks artifact delivery, native executability, and engineering qualification, then scores qualified models across six dimensions covering accuracy, output quality, mechanistic fidelity, control and causal integrity, operating-domain robustness, and dynamic response. We evaluate six agent systems with SimuVerity. The best system achieves an overall score of only 42.86. The results show that structural similarity is a poor proxy for engineering performance: capability bottlenecks arise both in producing qualified implementations and in satisfying multidimensional requirements after qualification. Meanwhile, some high-scoring models still exhibit severe visual-layout disorder. SimuVerity provides a systematic basis for assessing agents' engineering capabilities and diagnosing failures in executable Simulink model generation.

AI-generated content, often called AI slop, is increasingly common everywhere, particularly in academia. Slop in AI-generated scientific papers, however, has more complex patterns that cannot be easily detected by existing token-based AI detectors. Each part of such a paper looks plausible while the scientific reasoning that connects the parts breaks down, which can mislead how readers assess the work. We benchmark these failures as scientific slop through six measures across Structure, Argument, and Artifacts. We construct SciSlopBench with 390 AI-generated papers, mostly in computer science but spanning the life, social, and natural sciences, each paired with a human-written paper matched by research problem and contribution type. Our measures identify the AI paper in each pair with 85.9% accuracy, compared with 68.7% for Binoculars. Higher scientific slop accompanies lower ICLR ratings and distinguishes rejected from accepted papers above chance in every year from 2017 to 2025. Reducing these patterns, however, is not as simple as directly optimizing the measures. We therefore propose SciSlopHarness, a harness-level framework that guides a fixed LLM to revise slop only where the experiment records support the change. While standard revisions leave residual slop and direct slop-aware prompting triggers reward hacking, SciSlopHarness reduces the remaining AI-human gap by 63% over the strongest revision baseline without requiring human reference targets. Overall, we demonstrate that AI-generated scientific papers leave fundamental traces in their global reasoning, and that responsible mitigation demands strict evidentiary grounding rather than mere prose refinement.

Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing reward methods, however, often rely on coarse-grained holistic judgments, providing limited domain specificity and interpretability. We introduce LexReward, a taxonomy-driven framework for legal reward modeling. LexReward characterizes legal response quality along three complementary dimensions: Style, covering lexical and syntactic quality; Element, assessing legal subjects, facts, statutes, and decisions; and Chain, evaluating the order, completeness, correctness, and non-redundancy of legal reasoning. For each dimension, we develop rubrics that specify evaluation criteria and quality levels. The resulting rewards are used to construct pairwise preference data for Direct Preference Optimization (DPO) and reward-model training. Experiments show that the rubric-based rewards reliably distinguish legal responses of different quality and that DPO training on the preference data improves performance across all three dimensions. The learned reward models, LexRM, also support effective downstream optimization: each dimension-specific reward model improves policy performance in its corresponding dimension through reinforcement learning, without requiring reference answers at reward time. Dimension-wise analyses further support the effectiveness of the proposed taxonomy and reward construction.

We understand little about how capabilities acquired in one language carry over to another, or what governs this transfer: evaluations rely on incomparable, saturation-prone datasets and rarely examine its determinants jointly. Identifying what predicts transfer would let us avoid exhaustive evaluation across all language pairs and let developers target the factors that limit performance in low-resource languages. To evaluate cross-lingual capability transfer, we introduce Multilingual GSM-Symbolic, an extensible multilingual mathematical dataset covering 30,000 item-matched question-answer pairs and spanning 15 languages. It utilises symbolic templates to prevent overfitting and ensure generalisation by allowing generation of millions of high-quality variations from a single sample. Using Multilingual GSM-Symbolic, we quantify the largest determinants of capability as model size (β= 1.77), language resource level (β= 0.77), reasoning (β= 0.67) and typological distance (β= -0.25). This joint estimation allows these determinants to be expressed in terms of one another: a 32B model evaluated in Marathi performs like a 10B model in English. Our findings have important implications for model developers, showing that model size and reasoning narrow the performance gap between low- and high-resource languages (β= -0.27 and β= -0.20, respectively), while similar levers have little or no effect on typologically distant languages. Overall, our analysis framework explains 92% of between-language variation, but only 23% of the model-by-language variation, and predicts a model's performance on an unseen language within 6.0pp (r=.96). Incorporating measurements from just 10 templates in the target language reduces this to 4.19pp, enabling reasonable estimates of performance with little or no downstream dataset.

Long-video generation and world models have shown strong potential for interactive entertainment and embodied simulation by predicting future observations conditioned on user actions and historical memory. However, as memory sequences grow longer and their structures become increasingly complex, managing long-range spatial context becomes increasingly challenging, calling for a more intelligent and systematic memory-management strategy. Building on the advancing spatial reasoning capabilities of multimodal large language models (MLLMs) and the broader vision of unified models, we propose Spatial Memory Intelligence (SMI), the first framework to systematically employ an understanding model for spatial-memory management in long-video world models. SMI introduces four coordinated atomic operations: spatial clustering, within-cluster sparsification, action-aware retrieval, and reliability-aware filtering. Extensive experiments across multiple baselines, benchmarks, and world-model backbones demonstrate the effectiveness and generalizability of SMI, achieving comprehensive improvements in memory sparsity, generation stability, and spatial consistency.

Lora Kolodny / CNBC : SpaceX stock closes up 7.6% after Morgan Stanley called SPCX “cheap”, reaching its highest level since mid-June and returning Elon Musk to trillionaire status —  SpaceX shares rose almost 8% on Monday, reaching their highest since mid-June, shortly after Elon Musk's company held its record initial public offering.

Aisha Malik / TechCrunch : TikTok debuts Shopping Assistant, a conversational AI agent that helps users find and buy products, and Buy Direct for one-click purchases from the For You feed —  TikTok announced Monday that it's launching an AI shopping assistant and a new in-app checkout feature that lets users buy directly from brands.

Semafor : NY-based Reflection unveils Beam, an open model it says rivals Qwen3.8-Max on coding and agentic tasks and GLM 5.2 on reasoning, while using 3x-4× less compute —  Reflection AI, the startup billing itself as America's answer to open-source Chinese AI, is releasing its first model, called Beam.

The Information : Sources: Meta and Microsoft are working to cut their employees' use of Claude; Meta employees using Claude Code have dropped to ~30K from ~60K earlier this year —  Meta Platforms and Microsoft, two of Anthropic's biggest corporate customers, are working to cut their employees' use of Claude …

Dominic-Madori Davis / TechCrunch : Ghost, which makes a $3,499 computer designed for AI agents and includes an RTX Pro 4000 SFF Blackwell GPU, emerges from stealth with an $11M seed led by a16z —  For much of his life, Zain Javaid, 19, wanted to be a quant who uses math and data to guide his investment decisions.  And for a while, he was one.

Bloomberg : Former Anthropic researcher Jacob Coxon and representatives from Anthropic, Google, OpenAI, and Meta testified at a New York City Council hearing on AI safety —  Former Anthropic researcher Jacob Coxon warned that the race to develop artificial intelligence poses risks that the industry itself …

Jason Shubnell / The Block : The CFTC proposes a federal framework for crypto exchanges to offer retail customers leveraged and margined spot trading, without requiring congressional action —  The agency floated a new “crypto asset market” exchange category but said it can't require crypto to trade on CFTC platforms without Congress.

BBC : An official says the DOD has stopped using Anthropic's tools; sources: Claude was in use as recently as last week, including in military operations against Iran —  Technology reporter in San Francisco and  —  The US Department of Defence is no longer using Anthropic's AI tools …

OpenAI : To comply with the EU AI Act, OpenAI plans to add text watermarking for ChatGPT and Codex users in the EU and an opt-in setting for API customers globally —  - Starting today, API customers globally will be able to opt in to text watermarking for select models.

Annika Inampudi / Bloomberg : In a first-of-its-kind pilot in the US, Nolla Health will use AI to diagnose and prescribe acne medications to Utah patients without direct human oversight —  In a first-of-its-kind pilot, Nolla Health lets AI diagnose and issue prescriptions.  —  An AI-based healthcare startup on Monday …

Paul Sandle / Reuters : Meta, TikTok, and X challenge UK's Ofcom over the amount of info it is demanding under the Online Safety Act, saying it creates unprecedented regulatory burdens —  Meta (META.O), TikTok and X are challenging British regulator Ofcom over the amount of information it is demanding …

Zoya Hasan / Forbes : Valon, which makes software to help US mortgage companies cut red tape and costs, raised a $150M Series D from Ribbit, a16z, and others at a $2.3B valuation —  The U.S. mortgage market is massive, but its payment systems are antiquated and often analog.  Valon raised a $150 million Series D …

New York Magazine : Q&A with AI researchers Jacob Coxon, Daniel Kokotajlo, Alex Turner, and others on leaving AI labs, AGI's dangers, planned IPOs, AI safety, oversight, and more —  Exit interviews with defectors from OpenAI, Anthropic, and DeepMind.  —  On September 8, Jacob Coxon, a 27-year-old Anthropic employee …

Phoebe Liu / The Information : Sources: Nico Caprez, son-in-law to Jensen Huang, rapidly ascended to become an Nvidia VP, serving as its main contact for neoclouds and Huang's “consigliere” —  A little over a week ago, Madison Huang, a rising star at Nvidia and the daughter of its CEO, celebrated her wedding in Hawaii.

Sean Michael Kerner / VentureBeat : Cohere launches North 2, an update to its enterprise agent platform with cross-session memory and a redesigned harness, available across cloud and on-premises —  Cohere is going after two common enterprise agent problems with North 2, the new version of its enterprise agent platform.

生物学家在巴西识别出三种叫声最响亮的鸟。至于这些鸟如何演化出最响亮的叫声则仍然是个迷。白钟雀（white bellbird）叫声能达到 125 分贝，裸喉钟雀（bare-throated bellbird）和红腿叫鹤（red-legged seriema）叫声都超过 120 分贝。三种鸟类有着共同的生理特征：即宽大的喙口和肌肉厚实的大块头身体。研究人员表示，120 分贝可能是鸟类发声的生理极限；巴西之外的地方无疑也存在叫声洪亮的鸟类，但其音量不太可能比这些鸟高出太多。根据精确的测量标准，这三种鸟都可以被视为叫声最响亮者。但对科学家而言，谁是赢家并不是重点。

因涌入大量无效的 AI bug 报告，Google 宣布冻结其 Bug 悬赏计划 Open Source Software Vulnerability Reward Program (OSS VRP)，该决定于 10 月 1 日生效。Google 承诺将于 2027 年第一季度提供相关更新，期间将对该项目进行调整。Google 鼓励参与者探索其它 bug 奖励计划。大模型以及 Bug 搜寻自动化脚本的流行，导致了 OSS VRP 项目涌入了大量低质量的报告，Google 和开源项目维护者因此不堪重负，很多报告声称发现了 bug，但实际上无效，而维护者们浪费了大量时间去验证这些报告，无法专注于修复真正重要的 bug。整个行业都存在相同的问题。

2026 年诺贝尔生理学或医学奖授予了美国斯坦福-霍华德·休斯医学研究所的 Karl Deisseroth、德国柏林洪堡大学的 Peter Hegemann 和维尔茨堡大学的 Georg Nagel，以表彰他们在光门控离子通道和光遗传学方面的发现。大脑如何掌管情感、行为和身体功能，长期以来一直是个谜。20世纪，研究人员开始探究大脑的哪些区域影响哪些功能，但所用的方法意味着他们无法证明因果关系。光遗传学——一种能揭示神经细胞如何在活体大脑中塑造记忆、情感和行为的方法——改变了这一状况。Peter Hegemann 和 Georg Nagel 发现了通道视紫红质——一种具有独特性质的藻类蛋白质，存在于细胞表面。当它被蓝光照射时，蛋白质上会打开一个通道。带电离子随即流入细胞，产生电脉冲。他们发现，无论将这种蛋白质放入哪种细胞，那些细胞都会变得对光敏感。Karl Deisseroth 将通道视紫红质的基因导入大鼠的神经细胞中。通过用蓝光照射这些细胞，他能够触发神经信号。

一位玩家声称在购买了一块二手 CPU Ryzen 7 5800X3D 之后，因前拥有者有作弊行为这块 CPU 被列入了封禁黑名单，导致 Riot Games 旗下的所有游戏都无法启动。Riot Games 工作室负责反作弊的高管 Phillip Koskinas 通过社交媒体否认了这一说法， 他称没有找到相关记录，该公司的硬件封禁最长持续四个月，且只针对特定游戏，不会波及该公司的其它游戏，不会因为作弊者使用了一个硬件组件就将其它硬件组件加入到封禁名单。

太阳已有 46 亿年历史，大约 50 亿年后，随着氢燃料的耗尽，太阳外层将会膨胀转变为红巨星，它会吞噬水星和金星，甚至可能包括地球。太阳系外围的气体巨行星预计会幸免，随着太阳光芒的熄灭，太阳系的残余行星预计还能存在一千亿年。然而发表在《The Astrophysical Journal Letters》期刊上的一项研究对此提出了质疑，认为在太阳生命的末期，整个系统会进入极端不稳定状态，会陷入致命的混乱，在太阳转变成白矮星之后，残余行星可能无法坚持超过 10 亿年。也就是说整个太阳系行星系统的剩余生命可能只有 60 亿年。

巴西国立坎皮纳斯州立大学（UNICAMP）的研究人员发现，当你给聊天机器人输入不同政治观点时，机器人会改变其回答。研究人员警告称，用户可能会将这种迎合性的附和，误认为是中立的客观评估，从而可能加剧社会极化。研究人员测试了若干模型，让它们针对112项陈述进行“同意"或者“不同意”的判断。测试内容共涉及巴西政治七大领域（包括经济、公共安全、社会福利、腐败和环境等）。测试设计的三个场景是：不提供用户政治倾向；用户持左翼观点；用户持右翼观点。在第一个情景下，21个模型中，有20个给出的答案落在了研究人员设定的政治坐标系的左侧，只有几个模型的位置接近中间地带。Grok 4.1是唯一落在右侧的模型。尽管初始条件各不相同，但所有模型在面对提示信息时，都会将回答向提示中描述的政治倾向靠拢。研究人员将这些模型形容为“意识形态变色龙”，并发明了一个“变色龙指数”，衡量各模型的偏移程度。Meta的Llama 3.1 8B和DeepSeek V3.2的回答变化幅度最小；Google的Gemma 3 27B和OpenAI的GPT-5 Nano则表现出最大的偏移幅度。

新冠康复者或疫苗接种者能对类似冠状病毒产生免疫力。研究人员分析了 15 种有代表性的蝙蝠冠状病毒刺突蛋白与 34 个物种的 ACE2 受体库结合的可能性。新冠病毒的刺突蛋白就是通过与 ACE2 受体进入人体。研究结果显示，能与多种 ACE2 受体结合实现跨物种传播的蝙蝠冠状病毒都是与新冠病毒 SARS-CoV-2 有密切亲缘关系的，这些病毒的抗原也与 SARS-CoV-2 的抗原极为相似，因此感染新冠后康复或接种过疫苗的人能对这些潜在跨物种传播的蝙蝠冠状病毒产生免疫力。

Google 一颗冰箱大小的卫星搭载 SpaceX 的 Falcon 9 于 10 月 1 日升空，搜索巨人将测试太空 AI 数据中心的可行性。有很多专家已经指出太空数据中心可行性不高。Google 的太空 AI 数据中心项目称之为 Project Suncatcher，最新发射的卫星旨在验证芯片能否在太空环境中正常运行，它安装了 4 个 Tensor Processing Units 芯片，将运行该公司的开放权重模型 Gemma AI 的一个版本去回答简单查询，因热管理约束每次只运行 15 分钟。