OrangeBot.AI Digest — 2026-06-01

Generate more accurate investment dashboards and reports

Local natural language search across all your files

Control your robots from anywhere in the world

A smart LLM proxy that shows where you're overpaying

Local AI workspace to build and work with your computer

Chat with your business data inside Claude, ChatGPT and more

AI social media copilot for teams and agents

Context-aware Mac keypad, w/ Claude + community extensions

the AI in your texts that gets stuff done

Take your presentation skills to the next level

Keep your Mac awake

Advertise together on Meta Ads and pay 3x less

Your AI Teammate now responds and executes during your calls

Voice AI copilot for coworking & coliving operators

Run a research agent with cited answers in a single API call

Large Language Model (LLM) search agents have shown strong promise for knowledge-intensive language tasks through multiple rounds of reasoning and information retrieval. Most existing systems access information using a retriever that takes a keyword or natural language query and returns a ranked list of documents using an index of pre-computed document representations. In this work, we explore a complementary perspective in which the search agent treats the corpus itself as the search environment and finds evidence by issuing executable shell commands. We introduce GrepSeek, an optimized direct corpus interaction (DCI) search agent that trains a compact search agent to find, filter, and compose evidence from large text corpora. To address the instability of learning behavior directly with reinforcement learning on large corpora, we propose a two-stage training pipeline. First, we construct a cold-start dataset using an answer-aware Tutor and answer-blind Planner to generate verified, causally grounded search trajectories. Second, we refine the initialized policy with Group Relative Policy Optimization (GRPO), allowing the agent to improve its task-oriented search behavior through direct interaction with the corpus. To make DCI practical at scale, we further use a semantics-preserving sharded-parallel execution engine that accelerates shell-based retrieval by up to 7.6times while preserving byte-exact equivalence with sequential execution of the shell command. Experiments across seven open-domain question answering benchmarks show that GrepSeek achieves the strongest overall token-level F_1 and Exact Match. Our analysis also highlights the limitations of purely lexical interaction on queries with substantial surface-form variation, suggesting DCI as a practical and competitive method for search agents that can complement existing retrieval paradigms in the real world.

LLM agents are increasingly expected not only to complete isolated tasks, but also to carry bounded representations of human expertise, judgment, and interaction style. Building such person-grounded agents remains difficult because actionable knowledge associated with a person or role is usually embedded in heterogeneous traces rather than written as clean instructions. Existing memory and persona systems capture fragments of this evidence, while skill frameworks provide portable packaging formats; however, there is no end-to-end workflow for distilling these traces into inspectable, correctable, and agent-usable skills. We present an automated trace-to-skill distillation system for generating person-grounded AI skills via expert knowledge distillation. Given materials from a target person or role, COLLEAGUE.SKILL produces a versioned skill package with two coordinated tracks: a capability track for practices, mental models, and decision heuristics, and a bounded behavior track for communication style, interaction rules, and correction history. The package can be inspected, invoked, updated through natural-language feedback, rolled back, installed across agent hosts, and optionally prepared for controlled distribution. We describe the artifact contract, generation workflow, correction lifecycle, deployment surface, and domain presets implemented in the open-source system. At the time of writing, the public repository has approximately 18.5k GitHub stars; the gallery lists 215 skills from 165 contributors and more than 100k cumulative stars across listed skill cards. The system illustrates how person-grounded skills can be represented as portable, correctable packages rather than opaque prompts or hidden memories.

On-policy distillation (OPD) trains a student on prefixes sampled from its own policy while matching a stronger teacher. This addresses the prefix mismatch of offline distillation, but early student rollouts can still be poor, placing teacher supervision on weak or low-quality prefixes. We propose Trust-Region behavior Blending (TRB), a warmup method that replaces the early rollout policy with the closest-to-teacher behavior policy inside a student-centered KL trust region, while keeping the per-prefix reverse-KL OPD loss unchanged. The KL budget is annealed to zero, so training returns to pure student rollouts after warmup. Across two math-reasoning distillation settings, TRB attains the strongest average among the compared methods.

Zero-shot text-to-speech (TTS) has improved substantially for single-speaker synthesis, yet expressive long-form multi-speaker dialogue remains difficult. A common workaround is to synthesize each turn with a monologue TTS model and stitch the outputs together. This adds inference cost and often breaks acoustic consistency, conversational coherence, and affective continuity across turns. Recent dialogue TTS systems have begun to address this setting, but they still struggle to keep expressive coherence, controllable speaker switching, and monologue quality at the same time. We present SwanData-Speech and SwanVoice. SwanData-Speech builds monologue and dialogue corpora from in-the-wild audio, using Swan Forced Aligner for pause-aware word-level alignment and RobustMegaTTS3 for pronunciation-hard cases. Built on these data, SwanVoice is a zero-shot TTS model for 1--4 speakers, combining a 25 Hz VAE, raw-text conditioning with pause-aware symbols and pinyin substitution, and a flow-matching DiT with speaker-turn conditioning. Training starts from monologue speech, moves through mixed and real dialogue data, and then uses DiffusionNFT post-training with phone-level and speaker-similarity rewards. On SwanBench-Speech, SwanVoice obtains higher richness and hierarchy scores than all evaluated open-source baselines in both monologue and dialogue settings, while content accuracy remains the main limitation. Audio demos are available at https://swanaigc.github.io//#swanvoice.

We present Mellum 2, an open-weight 12B-parameter Mixture-of-Experts (MoE) language model with 2.5B active parameters per token. Mellum 2 is a general-purpose language model specialized in software engineering, spanning code generation and editing, debugging, multi-step reasoning, tool use and function calling, agentic coding, and conversational programming assistance, and it is the successor to the completion-focused 4B dense Mellum model. The architecture builds on the Mixture-of-Experts (64 experts, 8 active) and combines Grouped-Query Attention with 4 KV heads, Sliding Window Attention on three of every four layers, and a single Multi-Token Prediction head that doubles as both an auxiliary pre-training objective and a built-in draft model for speculative decoding; each choice was validated by ablation with inference efficiency on commodity GPUs as a design constraint. Pre-training spans approximately 10.6 trillion tokens through a three-phase curriculum that progressively shifts the mixture from diverse web data toward curated code and mathematical content, optimized with Muon under FP8 hybrid precision and a Warmup-Hold-Decay schedule with linear decay to zero. The pre-trained base is extended to a 128K context window via a layer-selective YaRN and then post-trained in two stages (supervised fine-tuning followed by RLVR), yielding two released variants: an Instruct model that answers directly and a Thinking model that emits an explicit reasoning trace before its final answer. Across code generation, math and reasoning, tool use, knowledge, and safety benchmarks, Mellum 2 is competitive with open-weight baselines in the 4B-14B range while running at the per-token compute of a 2.5B dense model. We release the base, instruct, and thinking checkpoints, together with this report on the architecture decisions, data pipeline, and training recipe behind them, under the Apache 2.0 license.

Real-world image restoration (IR) is bottlenecked by the scarcity of high-quality paired training data. Synthetic datasets are abundant but often fail to model real-world degradations, while real-world paired datasets are expensive and difficult to capture. As a result, IR models trained on these datasets show limited generalization in real-world scenarios. In this work, we propose Generative Ground Truth (GGT) by using generative multimodal foundation models (MFMs) to produce high-quality (HQ) targets from real-world low-quality (LQ) images. We first conduct a systematic evaluation of nine state-of-the-art MFMs, including Nano-Banana-2 and GPT-Image-2, on images of various scenes and degradation types. The results demonstrate that Nano-Banana-2 with VLM-based adaptive prompting shows the highest capability to synthesize perceptually realistic and content-faithful HQ targets, which can serve as the GGT for the LQ input. We then employ Nano-Banana-2 to build a GGT synthesis pipeline, which involves multi-stage quality control to ensure data reliability, and construct GGT-100K, an LQ-HQ paired dataset comprising 103,707 training pairs and covering diverse scenes and complex real-world degradations. A test set of 500 image pairs is also established. Extensive experiments show that GGT-100K consistently improves the real-world generalization of a wide range of IR models, with particularly strong benefits for finetuning generative models for IR tasks. Our results suggest that MFMs can serve as practical tools for restoration-oriented data generation, and GGT-100K is a useful resource to expand the generalization boundaries of real-world IR models.

Real-time and accurate spatial audio generation is pivotal for delivering an immersive experience. However, existing spatial audio synthesis technologies are often encumbered by a tradeoff between generation quality and high inference latency, as well as difficulty in capturing precise spatial information from multimodal inputs. To address these challenges, we propose SwanSphere, a unified streaming framework for high-fidelity spatial audio generation from panoramic videos and text prompts. SwanSphere mainly makes the following contributions: 1) We introduce a causal autoregressive diffusion transformer architecture that enables streaming high-quality spatial audio generation. 2) We design a Spatial Video-Audio Contrastive (SVAC) learning strategy to align the video encoder with the acoustic domain, and further employ a multi-objective online direct preference optimization (ODPO) scheme, resulting in strong spatial perception and robust multimodal spatial audio synthesis. 3) To alleviate the current scarcity of spatial audio datasets, we also develop an automated annotation pipeline for generating detailed spatial captions. Experimental results demonstrate that SwanSphere achieves superior performance in both video-to-spatial and text-to-spatial audio generation tasks. Demos can be found at: https://swanaigc.github.io.

Real-time streaming video-to-video editing (V2V) is critical for interactive applications such as live broadcasting and gaming, yet it remains a formidable challenge due to the stringent requirements for temporal consistency and inference throughput. In this paper, we present SANA-Streaming, a system-algorithm co-designed framework for high-resolution, real-time streaming video editing on consumer GPUs, with the following three core designs: (1) Hybrid Diffusion Transformer architecture introduces softmax attention in part of the blocks to improve local modeling capabilities while preserving the efficiency of linear layers. (2) Cycle-Reverse Regularization is a novel training strategy that enforces semantic consistency by predicting source frames from generated content via flow matching, improving temporal consistency without requiring paired long edited videos. (3) Efficient System Co-design combines fused GDN kernels and Mixed-Precision Quantization (MPQ) optimized for the NVIDIA Blackwell (RTX 5090) architecture. By profiling real-world throughput, our MPQ maximizes Tensor Core utilization while maintaining generation quality. The resulting system achieves real-time 1280 x 704 resolution editing at 24 end-to-end FPS on a single RTX 5090 GPU, with the DiT core running at 58 FPS. Experimental results demonstrate that our co-design approach significantly outperforms existing SOTA methods in both temporal coherence and system throughput.

Recent advances in speech generation have enabled high-fidelity synthesis, yet systematic evaluation of models under long-context conditions remains largely underexplored. A comprehensive evaluation benchmark for long-form speech is indispensable for two reasons: 1) existing test scenarios are often confined to limited domains, creating a significant gap with the diverse downstream applications; 2) existing metrics overlook critical long-text factors such as consistency and coherence, failing to generalize reliably. To this end, we propose Swanbench-Speech, a comprehensive benchmark that decomposes long-form speech quality into specific, disentangled dimensions. SwanBench-Speech has three key properties. 1) Rich speech scenarios: Focusing on long-form speech generation and dialog generation, SwanBench-Speech covers acoustics, semantics, and expressiveness challenges, and consists of 1,101 samples spanning 17 common speech scenarios; 2) Comprehensive evaluation dimensions: Along the acoustics, semantics, and expressiveness axes, SwanBench-Speech defines an automated evaluation protocol with seven metrics to provide a comprehensive, accurate, and standardized assessment; 3) Valuable Insights: Through extensive experiments, we reveal that current models still struggle in highly expressive scenarios and exhibit a notable gap in consistency and hierarchy compared to real recordings.

Long-term memory is essential for multimodal agents to build coherent experience, accumulate world knowledge, and achieve continual learning. However, constructing effective memory goes beyond memory module design and basic requirements such as accuracy and fidelity; the key challenge lies in determining what to memorize. Multimodal agents, such as embodied agents, continuously perceive, reason, and act in real or virtual environments, receiving an unbounded stream of multimodal observations. From this combinatorial explosion of information, an agent must selectively retain content that is relevant to its role in the environment and valuable for future tasks. To bridge this gap, we frame memory generation as a learnable memorization policy and introduce TaskMem (Task-focused Memorization Policy Learning), a reinforcement-learning-based framework that enables the policy to dynamically adjust its focus to the demands of real tasks encountered in the environment. TaskMem adopts a two-phase training paradigm: Phase One learns how to memorize by optimizing memory quality under fundamental fidelity requirements; Phase Two occurs after deployment, where the agent learns what to memorize by tuning an adapter on its base MLLM, using recent environment tasks to define a reward model that guides the memorization policy toward task-relevant content. To evaluate our approach, we reformulate VideoMME, EgoLife, and EgoTempo into streaming benchmarks that simulate a realistic setting in which an agent processes streaming observations and handles tasks arriving online. To isolate memory assessment, the questions must be answered using only the agent's memory, without access to raw video. Built on Qwen3-VL-30B-A3B, TaskMem improves VQA accuracy by 6.3%, 7.0%, and 5.3% on these benchmarks, respectively.

Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive models, offering competitive performance while naturally supporting parallel decoding. However, as dLLMs are increasingly integrated with Mixture-of-Experts (MoE) architectures to scale model capacity, a fundamental mismatch arises between block parallel decoding and token-level expert selection. Specifically, each dLLM forward pass processes multiple tokens with bidirectional dependencies, whereas conventional MoE layers route each token independently. This mismatch substantially increases the number of uniquely activated experts, making inference increasingly memory-bound. To address this, we propose dMoE, a simple yet effective block-level MoE framework. The central idea of dMoE is to aggregate token-level expert distributions within each block into a unified block-level expert distribution, which is then used to guide expert routing in a more coherent manner. In this way, dMoE substantially reduces the number of uniquely activated experts during inference without sacrificing performance, thereby mitigating the memory-bound bottleneck. Extensive experiments across a variety of benchmarks demonstrate the effectiveness of dMoE. On average, dMoE reduces the number of uniquely activated experts from 69.5 to 14.6 while retaining 99.11% of the original performance. Meanwhile, it reduces memory usage by 76.64% to 79.84% and achieves 1.14times to 1.66times end-to-end latency speedup. Code is available at: https://github.com/fscdc/dMoE

On-policy distillation (OPD) trains a student on its own rollouts with token-level teacher supervision. Recent selective OPD methods exploit the non-uniformity of OPD signals by prioritizing high-entropy or high-disagreement tokens. We revisit this principle and ask: which token-level teacher signals are actually learnable? Using a fixed-context diagnostic that measures same-context teacher-student KL reduction, we show that raw KL disagreement is a coarse proxy for learning value. It conflates learnable disagreement, where the teacher assigns corrective mass to the student's top-K candidates, with incompatible disagreement, where the teacher places mass mostly off the student's current support. We formalize this local compatibility as token teachability and show that it better predicts fixed-context improvement than raw KL alone. Motivated by this finding, we propose Teachability-Aware OPD (TA-OPD), a lightweight token-position selection method that applies OPD loss to high-teachability positions without reward models or verifiers. Across Qwen2.5 and Qwen 3 teacher-student settings, TA-OPD often surpasses full-token OPD with only 5% retained tokens and improves over entropy- and divergence-based baselines. Our results reframe selective OPD as selecting learnable teacher signals rather than merely salient tokens.

Self-play can train language models without external supervision. However, existing methods require rule-checkable answers, leaving open-ended tasks dependent on curated prompts or frontier-model judges. We introduce SCOPE, a data-free self-play framework for open-ended tasks that co-evolves two policies: a Challenger that generates document-grounded tasks, and a Solver that answers them through multi-turn retrieval. A frozen copy of the initial model serves as the self-judge, which writes task-specific rubrics from the source document and grades Solver responses against them. Across three 7-8B instruction-tuned models (Qwen2.5, Qwen3, OLMo-3), SCOPE improves open-ended performance by up to +10.4 points on eight benchmarks and matches or exceeds GRPO_data trained on ~9K curated prompts. Although trained only on open-ended tasks, SCOPE also improves held-out short-form QA by up to +13.8 points on seven held-out benchmarks, surpassing GRPO_data on all three models. Ablations show that co-evolving the Challenger is necessary to keep tasks near the Solver's frontier, that gains arise from improvements in both retrieval and synthesis with the relative contribution varying by task, and that rubric generation quality is the bottleneck for self-judging.

Video-language models can process only a limited number of frames, making frame selection a key bottleneck for efficient video captioning. Most captioning pipelines still rely on uniform sampling, which is computationally cheap but agnostic to visual content. Adaptive frame sampling has recently emerged as a promising approach for selecting the most informative frames from a video; however, existing methods remain computationally expensive. We introduce PEEK, an efficient dynamic frame sampling method that distills caption-conditioned frame relevance rankings from a stronger teacher model into a lightweight temporal model that operates only on visual content. We find that, overall, on ActivityNet Captions and MSR-VTT, our method outperforms state-of-the-art methods across all evaluated downstream vision language models, especially when only one or two frames are selected for captioning, obtaining the best CIDEr for most frame budgets. On ActivityNet Captions, PEEK is particularly strong, winning 14 out of 16 configurations. Zero-shot evaluation on MSR-VTT shows that our model transfers best at low frame budgets, while results at four and eight frames are more mixed as temporal coverage and visual diversity become increasingly competitive. Compared with recent adaptive baselines, PEEK is both more accurate in the low-budget regime and more efficient: it adds only 5.2% to the captioning time, compared with 65.4% for CSTA and 211.9% for MaxInfo. We release our code and pre-trained checkpoint at https://github.com/momentslab/peek.

Agentic search enables LLMs to solve complex multi-hop questions through iterative reasoning and external search. Despite the effectiveness, these systems often suffer from a critical limitation in practice: agents fail to recognize their own knowledge boundaries, blindly triggering searches when internal knowledge suffices and failing to terminate search even when adequate evidence has been collected. The lack of self-awareness leads to severe over-search, incurring substantial inference latency and prohibitive computational cost. To this end, we propose SAAS, a novel RL framework designed to cultivate dynamic self-awareness that precisely regulates search behavior without compromising accuracy. SAAS introduces three key components: (i) a search boundary modeling mechanism, which identifies the search boundary under the evolving policy by contrasting search-disabled and search-enabled rollouts; (ii) a boundary-aware reward module, which translates this boundary awareness into trajectory-level penalties, suppressing unnecessary and redundant searches; and (iii) a stage-wise optimization strategy, which leverages a sequential curriculum to prioritize reasoning over search regularization, thereby avoiding reward hacking. Extensive experiments demonstrate that SAAS substantially reduces over-search, while maintaining accuracy. Our code is anonymously released at https://github.com/XMUDeepLIT/SAAS.

Bloomberg : Alphabet is raising $80B in equity offerings, including a $10B investment deal with Berkshire Hathaway, to help raise money for its AI spending plans —  Google parent Alphabet Inc. is raising $80 billion in equity offerings, including an investment deal with Berkshire Hathaway Inc. …

Brody Ford / Bloomberg : HPE reports Q2 revenue up 40% YoY to $10.7B, vs. $9.74B est., Server revenue up 33%, forecasts revenue for FY26 and FY27 above est.; HPE jumps 30%+ after hours —  Hewlett Packard Enterprise Co. shares soared in extended trading after the company gave an outlook for annual sales that topped estimates …

Michelle Ma / Bloomberg : Gigascale Capital, a climate tech VC firm co-founded by former Meta CTO Mike Schroepfer, closed a $250M fund to back early-stage startups supporting the AI boom —  Gigascale Capital closed a $250 million fund for early-stage startups.  —  Climate tech venture capital firm Gigascale Capital …

Rohan Prabhu / Step Security Blog : Researchers find packages in the @redhat-cloud-services npm namespace shipped malware that harvests credentials for GitHub Actions, AWS, GCP, Azure, and others —  Several packages in the @redhat-cloud-services npm scope were found to carry malicious payloads that fire via a preinstall hook on every npm install.

Jason Koebler / 404 Media : Hackers say they used Meta's AI support chatbot to change emails tied to Instagram accounts, amid a wave of high-profile account takeovers; Meta fixed the issue —  The exploit shows the extreme risk of offloading technical support to AI.  —  Hackers say that they used Meta's AI support chatbot …

Brody Ford / Bloomberg : Source: Salesforce has a stake in Anthropic worth ~$5B; Salesforce first invested about $50M in an early 2023 round and has continually invested in rounds since —  Salesforce Inc. has a stake in Anthropic PBC worth about $5 billion after repeatedly investing in the ascendant AI startup.

Matthew Griffin / Bloomberg : IBM shares jump 7.6% after a nearly six-month old video of President Trump praising CEO Arvind Krishna recirculated on X; IBM was already up ~40% over two weeks —  A nearly six-month old video of Donald Trump praising International Business Machines Corp.'s CEO sent the shares surging …

Lucinda Shen / Axios : Stockholm-based Endra, which automates the design of plumbing and electrical wiring in new buildings, raised $50M led by a16z —  Endra, a startup automating the design of plumbing and electrical wiring in new buildings, raised $50 million led by Andreessen Horowitz, it tells Axios exclusively.

Mike Isaac / New York Times : Anthropic says it has confidentially filed for an IPO, which could happen as soon as this fall, joining OpenAI and SpaceX in preparing to go public in 2026 —  The artificial intelligence company, which is racing OpenAI to the stock market, has seen explosive growth over the last year thanks largely …

Anthropic : Anthropic confidentially submits draft S-1 to the SEC —  Today, Anthropic, PBC confidentially submitted a draft registration statement on Form S-1 to the U.S. Securities and Exchange Commission for a proposed initial public offering of our common stock.  This gives us the option to go public after the SEC completes its review.

Ben Weiss / Fortune : New York City-based Mecka AI, which trains robots with human data sourced from body sensors and iPhones, raised $60M, including a $25M Series A —  Data is the oil of the AI boom—and the startup Mecka AI hopes to tap the vast reservoir of hand gestures, walking gaits, and immense collection …

Kerry Flynn / Axios : Sekai, which lets users create mini apps through text prompts, raised a $20M Series A co-led by Khosla Ventures and Connect Ventures, after a $6M seed in 2025 —  Sekai has raised a $20 million Series A to grow a platform for creating mini apps through text prompts, founder and CEO Lucky Zhang exclusively tells Axios.

Kalley Huang / New York Times : Box has created 13 AI-focused roles, like AI architect and AI solutions manager, and plans to grow from 2,900 staff at the start of 2026 to 3,000+ by early 2027 —  Box, a Silicon Valley software maker, expects to have more employees, not fewer, as it hires A.I. architects, A.I. solutions managers and other new A.I.-related positions.

Bernie Sanders / New York Times : Bernie Sanders says the wealth AI creates “must benefit humanity”, calling for a sovereign wealth fund that would hold ownership stakes in top US AI companies —  Artificial intelligence will almost certainly be the most transformational technology in the history of the world.

Tom Warren / The Verge : Sources: at Build, Microsoft plans to unveil a Copilot “super app”, a new reasoning model developed by Microsoft AI, and Windows improvements for developers —  Build will include a Copilot super app, a new reasoning AI model, and lots of Windows improvements.

The International Aids Vaccine Initiative(IAVI)、牛津大学以及 Moderna 公司正在研发针对埃博拉病毒的疫苗。IAVI 表示正在刚果民主共和国爆发的埃博拉疫情可能是至今最严重的。疫情发生在冲突地区，已经报告了逾千例疑似病例，邻国乌干达已确诊 9 例。目前已知有六种埃博拉病毒株，只有三种会引发疫情。最常见的 Zaire 毒株已有针对性的疫苗，但此次爆发的是比较罕见的 Bundibugyo 毒株，目前还没有针对它的疫苗。Moderna 公司宣布将利用 mRNA 技术研发针对 Bundibugyo 毒株的疫苗。

最近发表的两项研究显示，巴西亚马逊地区开始出现此前预测几十年后才会出现的情景，包括旱季延长和降雨模式改变。如果没有采取应对措施，情况可能会迅速恶化，对生物多样性、天然水库的补充以及森林功能构成威胁。其中一项研究表明，亚马逊地区的旱季正从四个月延长至六个月，期间降水量减少逾 150 毫米。第二项研究分析了 2023 年至 2024 年间亚马逊地区的干旱情况。研究结果显示，过火面积增加了 9%，森林退化预警增加了 19%，在干旱高峰期，多达 420 万公顷的森林受到火灾影响。结果表明，干旱、火灾和退化的循环在加剧，削弱了生态系统的恢复能力。亚马逊雨林的面积也可能会减少。

去年 10 月的一天，Dong Hui 突然决定试试能不能握笔写字。6 年前他因为车祸导致的脊髓损伤而颈部以下瘫痪。他缓慢而坚定的写下了自己的名字、谢谢和日期。他能做到这一切来自他参加的脑机接口芯片试验。2024 年 11 月 Dong Hui 成为中国首批接受脑部手术植入侵入式脑机接口芯片的患者之一。今年三月他使用的植入式脑机接口产品获得了商业使用批准。他植入的脑机接口设备被称为 NEO，由上海初创公司 Neuracle Technology 和清华大学合作研发。手术历时约 1.5 小时，收集脑电信号的传感器植入放置在他的硬脑膜上。植入物会将信号传输到计算机。计算机将信号翻译成指令，控制他每天 2.5 小时训练期间佩戴的软体机器人手套，帮助他学习抓握。手术后大约一周他开始康复训练，“训练的第九天，我的右手成功不用手套抓住了一个球，那真是个奇迹。”悉尼科技大学的脑机接口研究员 Avinash Singh 表示，NEO 迅速获得批准的原因之一是其侵入性相对较小，它的 8 个传感器放置在大脑保护膜之上，相比下马斯克（Elon Musk）所创办的 Neuralink 公司开发的 N1 脑机芯片直接穿透了大脑皮层。NEO 的出血、胶质瘢痕形成和长期信号衰减的风险较低。中国还着手将脑机接口列入医保，将其与量子技术、人形机器人等列为对中国未来科技竞争力至关重要的六大关键产业之一。信息科学家 Meicen Sun 表示，中国一大优势是患者乐于接受新技术。美国初创公司 Axoft 正与中国公司合作在中国对四名患者进行脑机接口测试，并计划扩大规模。

胰腺癌是最致命的癌症，大部分现有疗法的效果甚微。现在名为 daraxonrasib 的药物公布了 III 期临床试验结果，有 500 名胰腺癌已扩散的患者参与了试验，其中 248 名患者每日服用 daraxonrasib，其余 252 名接受化疗。结果显示，服药组的中位生存期为 13.2 个月，化疗组为 6.6 个月，也就是药物将患者的生存期延长了一倍，而且副作用更少。研究报告公布在芝加哥举行的美国临床肿瘤学会年会上，专家认为这种药物有望引领一场治疗革命。Daraxonrasib 的作用机制是靶向名为 Kras 的蛋白质，这种蛋白质驱动了几乎所有胰腺癌。药物通过粘合分子去捕获并抑制 Kras 蛋白，从而阻止肿瘤的生长。

由 Amazon、Cisco, Google、Intel、Microsoft、Mozilla 和 Netflix 等联合组建的开放媒体联盟 AOMedia 正式发布了 AV1 的后继者 AV2 编解码器。AV2 在 AV1 继续上提高了压缩效率，以更低的比特率实现高质量视频传输，为流媒体、广播和实时视频会议不断变化的需求进行了优化。AV2 增强了对 AR/VR 应用的支持，支持多节目分屏播放，改进屏幕内容处理，能在更宽的视觉质量范围内运行。

马来西亚新网络安全法规星期一（6 月 1 日）生效，要求各大社交媒体平台验证用户年龄，并禁止 16 岁以下儿童注册账户。这项新法规适用于在马来西亚拥有至少 800 万用户的社媒供应商，包括 Facebook、Instagram、TikTok、YouTube 等。该国通信监管机构表示将给予社媒平台一段宽限期实施这些措施，但未说明宽限期的截止日期。新《网络安全法》的相关规定包括新的《儿童保护法》和《风险缓解法》，并要求社媒平台“加强内容管理”。通信与多媒体委员会说，未能遵守这两项守则的公司可面临最高 1000 万令吉的罚款。

过去几年，玩家群体中反 DEI 和拥抱保守派价值观的声音在社媒上非常突出，他们究竟只是代表了少数人的声音但被社媒的算法放大，还是代表了大多数玩家？研究人员利用 MRI-Simmons 的数据分析了 2012 年、2016 年和2020 年这三个特定年份在美国进行的全国消费者调查，追踪了受访者过去十二个月是否玩过网络游戏或单机游戏，观察了游戏行为与价值观之间的相关性。结果显示，玩家群体相比美国普通民众总体上持有更包容性的价值观。研究人员认为对 DEI 等包容价值观的敌意来自少数活跃玩家。

根据卫星对地磁场的测量，太平洋一区域下的地球熔融核心在 2010 年突然逆转了流动方向，从西向流动转为东向流动。爱丁堡大学地球科学家 Frederik Dahl Madsen 说，“科学家现在想了解，这种逆转究竟代表着短暂的波动、周期性振荡的一部分，还是地核环流的一种新的稳定平衡。持续监测对确定未来几年这一流动如何演变至关重要。”Madsen 团队分析了 1997-2025 年间 27 年的卫星数据，拼凑出可能发生的变化。外地核大部分运动都受被称为偏心行星环流（eccentric planetary gyre）的环流模式支配。2010 年太平洋下方的区域，部分外核突然偏离了这种模式，从 2010 年之前的微弱西向流动转变为 2012 年之后的强劲东向流动。这种流动持续增强至 2020 年。根据最新的测量结果，它又开始减弱了。这一发现表明地球内部可能比我们想象的更动态多变。

流行图像编辑软件 Paint.net 的官方域名是 www.getpaint.net，因为域名 Paint.net 掌握在第三方手中。现在你可以直接通过 Paint.net 域名获取该软件了。过去 22 年 Paint.net 域名原所有者一直拒绝出售域名，除非项目开发者 Rick Brewster 支付巨额费用。但域名所有者犯下了一个严重错误，他们创建了一个模仿 Paint.net 项目下载页的网站，通过恶意链接和广告获利。Brewster 提起了诉讼，主张利用他人作品牟利构成了侵犯版权和域名抢注。他赢得了诉讼，没有花钱就拿回了 Paint.net 域名。Paint.net 未来将成为主站，GetPaint.net 将重定向到主站。

维基媒体基金会的员工正在组建工会，但本月有多名参与组织工会的员工离职或解雇，此事在社区引发了强烈反应，有人呼吁罢工，或者暂停将破坏性编辑恢复到正确版本的工作。维基媒体基金会证实它解散了负责 Community Wishlist 的团队，但否认此事与组建工会相关。基金会称，它的内部评估认为依靠单一团队处理社区请求不再运作良好。因为基金会支持的软件众多，接收社区请求的渠道众多，很难靠一个专门的团队去满足社区的所有愿望。在新架构下 Community Wishlist 请求的处理职责将由更大的产品和技术部门承担。受影响的员工目前仍在职，他们正在考虑安排其他内部岗位。未被安排到其他岗位的员工将于下个月离职，将获得遣散费。基金会称，如果员工最终投票决定成立工会，基金会将尊重法律程序。

2026年 5 月 30 日下午 5:58，美联航 UA236 航班波音 767-400ER 客机从纽瓦克自由国际机场起飞，飞往西班牙马略卡岛帕尔玛机场（Palma de Mallorca Airport）。在跨大西洋飞行约一个半小时后，原本平静的飞行却让机上乘客陷入了混乱。据乘客在社媒上分享的经历，乘务员突然通过广播发出紧急指令：所有乘客必须立即关闭蓝牙连接。机组人员多次发出语气越来越紧张的广播，声称该指令直接来自美联航位于芝加哥的总部。机组人员警告说，如果蓝牙信号不被关闭，飞机将被迫返航。尽管收到了警告，至少还有两台蓝牙设备处于开启状态。飞行员最终决定中止飞行。根据社媒上的消息，原因是一名 16 岁男孩将其个人蓝牙音箱的网络名称改为 BOMB，男孩据说是几年前改的。蓝牙信号会广播给附近任何试图配对的智能手机或笔记本电脑，因此该名称会立即出现在机舱内乘客和机组人员的屏幕上，触发标准的炸弹威胁应对流程。

微软于 2018 年 9 月 24 日宣布推出 Windows 和 Mac 版本的 Office 2019，售价 149.99 美元，可永久使用，但不会引入新功能。但到了 2026 年 5 月 15 日微软更新了支持文档，不再保证 Office 2019 能正常运行。Mac 版本的 Office 2019 的支持于 2023 年 10 月 10 日结束，微软使用数字证书去验证 Mac 版本的许可，该证书将于 2026 年 7 月 13 日到期。微软不打算更新证书，而是就让证书过期，而证书过期之后软件将无法正常使用，进入只读模式。微软向受影响用户提供了三种选择：继续以只读模式使用 Mac 版 Office 2019、切换到免费的 Microsoft 365 Web 应用，或者付费订阅 Microsoft 365 或购买新的 Office 家庭版 2024 永久许可证。微软此举招致了广泛批评，认为其做法涉嫌违法。Windows 版本未受影响。

大量证据表明，动物大脑会受到高温的影响。天气炎热时，鸟类学习能力下降，狗咬人的次数增多，羚羊等体型较大的动物更容易挑衅打架。西澳大利亚大学的行为生态学家 Amanda Ridley 说，如果动物无法保持足够的警觉去寻找食物或躲避天敌，它们的生存几率会急剧下降。随着气候变化导致热浪日益频繁，动物王国的认知障碍可能会波及整个生态系统，本已脆弱的物种会面临更大的风险。如果授粉昆虫忘记该拜访哪些花朵，农作物和野生植物可能会歉收。如果鸟类难以觅食，其幼鸟可能无法存活。在一个气候暖化的行星上，敏锐的思维尤为重要。Ridley 指出气候变化意味着适应能力变得更重要。高温影响人类的大脑，有研究发现，对于在无空调学校学习的学生，学年气温每升高华氏 1 度，考试成绩会下降 1 %。对美国近 7 万起狗咬人报告的分析发现，32 摄氏度的天气狗咬人的风险比 16 摄氏度的天气高 10%，但研究人员并不确定是天热的条件下狗变得更具有攻击性，还是人类更暴躁而容易引发攻击，很可能是两个因素的组合。中国的一项研究发现，蛇和猫在天气变热时也更可能咬人。

全世界有数千万人服用 GLP-1 减肥药如 Ozempic。一个研究团队对 13 名服用 GLP-1 药物的年轻女性进行脑部扫描，发现她们的大脑发生了深远的变化。与注意力相关的突显网络(salience network)脑连接数量成倍增加。研究人员对此感到意外，他们表示不知道这意味着什么。GLP-1 药物的作用机制类似控制饥饿感、血糖和体重的激素。研究人员对药物作用机制深入研究后发现，它还会重塑部分大脑。致力于将 GLP-1 药物用于治疗成瘾的科学家 Lorenzo Leggio 表示其作用机制尚未完全被理解。这就引发了一个疑问：如果 GLP-1 药物能改变大脑中与奖赏、渴望和动机相关的系统，那么抑制一个人的破坏性冲动和重塑其人格之间存在怎样的界限？

丹麦养老基金 AkademikerPension 今年一月以美国政府的信用评级不高为由抛售美国国债，现在它以治理结构问题而将 SpaceX 列入投资黑名单。SpaceX 于 5 月 20 日提交了 IPO 申请，其目标估值高达 1.8 万亿美元。AkademikerPension 首席投资官 Anders Schelde 表示这一估值不仅严重过高，而且该公司还存在在灾难性的治理结构问题。Elon Musk 拥有该公司绝对的控制权，控制约 80% 的投票权，同时兼任 CEO、CTO 和董事会主席。美国多家养老基金也都对 SpaceX 的治理结构表示担忧。Schelde 认为 SpaceX 的合理估值在一万亿美元以内，从投资回报角度看，该养老基金无法证明参与此次 IPO 的合理性。Schelde 表示，如果不是因为 Space X的估值和治理风险，AkademikerPension 很想投资 SpaceX 及其技术，“我们不投资的决定并非反映其技术或工程能力的不足。”