Weekly Digest — 2026-W32

Search every AI Agent & IDE Conversation

Create interactive 3D and XR experiences without coding

Cloud canvas where your team and AI agents works together

Animated SVG mascot studios for apps that need a personality

Make existing Codex and Claude Code sessions multiplayer

Wild Pokémon appear while you wait for Claude Code

The MCP server for marketing data

The AI SDR that runs outbound for you

Japanese for the life you're actually living in Japan

Turn your Mac into a spinning vinyl player.

A terminal built for Claude Code w/ one-click session resume

Your voice-first AI assistant for macOS

Reinforcement Learning with Verifiable Rewards (RLVR) has driven recent progress in reasoning-oriented large language models (LLMs) by enabling large-scale optimization. However, its applicability remains largely limited to domains such as mathematics and coding, where correctness can be deterministically verified. Open-ended tasks instead often rely on human preferences, reward models, or LLM-based judges, introducing evaluation bias, judge capability bottlenecks, and additional inference costs.Drawing on the principle of self-supervised learning, which constructs pretext tasks to derive supervision from the data itself, we propose Reinforcement Learning with Self-Verifiable Rewards (RLSVR), a task-transformation-based training paradigm for extending RLVR to open-ended tasks. RLSVR transforms open-ended tasks into verifiable proxy environments whose internal rules and interaction outcomes automatically generate reward signals. We instantiate RLSVR with SpyRL, a multi-agent self-play environment inspired by Who Is the Spy?. Agents receive asymmetric information, complete the same target task, and vote to identify a designated spy. Because the spy identity is predetermined, voting outcomes provide fully verifiable rewards, while successful identification remains closely related to output quality. Experiments on text summarization, creative writing, and mathematical reasoning show that SpyRL outperforms existing self-improvement methods on non-verifiable tasks and yields consistent gains on verifiable reasoning tasks. These results demonstrate that task transformation can extend scalable RLVR-based self-improvement beyond inherently verifiable domains. Models and code have been released at https://github.com/wangqinsi1/SpyRL.

World models enable a predictive substrate for planning and action, yet existing formulations merely answer a physical question: what/where it is, and how will it evolve. Human behavior, however, is driven by hidden mental state (what a person believes, wants, intends, feels, and considers socially permissible), so a model that tracks the physical scene but not what each agent knows and believes about it predicts the wrong action for the right-looking scene. We formulate Mental World Modeling (MWM), a generic theoretical framework that makes mental variables core components of a world model rather than posthoc rationales: MWM aintains a coupled physical-mental world state, renders a target-specific partial observation, and simulates how candidate actions jointly update both components. We instantiate the framework in MENTIS, a training-free and fully inspectable baseline that decomposes the process into state parsing, target-observation generation, action decomposition, coupled physical and mental transition, and branch-level value evaluation. On a manually constructed, quality-controlled dataset of situated decision scenarios spanning text, image, and sounding-video stories, experiments with 8 modern LLM-based world models demonstrate that explicitly modeling the mental state is essential for predicting human decisions. Deeper analyses further expose the bottlenecks of current mental world modeling. We expect MWM as a next stage of world modeling, from simulating physical scenes to simulating the minds that act in them.

We present N_0-VTLA, a vision-tactile-language-action (VTLA) foundation model capable of (1) fine-grained contact-rich manipulation with tactile perception and tactile-feedback control, and (2) offline policy improvement from stored deployment data. Building on current vision-based backbones, we propose a training recipe for tactile integration consisting of visuo-tactile pre-training, staged tactile-pathway integration, and advantage-conditioned offline policy improvement. During pre-training, the policy learns broad contact priors from NeoData, our large-scale visuo-tactile robot dataset; to our knowledge, N_0-VTLA is the first VTLA model pretrained on tactile data at scale. During post-training, we augment the policy with a predictive tactile pathway that distills the contact patterns learned at scale into the fine motion adjustments required by downstream tactile-centric manipulation. For offline policy improvement, we introduce ALTER, an advantage-conditioned offline reinforcement learning method that converts relative progress and trajectory-event comparisons into binary advantage labels for policy training on a fixed deployment corpus, further improving task-specific learning on contact-rich skills such as deformable object manipulation. Across contact-rich benchmarks, N_0-VTLA outperforms strong baselines by wide margins: it wins all nine real-robot NeoReal tasks and reaches 63.8% mean success on a twenty-task simulation suite, against 44.0% for the strongest baseline. N_0-VTLA policies trained with ALTER reach 75-95% success on three long-horizon real-robot tasks. These results lay a foundation for versatile tactile-driven manipulation policies.

Polygonal meshes are the standard surface representation of modern 3D pipelines, and generating high-quality meshes with artist-style topology is essential for film, gaming, and interactive 3D applications. Mainstream approaches serialize a mesh into a token sequence and decode it autoregressively, which is slow at inference and sensitive to error accumulation, making them impractical for interactive asset creation. We present Meshy T2, a fast native mesh generation framework built on flow matching. At its core is a vertex-set mesh VAE that encodes a mesh into one continuous latent token per vertex and decodes vertices, edge connectivity, and face winding order in a single pass, preserving high-precision geometry and artist-authored topology without vertex quantization or welding. Generation proceeds as a coarse-to-fine cascade of two flow-matching models: an image-conditioned voxel flow first sketches the overall shape as a coarse occupancy scaffold, and a mesh flow then populates the scaffold with per-vertex latent tokens, conditioned on the image, the scaffold, and a requested vertex budget. This design delivers three practical capabilities: interactive generation speed through parallel flow-based synthesis; effective face-count control through the requested vertex budget; and native support for multi-part assets, whose components emerge directly from the generated connectivity. In our experiments, Meshy T2 achieves state-of-the-art geometric fidelity and completes end-to-end image-to-mesh generation within a median of 6 seconds, over an order of magnitude faster than autoregressive baselines. Code and weights will be available at https://github.com/meshy-dev/meshy-t2.

System prompts are instructions configured by developers to govern the behaviors of foundation models in AI applications. They are used throughout commercial AI products, but are rarely disclosed to the public or regulators, creating a serious trust and accountability gap in the wide deployment of AI systems. In this paper, we introduce Artificial Intelligence System Prompt Assurance (AISPA), a user-centric framework for systematically auditing system prompts in AI systems. AISPA examines specific parts of a system prompt and evaluates them along eight dimensions that matter to users. We then use this framework to review 3,249 instructions from system prompts in 88 commercial AI products, classifying each instruction as either protective (of users) or problematic. Our audit surfaces four core findings. First, system prompt design varies substantially across products and developers, with some organizations averaging over 60 protective instructions per product while others average fewer than 5. Second, protective instructions are widely adopted but shallow in scope: 98.9% of products contain at least one, yet only 24% cover all eight dimensions of the AISPA taxonomy. Third, system prompts have grown steadily longer and more protective of users, suggesting that user protection is becoming a more visible concern in commercial prompt design. Fourth, despite this progress, problematic instructions remain pervasive: roughly 40% of products contain at least one instruction that works against user interests, and protective and problematic instructions frequently coexist within the same prompt. Our findings highlight the need for greater transparency, standardization, and independent oversight for system prompts in commercial AI products.

We present N_0-TWAM, a tactile-native world-action model for contact-rich manipulation that predicts both future vision and future contact. To our knowledge, it is the first tactile world-action model trained at large scale, and it shows strong capability on contact-rich tasks. We pre-train N_0-TWAM at large scale with visuo-tactile joint training over tactile-rich demonstrations spanning six embodiments and 450 tasks. We use NeoForce, a unified force-based tactile representation, to form a physically grounded contact signal that conditions action generation. To improve long-horizon and multi-stage manipulation, we introduce tactile contact events for task staging and advance through them during execution. For real-time efficiency, we adopt an asymmetric Mixture-of-Transformers architecture that pairs a full-width expert for video prediction with slim experts for downstream action and tactile prediction. Evaluations on both real and simulated benchmarks justify the capabilities of N_0-TWAM across a range of contact-rich tasks, and demonstrate the benefit of data scaling for precise tactile and action prediction. In summary, N_0-TWAM endows a world-action model with predictive capabilities to foresee vision, touch and action, building a solid foundation for fine-grained manipulation on open contact-rich tasks. The codebase and model checkpoints will be made publicly available to foster further research and development in tactile-enabled robotic manipulation.

Speech and audio generation is often needed in animation dubbing, audio drama, movies, advertising, games, podcasts, and short-video production. In these scenarios, creators may need to design voices without reference recordings, control speaker styles with natural language, support acoustic scenes with environments and audio effects, and later reuse the designed voices. Therefore, it is important to support multi-speaker speech and audio generation for both instruct and zero-shot tasks. The instruct task requires a caption of the environment, speaker styles, and fine-grained content, while the zero-shot task uses reference audio together with the same fine-grained content. We address these tasks from both the data and model sides. First, we propose SwanData-Caption, which cleans raw speech and audio data, adds targeted synthetic coverage, and annotates diverse and accurate multi-level captions. Then, we propose SwanTale, a multi-speaker expressive speech and audio generation model that supports both zero-shot and instruct tasks. We introduce SwanVAE to support high-quality multi-audio-modality generation. Then, we adopt reward-conditioned quality control and Engram conditioning, along with Unified MoE for multi-task and multi-audio-modality modeling. In addition, we use curriculum learning and GRPO post-training to let the model progressively learn and strengthen its capabilities. Experimental results show that SwanTale leads on multiple key zero-shot and instruct metrics, achieves the best expressiveness scores in both tasks, and supports complex instruct generation involving multi-speaker speech and audio. Demos can be found at https://swanaigc.github.io/\#swantale.

Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, existing agent harnesses maintain task execution, task state, and completion assessment within a growing context, making the state difficult to track and allowing incorrect self-assessments to propagate into later decisions. We reformulate long-horizon execution as a task-state management problem and propose LongHorizon-Harness, which maintains the task state explicitly outside execution and updates it only with facts independently verified from the environment. Its Manage-Execute-Audit(MEA) loop uses a manager to maintain the task state and determine the next subtask, a fresh-context executor to perform it, and a read-only auditor to verify the resulting environment state before the next round. A lightweight AgentAdapter supports interchangeable model and harness backends without modifying their native agent loops. LongHorizon-Harness improves Qwen~3.7-Plus from 51.8% to 80.7% on WeaveBench, from 69.7% to 77.2% on Terminal-Bench~2.1, and from 2.8% to 8.3% on OSWorld~2.0. It also raises Claude Opus~4.7 from 20.0% to 34.3% on an OSWorld2.0 subset, demonstrating consistent gains across models, harnesses, and interaction domains.

On-policy (self) distillation (OPSD) is increasingly adopted for language-model post-training. It strengthens the teacher with privileged information but can induce a privilege illusion: the student learns privilege-dependent behavior it cannot reproduce from its inference-time context, yet behaves as if the training-time privileged information remained available, ultimately degrading performance. In this paper, we identify information asymmetry between the privileged teacher and the student at inference as the root cause of this failure in OPSD. To resolve this asymmetry, we propose Dual-Anchored Policy Distillation (DAPD), a unified framework with two levels of anchoring. Dual-Path Anchoring (DPA) introduces a self-conditioned bridge and aligns reference and rollout behavior along two matched-information paths, preventing privilege-dependent behavior from being transferred to the inference-time student. Dual-Source Anchoring (DSA) applies these paths in both reference-to-rollout and rollout-to-reference directions, reducing reliance on privileged reference guidance while preserving correctness supervision. Extensive experiments show that DAPD significantly alleviates privilege illusion, outperforming OPSD on Qwen3-4B by +2.00 points on average across tasks. Notably, its gains persist across scales, reaching +2.69 at 4B and +2.78 at 32B.

Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. In contrast, learning-based approaches offer a more unified way to model skill generation across heterogeneous sources. However, learning-based skill generation remains challenging because skills lack a natural supervision signal based on relevance or correctness; their value can largely be determined only by whether they improve the behavior of the agent on downstream tasks. To address this challenge, we propose Skill-α, a reinforcement learning method for progressively generating high-quality agent skills. Specifically, we formulate skill generation as a sequential editing process that decomposes skill construction into individually evaluable edits, and introduce a novel rollback reward that evaluates each edit by comparing downstream execution under the original and edited skills on an anchored query. Extensive experiments show that Skill-α generates more effective skills than methods based on heuristics or pipelines in both document-to-skill and experience-to-skill settings. Under the main GPT-4o worker, Skill-α improves average downstream success rates over the strongest skill-generation baseline by 3.3 points on CL-Bench and 6.7 points on tau2-bench. Further ablations validate the importance of rollback reward and progressive generation.

Multimodal on-policy distillation (OPD) transfers fine-grained visual knowledge by supervising student-generated trajectories with a privileged-view teacher. Yet its next-token corrections are source-mixed, combining visual signals with linguistic priors and teacher-specific effects. The key challenge is to estimate which corrections are supported by visual evidence, not merely where or how strongly to distill. We introduce Visual Attribution Distillation (VAD), a counterfactual target-reconstruction algorithm that estimates the visually attributable part of a teacher correction. At each student-generated prefix, VAD evaluates the same fixed teacher with the relevant evidence present and removed. The corresponding change in centered log-probabilities defines ut, a signed proxy for the visual evidence direction that estimates how revealing the evidence supports or refutes candidate tokens. VAD projects the original correction onto this proxy to obtain an intervention-aligned component and a proxy-unexplained residual, then reconstructs a student-anchored target from the former. During training, this reconstructed target supplies the primary supervision signal, while the privileged teacher contributes a weak regularizer. Across six fine-grained visual benchmarks at 4B and 9B scales, VAD outperforms direct privileged-view distillation and visual-advantage weighting. Token- level and controlled-target analyses show that the proxy-aligned component is enriched in task-relevant visual corrections and yields stronger target shifts, especially when evidence refutes a mistaken answer. These results support counterfactual target reconstruction as an effective alternative to source-mixed supervision.

Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retrieval (LSR) to push beyond exact lexical matching toward richer semantics. Yet LSR has so far remained tied to encoder-style bidirectional architectures, and its extension to multimodal settings still relies heavily on auxiliary cross-modal modules. To address these limitations, we introduce UEmbed (Unified Embedding), a decoder-only multimodal embedding model that produces both sparse lexical and dense representations in one causal forward pass. UEmbed appends N learnable special tokens to the input and partitions the vocabulary into N disjoint subsets. Each token's causal hidden state predicts sparse weights over its assigned subset, and the N subsets are concatenated into the full sparse vector. Trained on public data, we release UEmbed at 2B, 4B, and 9B scales. UEmbed-9B reaches 71.8 (dense) and 71.0 (sparse) on MMEB-v2, outperforming multimodal embedding models trained on publicly available data (e.g., RzenEmbed). On BEIR, UEmbed also remains competitive with strong dense and sparse baselines. Furthermore, we demonstrate the practical utility of UEmbed across three dimensions: effectiveness, efficiency, and agentic applications. Overall, UEmbed offers a new paradigm: it unifies dense and sparse embeddings in one model, while further extending sparse retrieval to unify text and multimodal inputs.

Jeran Wittenstein / Bloomberg : Amazon becomes the fifth company to reach a $3T market cap, joining Nvidia, Alphabet, Microsoft, and Apple; its stock closed up 4.58% on Monday —  Amazon.com Inc. surpassed $3 trillion in market value for the first time, becoming only the fifth company to ever reach the milestone.

Jonathan Vanian / CNBC : Snap reports Q2 revenue up 19% YoY to $1.6B, vs. $1.54B est., DAUs up 5% to 493M, vs. 487M est., forecasts Q3 revenue above est.; SNAP jumps 10%+ after hours —  Snap reported better-than-expected revenue and earnings for the second quarter and issued a forecast for the current period that topped analysts' estimates.

Samantha Subin / CNBC : Palantir reports Q2 revenue up 93% YoY to $1.94B, vs. $1.8B est., US commercial revenue up 149%, and raises FY 2026 revenue guidance; PLTR jumps 9%+ after hours —  Palantir topped second-quarter earnings estimates on Monday and said commercial revenue more than doubled from a year ago.

Zack Whittaker / TechCrunch : Samsung says it's restricting app registrations and removing apps with SDKs that can turn smart TVs into residential proxy exit nodes, after a similar ban by LG —  Several popular Samsung smart TV apps contain code that share the owner's internet connection with strangers …

Devlin Barrett / New York Times : Court docs: an FBI agent has been charged with amassing ~$1M worth of cryptocurrency, largely by making unauthorized withdrawals from a criminal target overseas —  The agent confessed to making unauthorized withdrawals from a criminal target overseas, according to court papers.

Michael Kan / PCMag : US satellite internet provider Hughesnet files for Chapter 11 after losing subscribers to Starlink and plans to focus on business and government customers —  US satellite internet provider Hughesnet has filed for Chapter 11 bankruptcy after running low on cash and losing subscribers to Starlink.

Samantha Kelly / Bloomberg : Match reports Q2 revenue down 1% YoY to $853M, vs. $856.6M est., 13.3M paying users, vs. 13.4M est., forecasts Q3 revenue below est.; MTCH drops 8%+ after hours —  Match Group Inc. gave a revenue forecast for the current quarter that narrowly missed analysts' estimates …

Sam Sabin / Axios : The UK AISI says it observed a total of 19 instances where Mythos and GPT-5.6 Sol tried to hack people and companies during a routine cyber evaluation in July —  The U.K. AI Security Institute said it observed nearly 20 instances of Anthropic and OpenAI's most advanced models trying …

Maria Curi / Axios : Sources: the US' AI framework excludes open models and defines a covered frontier model as closed source with SOTA capabilities and national security risks —  The White House is excluding open models from its framework to test advanced AI capabilities, sources familiar with the matter told Axios.

Micah Maidenberg / Wall Street Journal : SpaceX says its Q2 capex was $18.4B, up from $2.8B a year ago, including $15.8B for AI —  SpaceX laid out $18.4 billion in capital investments during the April-to-June quarter, with most of the spending supporting artificial-intelligence efforts.  —  In its earnings report Tuesday …

Nathan Bomey / Axios : SpaceX reports Q2 revenue of $4.29B from its connectivity division, which includes Starlink, $100B of cash and marketable securities, and a $47.5B order backlog —  SpaceX exceeded revenue expectations and reduced its losses in its initial quarter as a publicly traded company …

Jonathan Vanian / CNBC : Pinterest reports Q2 revenue up 18% YoY to $1.18B, vs. $1.15B est., MAUs up 11% to 640M, forecasts Q3 revenue in line with estimates; PINS drops 8%+ after hours —  Pinterest shares fell 7% in extended trading on Tuesday after the company reported better-than-expected earnings and revenue but issued lukewarm sales guidance.

微软 Windows 和设备执行副总裁 Pavan Davuluri 通过官方博客谈论了该公司在 Windows 11 上持续展开的改进工作，虽然大部分工作尚未推送给用户。他透露接下来的一项工作是优化 Windows 11 在 8GB 内存以上配置下的运行效率。由于目前的内存供应危机，PC 厂商又开始提供 8GB 内存配置的电脑，然而 Windows 11 在 8GB 内存下只能勉强运行，运行其它程序面临很大的局限性。Davuluri 表示该公司将减少 Windows 的内存占用，在用户日常使用的 PC 上提供快速响应的 Windows 体验。

塑料颗粒显然已经遍及地球的各个角落。根据发表在《Water Research》期刊上的一项研究，韩国研究人员检查了生活在水下 2000 米处深海热泉附近的蜗牛和贻贝，12 只受检动物 92%（11 只）体内都发现了微小的塑料碎片。海洋生物学家 Se-Joo Kim 说，深海热泉曾被认为是地球上最与世隔绝的环境之一。每只深海热泉动物体内平均发现了 3.42 个塑料碎片，最常见的是聚苯乙烯。研究人员还发现，摄食行为会影响微塑料在动物体内的积累位置。海床进食的蜗牛，塑料聚集在消化器官中；滤食性贻贝的塑料分布则更为均匀。

澳大利亚联邦银行、微软、 Uber 和凯悦酒店等公司正用自动化聊天和电话系统去处理以前需要人工完成的工作。AI 系统开始大幅削减客服人数。呼叫中心行业在美国、印度和菲律宾等地雇佣了数百万从业者。随着 AI 技术的进步以及高管面临拥抱新技术的压力，企业日益用生成式 AI 工具取代客服。美国的客服务人数正在下降，未来这一趋势会继续下去。分析师估计到 2030 年近半客服岗位将受到影响。西方公司此前将客服外包到熟悉英语的菲律宾、印度等国，随着 AI 的普及，这些国家的客服人数预计会大量减少。澳大利亚最大银行——联邦银行（Commonwealth Bank of Australia）裁减了数百名客服，每年预计节省数千万美元。微软将其客服团队（其中包括合同工和全职员工）从约 5 万人缩减至 4 万人。微软销售和服务运营负责人 Judson Althoff 在 4 月曾表示，AI 每年为公司节省约 7.5 亿美元的客服成本。他表示，更复杂的问题仍需要人工支持，但公司正不断扩展自动化修复的范围。凯悦酒店去年裁掉了美洲地区三成的内部客服。Uber 裁减了 10% 的客服岗位以“拥抱 AI”。

澳大利亚政府为 16 岁以下儿童的社交媒体禁令辩护，称禁令重塑了全国的舆论，极大改变了与家长之间的对话，关闭了数百万​​个儿童账户。政府从未指望禁令能得到 100% 的遵守，就像最低饮酒年龄法无法做到 100% 遵守一样，但法律的存在仍然是恰当的。此前的研究发现，禁令生效三个月后，逾八成澳大利亚青少年仍在继续使用社交媒体，10-15 岁儿童在 3 月使用社交媒体的频率与 12 月 10 日禁令生效前一样高。

在 Linux 游戏社区多年请求之后，GOG Galaxy 游戏启动器将原生支持 Linux。GOG 有游戏的 Linux 版本可以下载，但只能通过网站下载，GOG Galaxy 客户端只支持 Windows 和 macOS 操作系统。GOG 没有公布支持 Linux 的时间，只是表示正在开发中。GOG Galaxy 类似 Steam 客户端，支持自动游戏安装和更新、云端存档同步、成就追踪、游戏时长统计、游戏库管理、商店浏览、 好友列表和社交功能等等。在 Valve 的 Proton 推动下，Linux 对游戏的支持日益改进，越来越多的游戏公司也认识到了 Linux 的重要性。

加州居民从 8 月 1 日起可要求数据经纪商删除个人数据。Delete Request and Opt-Out Platform (DROP) 允许加州居民通过一次性选择加入的方式，从数百个数据处理中心删除个人信息。此前用户要删除个人信息需要联系每个数据处理商。数据处理商需要在 90 天内删除用户的数据，之后每 45 天删除一次。加州是美国第一个创建易于使用的数据删除系统的州，

主要 PC 制造商惠普、华硕和宏碁开始少量使用长鑫的内存芯片。多家大型 PC 制造商已于今年年中完成了长鑫 DRAM 芯片的认证流程，开始在笔记本电脑中少量使用。由于长鑫优先向华为等国内客户供应内存芯片，因此其它厂商的供应量有限，相关笔记本电脑型号主要销往美国以外市场。PC 厂商对使用长鑫内存十分谨慎，因为他们担心会惹恼三大内存芯片制造商美光、三星和 SK海力士，这三大公司占据了逾九成的内存芯片市场。长鑫的内存并不比美光或三星等公司便宜，厂商也无法采购更多内存。

30 米望远镜（Thirty Meter Telescope，TMT）项目于 2014 年开始建造，计划 2027 年投入运行。望远镜选址定在夏威夷的 Mauna Kea 山，而 Mauna Kea 被当地原居民视为圣地，由于原居民的反对望远镜项目从 2015 年起处于停工状态，至今已超过 10 年。现在西班牙正试图在该国的加那利群岛建造 30 米望远镜，它提出了 11.4 亿美元的方案用于建造和未来的运营费用。

开源多媒体库 FFmpeg 9.0 "Lei" 释出。新特性包括：Vulkan APV 视频解码和 Apple ProRes RAW Vulkan 加速、Vulkan v360 视频滤镜、HE-AAC 960 解码、NVIDIA CUDA 转置滤镜、动画 WebP 解码和解复用（demuxing）、AMD AMF 增强、AVX-512 优化等。其它包括 扩展 AMF 色彩转换器 (vf_vpp_amf) 的 HDR 功能、MP4 复用器支持 LCEVC 音轨复用，等等。

一项研究发现，11-12 岁设立第一个社媒账户的学生，其数学和阅读测试成绩低于几年后才设立账户的学生。16 岁时两组学生的成绩差距相当于六个月的学习差距。研究人员分析了逾 5000 名意大利中小学生的社媒使用习惯调查，及其在标准化数学、意大利语和英语考试中的成绩。13-14 岁时接受测试的学生中在 11 或 12 岁就设立社媒账户的学生，成绩低于至少等到 14 岁才开始使用社媒的学生。15-16 岁时意大利语的成绩差距依然存在，数学成绩差距则进一步扩大。英语成绩没有受到影响，研究人员认为是社媒上的英语内容较多，为学生们提供了偶尔学习的机会。研究人员认为，频繁查看社媒对学生而言是一大干扰因素。

云南玉溪的研究人员在玉溪红塔区的一个洞穴内发现了一种新的蜗牛，以《山海经》中的山神名字命名为帝江或 Erhaia dijiang。Erhaia dijiang 完美适应了终年无光的洞穴环境，是一种典型的洞穴专生物。它在长期演化过程中完全失去了眼睛，眼部结构已彻底退化。其贝壳小巧呈圆锥形，高度透明，身体柔软且没有色素，触角细长。实验室观察发现，该物种极度畏光，一旦受到光照便会迅速钻入岩石缝隙中躲避。Erhaia dijiang 生活在海拔2062米的洞穴中，刷新了洱海螺科所有已知洞穴物种的海拔分布纪录，证明洞穴淡水螺能够在海拔超过 2000 米的喀斯特洞穴中生存。该物种栖息于水深不足 1 米的洞穴水体底部砾石和碎石之间，洞内水温常年稳定在约 20℃。雨季水流湍急时，偶尔会有少量个体被冲出洞口，但它们从未在附近地表溪流中被发现，活动范围始终局限于距洞口约两米以内。在实验室条件下，它们主要取食岩石表面的硅藻，也能够摄食人工投喂的鱼饲料。

越来越多的动物研究表明，限制蛋白质摄入量或许能够延缓衰老、延长寿命。那么，这是否意味着摄入过多蛋白质存在危害呢？威斯康星大学麦迪逊分校的 Dudley Lamming 表示：“我们通常认为膳食蛋白质是有益的。”但他指出，最新的证据显示，“对于久坐不动的人或动物来说，如果摄入的膳食蛋白质超过所需的量，会引发代谢问题，还可能缩短寿命”。也有学者指出，在小鼠、大鼠等动物身上开展的营养学研究并不适用于人类，而且高蛋白摄入量与人体的益处相关。Lamming 认为，综合 300 项研究，当下流行的增加蛋白质摄入的做法，对经常运动的人而言或许并无不妥，因为他们需要更多蛋白质来修复肌肉损伤，然而摄入过多蛋白质可能对久坐不动的人造成伤害。