OrangeBot.AI Digest — 2026-09-17

Multiplayer space for your AI coding agents and teams

Turn your phone into a hands-free receipt scanning station

Make your website work better for people and AI

40MB Web Browser in CLI

Google reviews and testimonials, together on your site

Check to see if AI really completed a task or if it had gaps

From Chinese AI evidence to implementation with Astra

The endless lo-fi drive where every repo is a building

Add fast, accurate dictation with a single line API call

Build complex, reliable AI agent workflows & routines

Linux creative suite for design, layout, paint & motion

Track what AI says about your store, fix it, generate blog

Voice writing that works before, during, and after meetings

See what your Claude Code sessions actually cost

Play with friends and AI and let the crowd change the game

As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedback. Token efficiency therefore becomes important for scaling recursive self-improvement. We take an RSI-inspired approach at the harness layer, scaling auto-research loops across increasingly numerous and diverse environments for harness rollouts. At this scale, the process yields reusable improvements that transfer beyond their development setting, moving automated harness discovery toward production-level outcomes. Four mechanisms survive selection and form SoL-Pi, spanning action execution, context compaction, observation handling, and delegated reading. On the 51-task EdgeBench evaluation, SoL-Pi achieves performance comparable to Pi across GPT-5.6 Sol and Opus 5 while reducing recorded token traffic by 44.7-49.0% and API cost by about one third. In other words, estimated hourly savings are \8.75-13.50 relative to native Codex and Claude Code harnesses, and \4.36-5.71 relative to Pi.

Coding harnesses shape how autonomous coding agents translate model capabilities into long-horizon software-engineering performance, yet existing work typically evaluates harnesses as monolithic systems, leaving the effectiveness of individual components unclear. To enable component-level comparisons, we study this question with a lightweight coding harness whose execution loop is fixed while three components are varied: planning, action space, and context management. Across four models evaluated on SWE-Bench Verified and Terminal-Bench 2.1, we evaluate 176 matched settings spanning five context-management strategies, four context-window budgets, and targeted ablations of planning and action space. We find that: (1) Context management becomes increasingly valuable as the context-window budget tightens, with most of its benefit coming from preventing context-overflow failures. (2) Staging rule-based elision before LLM-based summarization provides the strongest overall efficiency among the context-management strategies, whereas making elided content recoverable adds machinery that models rarely use and yields no accuracy gain. (3) Planning shifts from an accuracy scaffold for weaker models to a cost saver for stronger models, with little change in accuracy. (4) Predefined tools improve performance for models with weaker bash proficiency, whereas bash-capable models can operate effectively with a bash-only interface and achieve substantially lower cost, especially on command-line-centric tasks. Trajectory-level analysis explains these effects: context management extends execution trajectories without substantially altering agent behavior, planning changes where trajectories stop, and the action space changes the granularity at which code is written. These findings inform model- and budget-aware harness design and provide a modular framework for evaluating future harness components.

The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transfer bandwidth. Together, these compute, storage, and bandwidth demands constitute the primary bottleneck to further lowering deployment costs. To address this challenge, we introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens. With its Causal Encoder-Decoder (CED) architecture, the model activates 16B parameters per token during decode but only 8B parameters during prefill, substantially improving cost efficiency for agentic workloads. To push the limits of KV cache compression, DeepSeek-V4.1-Flash combines cross-layer KV cache reuse in Compressed Sparse Attention 2 (CSA2) with FP4 KV caching. These designs reduce its global KV cache footprint (always in HBM) to 890 bytes per token, roughly 1/4 of the corresponding footprint of DeepSeek-V4-Flash. Further, through a dedicated deployment optimization known as SWA Bounded Replay, DeepSeek-V4.1-Flash reduces its persistent KV cache footprint (always on SSD or in host memory) to roughly 1/8 of that of DeepSeek-V4-Flash. Despite its much smaller KV cache footprint, the model delivers substantially better performance than the baseline. In addition, we streamline the DeepSeek-V4 architecture and introduce several efficient architectural extensions. We pretrain DeepSeek-V4.1-Flash on a multimodal corpus comprising 45T tokens and conduct comprehensive post-training, yielding strong performance across diverse text-based and multimodal agentic scenarios. Model checkpoints are available at https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash.

World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization (OPF). Extending joint-embedding predictive architectures, OPF decomposes latent targets into complementary factors, learns them through dedicated pathways, and recombines them within a shared predictive design. We evaluate JEPA-Anything across seven domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather. Experiments span representation learning, intervention prediction, out-of-distribution generalization, and long-horizon dynamics, including 10 matched dynamics tasks, forecasting of over 1,000 clinical events, and 100-step molecular rollouts across four systems. Against matched JEPA baselines, JEPA-Anything improves reported metrics on all 10 dynamics tasks and reduces single-intervention prediction error on Interventional Pong by 34.8%. It achieves the lowest one-step and 100-step molecular errors among compared methods in all four systems. Beyond prediction, a factor-nominated biological intervention receives experimental support in cell co-cultures, patient-derived organoids, tumor fragments, and mice; latent orbital modes recover the Keplerian scaling exponent with a fitted slope of -1.4991. These results support a common factorized predictive principle across heterogeneous worlds, connecting world modeling with intervention and experimentally grounded scientific discovery. Code: https://github.com/Gen-Verse/JEPA-Anything

Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with pornography, fraud, gambling, or illicit transactions. Existing benchmarks evaluate obfuscated text and risky webpages separately, obscuring how target recovery affects downstream evidence acquisition. We introduce RiskChainBench, pairing 3,600 synthetic token-text restoration inputs from 600 source sessions with 600 corresponding human-labeled local web environments. A model first restores the message, operational intent, and destination; the same underlying model then acts as a VLM-driven web agent that investigates the correctly associated website and produces a frozen, evidence-cited risk report without message-side semantics or domain-reputation cues. We score restoration and correct-routing web investigation separately and compose them offline by applying the frozen primary-entry prediction as a gate to the same Task 2 result. Human labels determine task correctness, while a fixed multimodal evidence judge assesses faithfulness, sufficiency, completeness, and consistency. Across ten models, Entry Top-1 ranges from 35.2% to 95.2% and web decision accuracy from 26.3% to 62.8%; the leading systems differ across entry recovery, full reconstruction, website decisions, and fine-grained typing. Execution failures account for 31.9% of web runs, whereas post-decision type errors account for only 0.9%, identifying stable exploration and risk judgment as the principal bottlenecks. We release the benchmark, protocol, and resettable local sandbox.

Document parsing converts document images into structured content and requires reliable performance across diverse layouts and acquisition conditions. Yet training corpora are biased toward common document types and clean digital pages, while expanding coverage alone does not specify how to address a parser's remaining weaknesses. We present WeVisDoc, a two-stage data-centric framework for robust end-to-end document parsing. Stage I broadens semantic, structural, and appearance coverage through heterogeneous data and structure-preserving degradation synthesis. Stage II uses a held-out probe to measure the Stage I parser's residual errors within fixed visual-structural clusters. These diagnostics guide targeted data construction and reallocation of the target-token budget. WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings. Compared with Stage I, Stage II improves Overall scores for the 2B and 4B models on both benchmarks, with larger gains on the degraded PureDocBench tracks, including a 4.03-point gain for the 4B model on the Real Degraded track.

Multi-turn agents trained with reinforcement learning (RL) receive a single scalar reward per trajectory, which motivates self on-policy distillation (OPD) to supply dense token-level supervision from a self-teacher with privileged task skills, letting a skill-free student internalize them. This recipe, however, is undermined by two findings in agentic tasks: privileged information alone does not always make a teacher reliable, and the benefit of teacher supervision is stage-dependent. We therefore propose RetireOPD (Self-Retiring On-Policy Distillation), which first optimizes a decoupled, skill-conditioned teacher with environment rewards and then trains a skill-free student jointly with RL and OPD. Rather than following a predefined distillation schedule, RetireOPD adopts Adaptive Retirement: the student drops the teacher on its own once their discrepancy stops shrinking and it reaches a target fraction of the teacher's success rate, after which training proceeds with RL alone. Across Qwen2.5 models from 1.5B to 7B, RetireOPD improves ALFWorld success rate over RL baseline by 14.1% to 18.8% and WebShop accuracy by 11.8% to 19.0%, and surpasses its own skill-conditioned teacher in every setting.

GUI agents execute long-horizon tasks on dynamic graphical user interfaces, where pop-ups, delayed loads, and relocated widgets routinely invalidate plans fixed before execution. Recent agent-skill frameworks encapsulate reusable procedural knowledge to mitigate this, yet existing skill designs are largely developed without targeting GUI execution dynamics and treat skills as static artifacts produced before deployment rather than living procedural knowledge that improves through it. We argue that what GUI agents need is not better static skills, but skills that can be revised from execution feedback at deployment time, without additional training. We propose EvoSkill-GUI, a training-free framework in which each skill is a structured multi-file package containing retrieval metadata, executable plans, backup localization, failure-recovery rules, accessibility utilities, and failure cases. EvoSkill-GUI operates through a \emph{reflect-revise-reuse} loop: the executor performs instant in-rollout revisions, an isolated critic diagnoses failed trajectories under strict information isolation, and the executor edits specific skill files through a restricted tool interface. Across MobileWorld, AndroidWorld, and OSWorld, three mainstream GUI benchmarks spanning mobile and desktop platforms, EvoSkill-GUI consistently improves multiple base models without any training, with maximum gains of +16.2%, +6.0%, and +10.5% respectively, and evolved skill libraries continue to benefit related tasks rather than being rebuilt from scratch. Our code is available at https://github.com/ZJU-REAL/EvoSkill-GUI.

On-policy self-distillation (OPSD) lets a language model learn from a frozen copy of itself that sees an answer or a worked solution. Giving the teacher this extra information seems to offer the student more to learn, but how much does it add beyond distillation itself? To isolate that contribution, we construct AMPLE-Math, a reusable suite of 5,319 mathematical problems with six reasoning views that share the same answer, and compare each view with matched reference-free distillation. With a thinking-enabled teacher supervising direct-response rollouts, reference-free distillation accounts for much of Qwen3-1.7B's improvement under thinking-enabled evaluation, both in domain and on external benchmarks. Evidence for an additional reference benefit is modest in Qwen, strongest for a polished solution, whereas complete traces add two percentage points in SmolLM3-3B at step 50. These benefits depend on the student being trained. At the same checkpoint, replacing short direct-response rollouts with long thinking-enabled rollouts turns gains into losses in both families while the problems, references, and evaluation stay fixed. Teacher profiles and matched loss interventions in Qwen further show that changing token-level supervision can leave student behavior largely unchanged. Together, these findings suggest that OPSD can improve access to existing reasoning capabilities through parameters shared by direct-response and thinking-enabled inference. The value of a privileged reference is what it adds to this cross-mode transfer, not how much of the solution it reveals.

Spatial intelligence requires more than describing object locations. Under incomplete observation, models must identify and acquire missing evidence, interpret it in a common spatial frame, and act on it. We introduce VA-Bench to evaluate the complete observe-reason-act-revise loop. General-purpose MLLMs learn procedural context from RGB-only demonstrations, actively select camera viewpoints, issue metric Cartesian commands, and revise them from execution feedback. Models receive no privileged object poses, oracle trajectories, or learned action heads. A fixed model-agnostic controller executes only model-specified targets. VA-Bench contains 14 base task families (11 single-arm and three dual-arm), seven held-out geometry/layout variants, and a long-horizon five-object composition track. We evaluate 12 primary model conditions in three independent runs over the same 20 physically verified seeds per base task, reporting terminal success, nine trajectory-level behavioral diagnostics, and subtask progress. First, the best-performing model scores 100.0% on target localization and 78.9% on spatial relations in the annotated run. Its three-run macro-average task success is only 53.93+/-3.17%. Second, active camera control significantly improves task success over passive multi-view observation. In one matched comparison, success rises from 27.86% to 57.50%. Third, held-out geometric transfer can reduce task success by over 30 percentage points. No model completes a strict long-horizon episode, despite substantial partial progress. VA-Bench thus tests whether general-purpose MLLMs can turn visual demonstrations and actively acquired evidence into successful embodied action.

Multi-modal image generation, particularly subject-driven customization, has garnered growing attention in recent years. Despite the rapid advancement of generative models, their evaluation remains largely lagging. Existing methods, whether embedding-based or Multi-modal Large Language Model (MLLM)-based, evaluate alignment with each modal condition in isolation, which contradicts the simultaneous condition alignment objective of multi-modal image generation, leading to poor consistency with human judgments. To address this challenge, we propose UFO, the first unified framework for omni-condition alignment simultaneous evaluation. Specifically, UFO introduces a novel Atomized Chain-of-Evaluation paradigm, i.e., it first decomposes omni-condition alignment into a sequential chain of fine-grained, disentangled Atomic Evaluation Units (AEUs), categorizes them into distinct modality-relevance classes, and then employs general or dedicated functional calls for accurate verification of different AEU types. Experimental results demonstrate that UFO achieves the highest correlation with human evaluation preferences, delivering an average improvement of 15.25%. Furthermore, we present UFO-Bench, a dedicated benchmark designed to holistically evaluate the performance of existing customization models under the diverse mutual interactions of textual and visual conditions.

Video diffusion models repeatedly process long spatiotemporal token sequences during denoising, making attention a major computational bottleneck. Linear attention offers an appealing alternative and has been widely adopted in recent large language models, but directly applying it to video models often fails to preserve the fine-grained interactions required for high-quality generation. We present Video DeltaNet (VDN), which combines local Softmax attention with bidirectional linear memory for long-range video context. Its linear branch introduces Video Delta Attention (VDA), which updates memory once per frame by jointly incorporating its spatial tokens. Separate output projections and learnable gates calibrate the two branches, while a staged teacher-alignment recipe progressively introduces the new pathway into pretrained models. We instantiate VDN on MiniMax H3, applying the hybrid to video-to-video interactions while retaining Softmax for interactions involving text or audio. With eight-step distillation and an optimized SGLang serving stack, VDN-H3 completes DiT denoising for a 14.3-second, 768p video in 6.70 seconds on eight NVIDIA B200 GPUs, corresponding to a 14.5x speedup over the 50-step dense H3 baseline on the same GPU count.

We study length inflation in on-policy distillation (OPD), where student responses can become excessively long and even exhaust the generation budget. We identify termination-token mismatch between base students and post-trained teachers as an important source of this behavior. Across Qwen3, Llama, and Gemma, the two models can place their stopping probability on different EOS tokens, even when their declared stopping sets are identical. This mismatch can suppress the student's preferred termination action without reliably transferring the teacher-preferred alternative. We show that aligning the decoding stopping set alone is insufficient, while treating functionally equivalent EOS tokens as a shared semantic stopping action substantially mitigates mismatch-induced length inflation across all three model families. To further understand how termination behavior evolves over training, we study OPD across different K2-Horizon training stages. This stage-wise analysis shows that termination preferences can shift substantially during training, while also revealing a distinct length inflation late in the OPD run that persists beyond termination alignment. Together, these results identify termination mismatch as an important, but not exhaustive, source of OPD length dynamics. We release an implementation incorporating the proposed termination-handling corrections.

As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and finance. In these contexts, compliance with rules specified in an agent's system context is a first-order legal concern. Currently, no evaluation framework systematically measures which LLM models tend to violate compliance rules, especially under pressure from a persistent user, a hurried manager, or circumstances where violation is convenient or attractive. We introduce PACT (Pressure-Applied Compliance Testing), a benchmark for rule-following under pressure in AI agents assisting employees in daily tasks across twelve regulated enterprise domains and forty-eight scenarios, each set in a realistic multi-turn conversation. Each benchmark item pairs a standing rule against a rule-violating shortcut, and applies a battery of pressures across different wordings and system-prompt modes. We construct PACT component by component under strict LLM-as-judge auditing to ensure samples are unambiguous, ungameable, and realistic enough to avoid eliciting evaluation-aware behavior. We use PACT to profile LLM compliance across six complementary metrics that create a holistic picture of an AI assistant's robustness under pressure and throughout multi-turn conversations, its transparency, and ability to correctly discern where a rule applies. We aggregate this profile into PACTScore, a reliability-weighted compliance rate over all items and modes. Our results across 22 common LLM models spanning multiple providers and sizes show substantial variability in compliance across models and metric dimensions. Even the strongest assistants mis-apply a rule on 6 to 10% of items, and ordinary user pressure raises the violation rate by 65% on average. PACT highlights compliance risks in LLM assistants, motivating guardrails and careful model selection.

Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and underthink hard ones. Existing approaches based on uniform length penalties or rigid routing incur an efficiency tax, trading reduced computation on easy instances for accuracy loss on hard instances. We formulate efficient reasoning as an instance-adaptive computation allocation problem and propose When2Think, a post-training framework for hybrid reasoning that dynamically allocates computation based on problem difficulty. Our method introduces Instance-level Difficulty-Aware Control (IDAC), a reward-shaping mechanism that leverages pre-computed reference statistics (accuracy and token usage) to regulate reasoning depth. Combined with verifier-based rewards and batch-wise standardized advantages, IDAC enables stable critic-free optimization without learned reward models or online reference-model queries. When2Think encourages direct answering on easy instances while preserving extended reasoning on hard instances, thereby learning when to use System 1 (NoThink) versus System 2 (Think). Experiments on mathematical benchmarks demonstrate improved accuracy-efficiency trade-offs: on AIME24, Pass@3 increases by 10.0% while token usage is reduced by 27.9% relative to the base model, and on AIME25, When2Think achieves 40.0% Pass@3, outperforming compression and routing-only baselines.

Lydia Beyoud / Bloomberg : The US CFTC exempts “passive software” providers from broker registration if they never hold user assets, expanding crypto and prediction market online trading —  The Commodity Futures Trading Commission moved to expand crypto and prediction market trading via online platforms …

Jason Shubnell / The Block : World rolls out World Money, a self-custodial financial “super app” in 150+ countries that combines stablecoin payments, trading, earning, and virtual accounts —  - World is rolling out World Money, a financial “super app” spanning stablecoins, investing, yield, and global payments in more than 150 countries.

Reuters : Sources: Apple plans to launch Apple Pay in India next month with Axis Bank's credit cards; Axis Bank is the fourth-largest credit card issuer in India —  Apple (AAPL.O) is poised to launch Apple Pay in India next month with Axis Bank's (AXBK.NS) credit cards, three sources familiar with the matter said …

Jay Peters / The Verge : Meta launches a Mac app for Muse after releasing the AI agent on iOS, Android, and the web earlier this month, allowing it to manage files, pull from apps, more —  Follow topics and authors from this story to see more like this in your personalized homepage feed and to receive email updates.

Robert McMillan / Wall Street Journal : Security researchers in an OpenAI bug bounty program hacked OpenAI, accessing its “monorepo” on GitHub, using a cybersecurity version of Opus 4.8 and Opus 5 —  A bug-hunting independent security research team was able to access OpenAI's internal code system, exposing growing risks in automated cyber threats

Julie Bort / TechCrunch : PrismML releases Bonsai 2 27B, which compresses Alibaba's Qwen3.8 27B to 5.9 GB, small enough for smartphones, while retaining 98.2% of Qwen's benchmark scores —  If AI lab PrismML isn't on your radar yet, it should be — not because it's raised gobs of money (it hasn't yet …

Hassan Ali Kanu / Politico : A federal judge denies OpenAI's bid to see SpaceXAI's confidential settlement with Apple in an antitrust case, finding the materials irrelevant after a review —  A federal judge has denied OpenAI's request to force the disclosure of a settlement agreement between Apple and SpaceXAI …

Dwarkesh Patel / Dwarkesh Podcast : An interview with OpenAI researcher Noam Brown about multi-agent systems, AI solving the Navier-Stokes problem, the internal/external model gap, and more —  “We never want to be in a situation again where we underestimate the AI.”  —  New episode with Noam Brown.

Jagmeet Singh / TechCrunch : The UN says it is working with Google on the UN System Data Commons, which lets users search for statistics from across UN agencies via natural-language queries —  The United Nations on Thursday announced that it is working with Google to make its vast collection of global statistics easier for AI systems to access and use.

Shirin Ghaffary / Bloomberg : Anthropic says Claude “leads” 26% of its AI R&D work, up from 1% in March, and “collaborates” on 90%+, doing large chunks of work under close human direction —  More than a quarter of Anthropic PBC's research and development work for artificial intelligence …

Claude : Anthropic redesigns Claude projects, letting users describe work in one conversation and have Claude manage it across parallel threads, starting in Claude Code —  A new experience for Claude projects, now available in beta in Claude Code  —  https://claude.com/blog/projects- redesigned

OpenAI : OpenAI launches Astra for Law, combining GPT-6 Astra with a legal search index and instructions for legal analysis and writing, initially for select law firms —  Our most powerful model, configured into a new AI foundation for law.  —  Explore solutions for law Contact Legal sales

Anthropic : Anthropic outlines metrics to track AI development at frontier labs: how much AI R&D is done by AI, how well agents are overseen, and how compute is allocated —  AI systems are becoming exponentially more powerful and have begun to automate more of the process of building themselves.

Micah Barkley / Bloomberg : Andrew Ng says renewed AI extinction warnings are “much more science fiction than science” and the latest “wave of PR” is probably intended to shape regulation —  Artificial intelligence pioneer Andrew Ng said warnings about existential AI risks from researchers …

Financial Times : NYT court filing: ChatGPT's head wrote that publishers face an “existential threat” from AI, and a Microsoft director called AI training “an astonishing theft” —  Lawyers say AI co-founder Greg Brockman was ‘motivated by the gazillions’ he hoped to gain from models trained on copyrighted content

欧洲的一项研究发现，微塑料会像特洛伊木马一样在土壤中传播污染物、农药和细菌。研究人员在 11 个国家测试了 227 块农田，都发现了微塑料。研究人员还发现，微塑料可能会与农药兽药发生相互作用。瑞士的一项研究发现，轮胎磨损颗粒含量最高的农田，其有毒化学物质和金属的含量也最高。微塑料的表面能为微生物创造新的栖息地，该表面被称为塑料圈（plastisphere）。相比对照组，塑料圈内的抗生素抗性基因有所增加，农药则进一步放大了这一效应。研究人员表示，微塑料颗粒越小，越容易吸附污染物、微生物和 DNA，从而产生一种“特洛伊木马效应”，可能导致病原体和抗生素抗性基因的扩散加剧。可生物降解塑料不一定更安全，它们仍可能降解为微塑料。

德国一家法院周四裁定，Meta 必须对其 Instagram 和 Facebook 平台上第三方发布的虚假广告承担责任，责令 Meta 删除此类内容和支付赔偿。诉讼由一家德国金融门户网站的运营商及其创始人提起，虚假广告在推荐可疑投资项目时未经许可使用了它的 logo 和图像。该运营商在 2024 年 8 月向 Meta 举报了近 260 起违规，而 Meta 花了 62 天时间才删除部分内容。Meta 发言人表示对裁决持异议，正在考虑后续行动。法院表示，该裁决还不具有最终的法律效力，Meta 可通过提起上诉提出异议。

刚果民主共和国的埃博拉疫情死亡人数超过 3500 人，确诊病例超过 7400 例，逾 1700 人康复。WHO 称，疫情最严重的 Ituri 省的病毒传播速度在下降，上周报告了约 300 例新增病例和 160 例死亡病例，占全国的一半；South Kivu 省自 5 月以来未报告新增病例；North Kivu 省的传播速度在加快，过去两周的新增病例从 100 多增加到 200 多；邻国乌干达境内的传播已经遏制。WHO 宣布，埃博拉疫苗试验将在未来几周内启动，如果疫苗证明有效将会扩大其使用范围。

NASA 于 8 月 30 日使用 SpaceX 重型火箭 Falcon Heavy 成功发射了 Nancy Grace Roman 太空望远镜，目前正飞往日地之间的 L2 拉格朗日点，它需要三个月时间才能抵达目的地。Roman 望远镜耗资 43 亿美元，原计划执行 5+5 为期十年的任务，NASA 表示受益于精确的发射、巧妙规划的飞行轨道，地面操作团队的出色执行，望远镜有足够的燃料能执行 22 年的任务。NASA 称，Roman 望远镜控制推进器在发射后的第一次长点火燃烧仅用了 18 公斤的联氨燃料，远低于预留的 200 公斤。望远镜的实际重量为 8,056 公斤，比最大允许重量轻了两吨，因此地面人员给其燃料箱多加了 290 加仑的联氨燃料。加满燃料的望远镜在发射时的总重为 9,173.5 公斤，仍然比最大允许重量少了半吨，更低的质量意味着望远镜在执行航向修正机动时推进器的点火时间无需过长。

陈奕龙有 20 年出演影视剧的经验，由于工作机会日益稀少，他在 8 月与一家 AI 工作室签约，授权对方使用其面部肖像。陈奕龙的工作就是坐在摄像机前，根据导演的指令做出不同面部表情——如无表情凝视、怒视或惊讶。这家工作室将利用 AI 技术，以陈奕龙的面部图像为基础生成虚拟数字人，让其主演一部由 AI 生成的电影。现年 38 岁陈奕龙说道，“如果你无法战胜它，那就加入它。”雇佣陈奕龙的是总部位于深圳的 Feixiang Universe 工作室，创始人 Sun Wei 表示，之所以为一部唐朝背景的 AI 电影项目获取真人肖像授权主要是担心无意中窃取他人肖像。她指出，受 AI 训练数据偏差的影响，AI 生成的演员通常面部特征相似，长相千篇一律。这促使她和其他中国 AI 电影制作人去搜寻和购买新面孔，将演员的真实表情数字化。根据广电总局的数据，今年首八个月，中国上线微短剧共 43 万部，是去年全年的 13 倍，其中 AI 剧占比超过九成。Sun Wei 的团队则计划以 50 万美元的预算制作一部 90 分钟的 AI 长片。

地球并非是完美的球形，自转以及围绕太阳公转使其赤道部分隆起，两极部分扁平。根据一项新研究，可能是由于极地地区的冰雪融化，地球两极在隆起，与此同时赤道地区则在加速下沉，地球的整体形状变得不那么扁平。研究人员利用了 GNSS 导航网络地面站在 1997-2015 年间的测量数据，绘制了地球固体部分的升降变化。结果显示，1997-2000 年间地球两极以每年约 0.5 毫米的速度隆起。到 2015 年，隆起速度已增至每年 1 毫米。

GNOME 桌面环境项目释出了代号为 A Coruña 的 GNOME 51，该代号旨在感谢 GUADEC 2026 的主办城市。GNOME 51 主要变化包括：Mutter 重新设计了调度和屏幕帧交付系统，即使系统处于高负荷下，动画仍然流畅；全面提升了屏幕录制性能；记住显示器的亮度设置；移除了基于 EGLStreams 的旧 NVIDIA 驱动接口支持；支持新 Wayland 协议如 ext-background-effect-v1；支持自定义指针加速配置文件；GNOME Maps 支持离线地图；GNOME Software 启动更快，GNOME File Previewer UI 重构，等等。

发表在《自然》期刊上的一项研究表明，移植到小鼠体内的人脑类器官能够重现大脑发育的某些特征，包括形成功能性神经网络，从而支持脑活动。这一成果得益于一种培育脑容量缩小的小鼠的方法。这种方法使得这些由人类细胞培养形成的三维类脑组织，即人脑类器官能够与小鼠大脑整合。该模型有望以比此前更深入的方式研究人脑组织的发育。研究结果显示，移植的类器官生长良好。它们占据了大部分空间，分化为多种人类脑细胞，组织形成了神经回路，并产生了类似大脑皮层组织的复杂结构。此外，整合形成的皮层组织可能造成了某些行为表现。研究人员将移植类器官和未移植类器官的皮层缺失小鼠进行了比较，发现它们在运动和记忆任务中的表现存在差异。

被现代商船逐渐淘汰的风帆正以新的形式再次回归。散货船、油轮到集装箱船等各类商船正在安装高耸的转子帆、刚性翼帆以及吸风式推进系统。此举并非是要将现代船舶重新变成帆船，而是让基于风帆的系统与现代推进系统协同合作，在条件允许时利用风力降低油耗。International Windship Association 协会的数据显示，已有逾百艘大型商船配备了现代风力推进系统，总载重吨位逾 500 万吨。Maersk 公司计划在一艘有 8700 个标准箱的集装箱货船上安装一座 35 米高的转子帆，预计于 2027 年在大西洋常规航线上启动测试。它有望成为第一艘安装转子帆的集装箱货船。Vale 旗下一艘载重吨位达 40 万吨的巨型矿砂船 Sohar Max 已经安装了五座 35 米高的转子帆，风帆系统将油耗降低多达 6%。韩国船舶制造公司最近宣布研究在液化天然气货船（LNG）上安装风帆系统。如果研究取得成功，那么将进一步证明风帆技术重新迈向主流。

广州市人大常委会公布了《广州市噪声污染防治规定（草案修改稿·征求意见稿）》，向社会各界征求意见和建议，截止日期为 10 月 15 日。 《征求意见稿》明确，在巴士、地铁等公共交通运输工具内使用手机、平板电脑等电子设备时不得外放声音。使用电子设备外放声音的，公共交通运输工具司乘人员应当劝阻，劝阻无效的，可以向公安机关报告。 违反规定，在公共交通运输工具内使用电子设备外放声音，经公共交通运输工具司乘人员劝阻仍不改正的，由公安机关说服教育，给予警告，可以处 200 元以上 1000 元以下的罚款。

根据发表在《Scientific Data》期刊上的一项研究，地球暖化正加速两大冰盖的融化。卫星数据显示，自 1979 年以来格陵兰岛和南极洲损失了 12.5 万亿吨冰量。这些冰融化为 11.3 千万亿升的水，导致 1979 年以来全球海平面上升了 3.1 厘米。而海平面每上升 1 厘米，就会导致额外 200-300 万人每年面临至少一次洪水威胁。研究发现，六分之五的冰体流失并非源于气温升高，而是因为较温海水从底部和侧面侵蚀冰盖，导致冰川滑向海洋。格陵兰岛的 Jakobshavn 冰川目前每天退缩的距离达到 50 米。数据显示，1970 年代、1980 年代和 1990 年代的冰盖状况相对稳定；但随后融化现象开始出现，2010 年代显著加剧。

全世界有无数人的睡眠不足，睡眠不足与肥胖、糖尿病、高血压、心脏病、中风及过早死亡相关。如果有人付费让你睡更长时间？科学家为此做了一项社会实验。研究人员向匹兹堡大学的 1100 多名本科生提供了 Fitbit 以及一款能发送就寝提醒和晨间反馈的应用。在为期四周内研究人员随机选择了 468 名学生，只要他们某晚睡眠时间达到至少七小时，就向其支付 5 美元报酬。研究人员通过他们佩戴的设备核实实际睡眠时长。参与研究的学生平均年龄约为 19 岁，其中半数为大一新生。72% 为女性。55% 为白人，28% 为亚裔，9% 为黑人，4% 为西班牙裔。研究结果表明，提供即时经济奖励有助于学生实现每晚七小时的睡眠目标，且这种效果在停止发放奖励后仍能持续一个月。参与研究的学生此前平均每晚睡眠时间为 6.6 小时。半数学生在凌晨 1 点之后才睡觉，四分之一学生甚至在凌晨 2 点之后才入睡。在实验中，获得现金激励的学生在每个上课日夜晚平均多睡了 19 分钟，该学期的 GPA 得分上升了约 0.08 分——相当于成绩高于平均水平和低于平均水平的学生之间差距的四分之一。

1999 年初代 PS2 Fat 游戏机使用的安全芯片 CXP102064 MechaCon 在时隔 26 年被爱好者破解。加拿大复古软硬件爱好者 DiscoStarslayer 通过社交媒体称其花了四年时间破解了其秘密。DiscoStarslayer 采用的逆向工程方法包括：利用化学方法对 CXP102064 芯片进行开盖，利用显微镜和光学数据提取技术分析芯片电路。期间发现了一个漏洞利用方法，可通过软件提取芯片数据。MechaCon 芯片也被用于当时推出的几款街机，包括 Namco System 246 和 System 256 以及 Konami Python 1 等。

Denuvo 在美国加州北区联邦法院起诉了名叫 voices38 的匿名游戏破解黑客，指控其违反了 DMCA 的反规避条款。被告被控绕过了逾二十款游戏使用的 Denuvo DRM，相关游戏包括了《霍格沃茨之遗（Hogwarts Legacy）》和《黑神话：悟空》。随着诉讼的推进，Denuvo 可能会向 Reddit、Discord 和 Valve 发出传票，以获取黑客的身份信息。voices38 发布了一系列使用 Denuvo DRM 的游戏破解补丁，曾在一天之内发布了创纪录的五款 Denuvo DRM 游戏破解补丁，以至于引起了 Denuvo 公司的注意。Denuvo 称被告是一名专注于对 Denuvo DRM 游戏进行逆向工程的计算机黑客。

亚马逊云服务 AWS 称，由于其数据中心因战争受损它无法恢复中东部分可用区资源和数据的访问。AWS 通过其 AWS Health Dashboard 页面发表声明称，全面评估后它确认无法恢复巴林可用区 me-south-1 的资源和数据的访问。如果客户的数据只存放在该可用区，那么数据可能永远丢失了。亚马逊此前已建议其客户将其工作负荷迁移到其它可用区，它表示在该可用区完全无法使用前大部分客户已完成了迁移。位于阿联酋的可用区 mec1-az2 情况类似，阿联酋有三个可用区，另外两个 mec1-az1 和 mec1-az3 也受到战争影响，AWS 目前还在继续恢复这两个可用区的资源的访问。