OrangeBot.AI Digest — 2026-10-03

Autofill OTP codes from your email on Mac

A familiar iPhone keyboard that moves to your thumb

A smaller, faster Kindle with a flush-front display

Talk to your Mac and let Yubi do the typing

Browser multiplexer for the agentic era

Which app is draining your Mac, in watts and minutes saved

Pick a frequency, launch your radio station and go on air

Claude Code, Codex, music, your day & 20+ apps in your notch

The camera app Apple never shipped for the Mac

Your agentic fitness coach in iMessage

A desktop client for SSH, SFTP, Docker over SSH

The execution layer for AEO

The AI agent delivery platform for FDEs and domain experts

Make your AI-built sites look expensive and not like slop

Meta's open-source kit for building your own AI gadgets

Streaming video LLMs must retain evidence before its relevance to future tasks is known and respond when sufficient evidence becomes available. The challenge is to form reusable factual memory without compromising real-time perception. We introduce OneStreamer, which jointly learns query-independent evidence recording and task response through a shared proactive generation process. Its Proactive Hierarchical Caption Memory (PHCM) produces time-grounded local-detail captions and summaries of completed events. Streaming caption targets supervise the interpretation of observed video prefixes during training. At inference, model-generated records complement a recent visual window, providing reusable factual context without revisiting historical visual features. Proactive State Transition Learning (PSTL) reduces the dominance of repeated waiting states by preserving supervision at all output anchors and selecting representative state-change and state-persistence tokens. We further develop a streaming data synthesis pipeline that aligns output content and timing with available evidence. Combining the resulting streaming captions and QA with cleaned open-source data yields OneStreamer-1M, a broad-coverage streaming video interaction dataset with over one million records spanning diverse tasks. Our 4B model achieves the best results among the compared methods across all eight evaluated streaming video understanding benchmarks. Ablations show that retaining generated captions improves historical QA without degrading real-time perception. PSTL also outperforms dense state supervision while supervising only 27.5% of annotated state tokens. Together, these results support proactive generation as a shared learning interface connecting perception, memory formation, and timely response in streaming video interaction.

On-policy learning has been argued to reduce catastrophic forgetting, produce sparser parameter updates, and improve generalisation. However, existing comparisons between supervised fine-tuning and reinforcement learning vary many factors simultaneously, making the contribution of rollout policy difficult to isolate. We study the effect of rollout policy in a controlled strong-to-weak distillation setting, by independently varying rollout policy, token-level KL direction, and learning rate across the Llama3 and Qwen2.5 model families and reasoning tasks spanning scientific, medical, and arithmetic domains. Our analysis reveals a nuanced picture of distillation dynamics in which rollout policy does not necessarily play a central role. Instead, token-level KL direction more clearly shapes task performance and output coverage, while learning rate governs forgetting and update sparsity. Analysis of KL gradients and experiments along a continuous student-teacher rollout-policy spectrum explain this pattern: forward KL is remarkably robust to rollout policy, with its performance stable and strong despite changes to the rollout policy, whereas reverse KL is substantially more sensitive and favours student-generated rollouts. On-policy data nevertheless improves generalisation to harder variants of the Countdown arithmetic task under both KL directions, although this advantage does not reliably persist after subsequent RLVR. Our broader conclusions remain robust to removing gradient clipping, using sampled KL estimators, and training on tasks requiring longer reasoning chains. Overall, our results challenge the view that on-policy rollouts are inherently preferable and show that their value depends critically on the objective, evaluation setting, and optimisation hyperparameters.

Pretrained transformers use little of their depth to follow references in context. Thirteen base models reliably follow only 1.4-3.6 lines, and extra pretrained loops add little. A task-trained rank-8 LoRA at one early layer extends this computation with all model weights frozen. Qwen3-8B improves from 15.5% to 99% exact accuracy on 24-line chains; a longer-trained LoRA reaches 50 lines. Ouro-1.4B reaches 60 lines after four loops and at least 160 after eight. The LoRA starts a relay: program lines pass on their chain identity through a short range of middle layers. Frozen heads read progressively further up the chain, and removing parent-line attention stops the relay. A frozen-model measurement locates the last useful intervention layer within tolerance in three of four held-out models. Task-specific LoRAs also improve MuSiQue. Default answers therefore understate the computation accessible through a tiny edit. Code and an interactive demo are available at https://lunamos.github.io/stop-thinking-too-early/

Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically factorized over positions, limiting sample quality in the few-step regime where diffusion's speed advantage over autoregressive decoding matters most. A recent line of work introduces a continuous Gaussian latent, trained as a variational autoencoder, to capture correlations across positions, but such approaches are prone to posterior collapse, where the latent is silently ignored. We propose Enhanced Mixture-of-Experts (E-MoE), which builds the reverse process as a mixture of factorized distributions over a discrete shared latent given by the expert-routing decisions of a Mixture-of-Experts (MoE) backbone, without increasing active parameters over the factorized baseline. Across synthetic multi-modal benchmarks, binarized MNIST, and LM1B, E-MoE improves few-step generation over factorized baselines.

Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously. Reward-wise normalization, as used in GDPO, preserves reward-specific relative information within rollout groups, but different objectives can still exhibit uneven learning progress. We study this behavior through advantage energy, the sum of a reward's squared advantages over a batch. Under idealized GDPO normalization, we show that this energy is proportional to active-group density: the fraction of rollout groups in which the reward provides nonzero relative advantages. This reveals a residual batch-level signal imbalance and provides a basis for calibrating reward contributions. Based on this relation, we propose Density-Aware Reward Aggregation (DARA). We derive an inverse-square-root density correction that gives greater weight to signals from less frequently active rewards. DARA computes its weights from each rollout batch, adapting to changes in reward activity throughout training without modifying the underlying policy optimization objective. Experiments on tool calling and mathematical reasoning show that DARA learns the targeted behaviors faster than GDPO, reaching high format compliance in up to 26% fewer training steps on tool calling and near-saturated length compliance in up to 65% fewer steps on mathematical reasoning, while remaining competitive in final performance. Our code is available at https://github.com/zhaihaotian/DARA.

Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamics. Despite strong generative priors learned through large-scale pretraining, pretrained video models often fail to reliably follow human intent, maintain temporal coherence, or satisfy physical and safety constraints. Compared with image and text generation, alignment in video generation presents unique challenges, including error accumulation over time, motion-appearance coupling, multi-objective trade-offs, and limited supervision for temporal properties. These challenges motivate systematic post-training strategies that adapt pretrained models without retraining them from scratch. In this survey, we present the first comprehensive review of post-training and alignment in video generation models. We frame post-training as a unifying framework and distinguish between implicit alignment and explicit alignment based on how alignment signals are enforced. From this perspective, we organize existing approaches into four broad categories: supervised fine-tuning methods, self-training and distillation methods, preference- and reward-based methods, and inference-time methods. This taxonomy provides a coherent view of how alignment signals shape model behavior across both training and deployment. Beyond methodological advances, we review commonly used datasets, benchmarks, and evaluation practices, and discuss open challenges such as scalable reward design, long-horizon temporal consistency, stability-expressiveness trade-offs, and safety-aware generation. This survey aims to provide a structured conceptual foundation and practical guidance for advancing controllable and reliable video generation models.

Real-world embodied tasks, from everyday activities to professional procedures, require agents to act under physical constraints while tracking evolving object and task states. Tool use sits at the heart of such tasks, as many everyday and professional activities are tool-mediated. Understanding them requires reasoning about affordances, hand-tool-object geometry, procedural progress, and causal effects on target objects. Yet despite strong performance on perception-oriented video tasks such as captioning and general video QA, current multimodal video models remain limited in this form of tool-centric embodied reasoning. Progress in this direction has been limited by the lack of real-world egocentric data and diagnostic benchmarks. To address this gap, we introduce EgoTools, the first comprehensive suite for egocentric tool-use understanding. It consists of two complementary components: EgoTools-Data, a large-scale corpus of 100 hours of tool-centric egocentric recordings with synchronized audio, dense captions, reasoning-heavy narrations, and supplementary 3D information; and EgoTools-Bench, a diagnostic benchmark of 1,000 QA pairs across four tracks that cover tool-use understanding from perception and geometry to procedure and causal reasoning. Experimental results show that current models still struggle to ground tool use in visual evidence: Gemini-3.1-Pro achieves 66.9% overall accuracy but only 51.7% on Perception & Grounding. Beyond evaluation, we validate EgoTools-Data as a training resource. On the full 1,000-question benchmark, full supervised fine-tuning improves Qwen3-VL-8B-Instruct from 50.0% to 60.9%, under strict source-video separation. Together, these results establish EgoTools as a unified resource for both training and diagnostic evaluation of real-world egocentric tool-use understanding.

We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills, improving from its own attempts, and retaining what it learns, without retraining. Our key insight is that a broad controller already holds much of the competence a new task needs, and that this competence becomes accessible through an interface between planning and control that is expressive enough to specify contact-rich, multi-stage interactions, yet executable and measurable enough that execution feedback can guide planning from experience. InterEvolve realizes this interface with two components. First, we develop an object-aware forward-backward (FB) behavioral foundation model, whose object residuals on a frozen body prior turn a new reward about the body or objects into loco-manipulation behavior at test time. Second, we specify tasks as reward programs: staged rewards with completion conditions and tunable constants. A large language model (LLM) agent revises the program structure in context, drawing on execution feedback and a skill library of verified programs, while a numerical optimizer tunes its constants. With every candidate verified across parallel simulation scenarios, the program explores new ways to induce, repurpose, and compose the controller's existing motor competence for the task at hand, and thus improves over iterations. Experiments show that human-designed rewards leave much of the FB model's loco-manipulation competence untapped, whereas the programs InterEvolve evolves release it, sometimes through novel strategies. It further produces behaviors for diverse tasks, complex scenes, and long-horizon compositions in simulation, and evolved skills run autonomously on a physical Unitree G1 from egocentric onboard perception.

Decentralized multi-agent path finding (MAPF) with communication requires agents to reach individual goals without collisions under partial observability. Learnable policies trained on expert data provide an effective approach to this problem. However, when several coordinated joint actions are valid in the same context, independently sampling from per-agent distributions can recombine locally valid choices into incompatible joint actions. This failure can arise from the final sampling mechanism even when the per-agent action distributions are learned correctly. DMM (Decentralized Master-Mind) addresses this by replacing one-shot action sampling with discrete, iterative refinement of action intents across communication rounds, inspired by denoising in diffusion models. Agents initialize random action intents and refine them through local communication, coupling their choices before commitment. DMM is pretrained with imitation learning on expert MAPF solutions and further optimized with MICPO, a critic-free group-relative reinforcement-learning method designed for multi-agent, multi-round action refinement. DMM generally achieves higher success rates and lower solution costs than the evaluated learnable baselines. On 1,600 MovingAI tasks, DMM fine-tuned with MICPO solves 1,598, the highest coverage among the evaluated methods, while achieving solution costs close to those of the strongest baselines. DMM also scales to over one million simultaneously acting agents in obstacle-rich environments. These results show that round-level intent refinement can improve joint-action coordination while preserving decentralized execution.

An activation-steering coefficient sets intervention strength, but requesting a particular degree of persona expression requires a behavioral scale. We study persona dosing: controlling a language model through a trait description and a requested mean intensity. PersonaDose specializes a shared, description-conditioned FLAS controller on persona responses, then calibrates its flow time against measured trait expression. Training responses are not paired with requested target intensities. Across Llama-3.1-8B, Qwen3-8B, and Gemma-3-4B, PersonaDose raises core-trait expression at the Persona Vectors coherence floor of 75 by 33.2, 18.3, and 17.8 points over contrastive activation addition. Calibration-selected settings retain an expression advantage on held-out questions, although the coherence floor does not hold for every trait there. Across seven trained traits, calibrated requests yield mean targeting errors of 4.7-6.2 points over 14-22 calibration-reachable targets out of 28 per model. These results separate the behavioral range learned by a controller from the accuracy of requests within that range.

Vision language model (VLM) agents can control robots through visual feedback and action primitives, but repeated model invocations and redundant observations incur substantial token overhead. We introduce PyRUA-Lean, an interactive code-execution framework that couples feedback-driven primitive composition with selective observation: the agent composes classical robot primitives and learned vision-language-action (VLA) policies into Python cells that perform conditional checks and local retries, returning only explicitly requested images and state feedback for replanning. Across 700 simulated task instances from LIBERO-PRO, RoboTwin 2.0, and RoboCasa365, we compare PyRUA-Lean with a tool-calling baseline using the same GPT-6 Astra planner and underlying robot primitives. Under equal LLM-call budgets, PyRUA-Lean increases overall success from 63.1% to 71.7%. On instances solved by both agents, it uses 49% fewer LLM calls and 65% fewer input tokens.

Multi-step agents are trained on flat action streams: SFT and RLVR weight every token uniformly and ignore the sub-procedures that recur across tasks, the hierarchy that lets humans plan top-down from reusable routines. This structure sits unused, and flat training uses each scarce trajectory less fully than its content allows. Recent agents do use that structure, but only as LLM-written skills in context, never in the weights, so their gains do not generalize beyond retrieval. We instead recover this hierarchy from the data itself and train on it, with no LLM calls. Following text tokenizers, which build a vocabulary by counting alone, we score action spans by reusability and merge canonicalized actions into a reusable eXperience tree (X-Tree). Each X-Tree node captures how a frequent and success-bearing skill is composed from sub-skills, guiding efficient generalization. We integrate X-Tree into three training settings: offline RL, with each node as a training instance; online RLVR, with an adaptive skill bonus; and on-policy self-distillation, with X-Tree as the self-teacher's privileged context. Across WebArena, ScienceWorld, and WebShop at three model scales, X-Tree improves over standard recipes at matched data and budget by up to 4.5% SR on WebArena, 5.8% SR on ScienceWorld and 4.1% success on WebShop. Matched analyses attribute the gains to the X-Tree structure and the three integrations.

High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize local geometry. While effective, this design fragments continuous surfaces into many local tokens, inflates generation cost, and often weakens topological consistency for thin or highly connected shapes. We introduce SILSA, a topology-aware 3D generation framework that represents shapes with compact sliding-window slice latents. Instead of generating expensive voxel tokens, SILSA uses a fixed set of overlapping slices along the three canonical axes, where each token summarizes a local depth window to preserve cross-sectional continuity and support single-stage rectified-flow generation. A Slice VAE encodes oriented surface samples into multi-axis slice latents and reconstructs them with a sparse volumetric decoder, while a Volumetric Anchor Lattice coordinates directional slice streams through a shared 3D workspace. To preserve structural correctness, we introduce slice-level topology supervision that matches persistence diagrams and aligns Betti transitions across neighboring slices. Experiments show that SILSA improves structural fidelity while substantially reducing generation cost. SILSA improves PSNR by 8.7%, coverage by 5.96 absolute points, and Betti error by 9.2% over the strongest baseline, while using 70.0% fewer tokens than the next-most compact baseline and over 98% fewer tokens than sparse or hierarchical tokenizers, effectively reducing training memory by 40.4% and inference time by 58.5%. Qualitative results further show improved preservation of thin structures, repeated components, and long-range connectivity.

Furnished floor plans support real-estate visualization, interior design, and architectural workflows, yet automatic furnishing remains challenged by limited real-world data and the need to satisfy interacting geometric and functional constraints. We ask whether professional furnishing knowledge can be learned from real floor plans using a pretrained model, enabling direct constraint-aware layout generation without relying on costly iterative agentic inference. We introduce AntPlan, a curated dataset of 505 real professional architectural floor plans with dense furniture annotations spanning 92 object classes and ten residential room categories, and Architect-Ant, a framework for generating furniture layouts. Architect-Ant represents layouts with an editable coordinate-based DSL and first learns professional furnishing patterns through supervised fine-tuning. It is then optimized with GRPO using a Layout Rule Score (LRS) that aggregates geometric and functional constraints derived from professional plans, providing outcome-level supervision without prescribed reasoning traces. Experiments against diverse state-of-the-art baselines show that Architect-Ant combines low geometric violation rates with high functional completeness, while qualitative results more closely reflect real-world residential furnishing patterns. The resulting layouts remain object-level editable and can be converted into 3D scenes.

Looped Transformers achieve parameter efficiency by repeatedly executing a shared block across recurrent loops. Each loop yields an intermediate representation decodable for the same next token, yet standard decoding discards earlier states. Because earlier loops embody less computation, recurrence inherently supplies aligned weak-and-strong prediction pairs without auxiliary models or external training. We introduce LoopCD, a training-free contrastive decoding framework that guides token selection by contrasting the final prediction with an earlier recurrent pass, operating either in logit space with one extra output pass (LoopCD-Logits) or in hidden-state space with zero output overhead (LoopCD-Hidden). Across four looped Transformer families, LoopCD delivers substantial, consistent gains at full recurrent depth: LoopCD-Logits raises Ouro-2.6B-Thinking's AIME 2024 pass@1 from 61.88% to 73.33%, while LoopCD-Hidden lifts Huginn's HumanEval pass@1 from 22.56% to 31.71%. Crucially, these performance gains enable halving the number of recurrent loops while still matching or exceeding full-depth unguided baselines, reducing forward FLOPs by 22.5% to 48.2%. By transforming intermediate recurrent states into effective guidance signals, LoopCD achieves superior decoding quality while substantially reducing inference compute.

David Robinson / The Atlantic : David Robinson, ex-OpenAI safety and policy: SV lacks a safety-centric culture; labs must study other fields' safety approaches; time for trial and error's over —  What I'm about to tell you has, I realize, become something of a cliché: I resigned this week from OpenAI.

Reuters : Sources: ShinyHunters member Saif al-Din Khader, aka “Rey,” was detained in Jordan and is cooperating to identify other hackers involved in the FBI breach —  A key member of the ShinyHunters hacking group, which claims to have stolen data on every FBI employee …

Andrew Romero / 9to5Google : YouTube says it is adjusting its Shorts recommendations to prioritize “original content” and reduce the reach of channels re-uploading others' content —  YouTube says it wants to ‘focus on originality’ in a push to surface better Shorts recommendations for users.

Sarah Perez / TechCrunch : OpenAI's DevDay 2026 announcements to turn ChatGPT into a place to discover, launch, and use software could potentially disrupt the traditional app store model —  The focus of OpenAI's Dev Day on Tuesday may have been on its agentic assistants known as Dots, or its new AI models, but combined …

Nilesh Christopher / Los Angeles Times : A look at a radio show co-hosted by an AI DJ and a human that is expanding in LA and other cities, as radio workers worry synthetic hosts may soon replace them —  - 8 min Click here to listen to this article  — Coyotec, an AI co-host on Los Angeles' José FM …

Howard Liu / South China Morning Post : Center for Technology & Statecraft: Chinese fabs had acquired ~343 DUVi tools by early 2026, with ~270 from ASML; DUVi can be adapted to make 7nm chips and HBM —  DUV tools, though not as advanced as EUV, can be adapted to manufacture 7-nm logic chips and advanced memory for AI processors, report says.

Robert McMillan / Wall Street Journal : A look at the Swarmchasers forum, which has 400 members, including the Nightingale Collective and Transluce, who comb the web for traces of rogue AI agents —  Swarm chasers hunt for clues of bad behavior.  Their work is our starkest understanding yet of what happens when AI goes wrong.

Mike Wheatley / SiliconANGLE : Relay, which develops cloud-hosted, AI-powered smart radio communicators for frontline workers, raised $36M to help businesses capture “frontline intelligence” —  The business communications startup Relay Inc. says it's trying to capture “frontline intelligence” …

Meir Orbach / CTech : Nasdaq-listed neocloud Nebius acquires Inferize, whose tech helps optimize GPU utilization and reduce AI request costs, sources say for $100M to $150M —  The Israeli startup, which was founded by former Granulate executives and employs 17 people, built technology designed to reduce idle GPU capacity when AI demand changes.

Tim Culpan / Culpium : Elon Musk confirms TSMC discussions on a Terafab deal to supply Tesla, SpaceX, and xAI, as TSMC also mulls a Texas presence; Musk didn't elaborate —  [Exclusive] Taiwanese foundry giant may use Terafab as anchor client for a future Texas semiconductor factory

Ram Iyer / TechCrunch : Reco, whose tech helps enterprises secure and govern AI agents across SaaS environments, raised a $55M Series B extension, taking its total funding to $140M —  Terms like “AI sprawl” have become common fare on tech social media and in thought leadership as enterprises start deploying AI agents en masse.

Elias Schisgall / Wall Street Journal : Onsemi revises its Synaptics acquisition to a ~$5.7B all-cash offer, down from the ~$7B all-stock deal announced in June, after Synaptics received another bid —  ON Semiconductor says it will pay $123 a share for Synaptics, giving the deal a value of around $5.7 billion

Jason Koebler / 404 Media : A US judge rules an officer's use of Flock to search a car's license plate was a form of “indiscriminate mass surveillance”, and violated the Fourth Amendment —  Flock's nationwide network is quickly “approaching dragnet-type law enforcement practice” and the cop should have got a warrant, the judge wrote.

Maria Deutscher / SiliconANGLE : Supabase raised $150M led by Singapore's GIC and agrees to acquire Turso, which offers a database optimized for AI agents, for an undisclosed sum —  Supabase Inc., a startup that commercializes the open-source PostgreSQL database, has raised $150 million in funding.  —  Singapore's GIC sovereign wealth fund led the deal.

Business Insider : David Robinson, who worked on OpenAI's Safety Systems team and had previously led policy planning, left OpenAI last week —  - David Robinson, a leader on OpenAI's Safety Systems team, resigned last week.  — In a published essay, he suggested the company doesn't have enough humility to handle AI responsibly.

当前一代游戏机的模拟器通常需要较长时间才能成熟，但 PS5 的模拟器仅仅几个月时间就让许多 PS5 游戏能在 PC 平台上可玩。SharpEmu 从 5 月的极早 Alpha 阶段到现在具备加载真实游戏 eboot.bin 文件、执行原生 CPU 指令以及部分处理 GPU 相关功能的能力。已有 10 款游戏被标记为可玩，其中包括简单 2D 游戏如 Tetris Forever，也有复杂 3D 大作如 Astro Bot 和 Demon’s Souls 重制版。另一款 PS5 模拟器 KytyPS5 也于上周发布了首个公开版本，它是 PS4 模拟器 Kyty 的扩展版，能启动 2D 游戏及部分 3D 游戏，包括使用虚幻引擎 4/5、Unity 以及自研引擎开发的作品。有 134 款游戏标记为可玩，但很多存在严重 bug。

一名玩家购买了一个二手 CPU Ryzen 7 5800X3D，结果发现无法启动 Riot 工作室旗下的多款游戏，每次启动游戏就被踢出，在联络了 Riot 的客服之后才知道该 CPU 被列入了封禁黑名单，因为其前任主人有作弊行为。Riot 工作室旗下的所有游戏都受到影响，其中包括了 Valorant、League of Legends、Teamfight Tactics、Legends of Runeterra、2XKO 等。Riot 使用了内核级反作弊系统 Vanguard，它深度嵌入在 Windows 内核模式中。暂时不清楚 Riot 是如何唯一标识 CPU 的，CPU 会向操作系统报告硬件 ID，但该硬件 ID 不是唯一标识符，只是告诉操作系统其 CPU 型号。它可能是通过 CPU 运行产生的独特指纹标记 CPU，每个 CPU 在运行时候都会有微小的差异。

为了提高生育率，新加坡试点推出了为公务员牵线搭桥的约会应用 FirstDate。新加坡的总和生育率已降至每名女性生育 0.87 个孩子，而十年前这一数字为 1.24。政府最近成立了一个专门研究生育率下降问题的工作组，预计该作组将在 2027 年初发表研究结果。FirstDate 面向 21-35 岁的单身人士，目前仅向公务员开放，申请截止日期为 10 月 5 日。用户无需浏览海量的个人资料，而是填写一份关于兴趣、习惯、价值观和偏好的问卷。FirstDate 会在每个周期内（即双方互相接受并预计见面所需的时间）为用户推送一个匹配对象。每项匹配结果都包含匹配度评分、对方的简介和一段个人留言。该服务使用了 Gale-Shapley 稳定婚姻算法。有公务员认为该应用的一大优势可能是被诈骗的可能性较低。

根据发表在 PLOS One 期刊上的一项研究，猫的数量与幸福感正相关，一个国家的猫越多，其幸福感通常越高。但主要通过猫传播的弓形虫感染率与幸福感负相关，弓形虫感染率较高的国家的幸福指数通常较低。两个关系似乎矛盾，但也可能与卫生、经济条件相关联。研究分析了 93 个国家和地区的数据，幸福指数平均值为 5.72，每万人拥有猫数量的平均值为 989 只，其中中国的幸福指数为 5.97，略高于平均水平；每万人拥有猫的数量为 376 只，显著低于平均值，中国的弓形虫感染率与平均水平相当。研究作者提出，猫对幸福感的影响可能与经济因素有关。也就是随着收入水平的提高，公共卫生条件会得到改善，虽然猫的数量增加，但感染风险不会有相应的增加。

由于索尼频繁更新 PS5 的固件，而大部分 PS5 越狱方法只针对特定固件版本的漏洞，因而这些越狱方法实用性相当有限。但情况在本周二发生了变化，名为 Relapse 的漏洞利用方法适用于最高固件版本 v13.6 的 PS5 游戏机，而 v13.6 是在今年 7 月释出的，意味着 PS5 只要不更新最新固件，就能成功越狱。Relapse 利用了 PS5 浏览器的一个已知的 WebKit 漏洞，提权获取内核的写入访问权限，安装 ELF 加载器去简化任意代码的运行。越狱后的 PS5 除了能备份游戏外还能运行模拟器以及 PS4 游戏的非官方 60 帧 MOD。