推测式解码是对大型语言模型的一种强力优化，用以克服标准自回归解码的效率瓶颈。基线方法在循环中每次只生成并确认一个 token，而推测式解码将流程拆成两个阶段：先由一个轻量级草稿模块提出一串候选 token，随后目标模型在一次前向中验证这些候选项。如果目标模型接受草稿的 token，系统即可一次性提交多个输出，从而显著提升吞吐量。

文章介绍了 vLLM 当前支持的五种具体草稿方法：native Multi-Token Prediction (MTP) 、 Gemma 4 MTP 、 EAGLE-3 、 DFlash 和 DSpark 。这些方法主要在于如何利用目标模型的信息以及如何生成候选 token 存在差异。例如，native MTP 使用模型本身的辅助路径，而 DFlash 和 DSpark 则采用专门的、以目标为条件的网络并行预测整块未来 token 。相比之下，EAGLE-3 依赖一种自回归机制，结合了目标 Transformer 不同阶段的隐层状态。

在 AMD Instinct™ MI300X 和 MI355X GPU 上的实验表明，推测式解码的效果高度依赖具体的模型家族、草稿检查点和目标工作负载。某些配置的吞吐量提升超过 2 倍，但并非所有数据集和模型都能获得一致的性能提高。推测 token 的数量（即 proposal length）是关键调参项：随着草稿 token 数量增加，吞吐量通常会上升，但到达某一点后会趋于平缓甚至下降，因为额外的草稿开销开始抵消验证成功带来的收益。

在实际落地时，推测式解码需要细致的观测和调优。文章建议监控吞吐量、平均被接受长度和各位置的接受率等指标，以确定最优配置。由于不同负载的 token 可预测性差异显著，通常没有一刀切的设置。推荐的流程是从已知配置出发，针对不同的 proposal length 进行扫描，找到最适合当前任务的平衡点。

最后，为新的目标模型训练定制的 speculator 是进一步提升性能的可行途径。该过程包括收集目标模型的代表性隐层状态来训练草稿组件，确保投机器与目标的内部表征对齐。通过让草稿模型的架构和训练数据与目标领域（例如数学或代码生成）相匹配，用户可以获得更高的接受率，从而在生产环境中实现更高的服务吞吐量。 Speculative decoding serves as a powerful optimization for large language models, addressing the efficiency limitations of standard autoregressive decoding. While the baseline approach generates and commits one token at a time in a repetitive loop, speculative decoding separates the process into two distinct phases. A lightweight draft component first proposes a sequence of candidate tokens, and the original target model then verifies these candidates in a single pass. If the target model accepts the draft tokens, the system commits multiple outputs simultaneously, significantly increasing throughput.

The article details five specific drafting methods currently supported in vLLM: native Multi-Token Prediction (MTP), Gemma 4 MTP, EAGLE-3, DFlash, and DSpark. These approaches differ primarily in how they leverage information from the target model and how they generate candidate tokens. For instance, native MTP uses a model-native auxiliary path, while DFlash and DSpark utilize dedicated, target-conditioned networks to predict entire blocks of future tokens in parallel. EAGLE-3, by contrast, relies on an autoregressive mechanism that incorporates hidden states from various stages of the target Transformer.

Experimental results on AMD Instinct™ MI300X and MI355X GPUs demonstrate that the impact of speculative decoding is highly dependent on the specific model family, draft checkpoint, and target workload. While some configurations showed throughput gains exceeding 2x, performance improvements were not uniform across all datasets and models. The number of speculative tokens, or the proposal length, proved to be a critical tuning variable. Throughput generally improved as more tokens were drafted, up to a point, after which it hit a plateau or declined because the overhead of additional drafting work began to outweigh the benefits of successful verification.

Practical implementation of speculative decoding requires careful observability and tuning. The article suggests that users monitor signals such as throughput, mean accepted length, and per-position acceptance rates to identify the optimal configuration. Because different workloads exhibit different token predictability, a one-size-fits-all setting is rarely sufficient. A recommended workflow involves starting from a known configuration and performing a sweep of different proposal lengths to find the balance that best suits the specific task at hand.

Finally, training a custom speculator for a new target model is a viable path for further performance gains. This process involves collecting representative hidden states from the target model to train the draft component, ensuring that the speculator is well-aligned with the target's internal representations. By matching the draft model's architecture and training data to the target's expected domain, such as mathematics or code generation, users can achieve better acceptance rates and, consequently, higher serving throughput in their production environments.

本界面为 Mellet 地区的乘客提供实时交通信息，重点显示即将发车的交通选项，并提示 Mellet Pont-à-Migneloux 站距当前位置仅约三分钟步行。该平台作为独立应用运行，明确声明不隶属于任何政府机构或官方交通运营商。

界面还列出附近的其他站点，如 Chaussée de Wayaux 、 Villa Stassart 、 Lotissement 和 Rue du Cimetière，旨在帮助通勤者找到步行可达的便捷乘车地点。

为保证信息透明，平台引用了多个官方数据来源，包括 De Lijn 、 NMBS/SNCB 、 STIB 和 LETEC 。所展示的内容均来自这些机构提供的开放数据集，并标注了具体归属日期，以说明时刻表信息的时效性。 The provided text serves as a real-time transit information interface for users in the Mellet area. It highlights upcoming departure options, specifically identifying the Mellet Pont-à-Migneloux stop as being a short three-minute walk away. The platform functions as an independent application, clarifying that it is not affiliated with any government body or official transit operator.

In addition to the primary stop, the interface lists several other nearby locations, including stops at Chaussée de Wayaux, Villa Stassart, Lotissement, and Rue du Cimetière. These options are presented to assist commuters in identifying convenient transit points within walking distance of their current location.

To ensure transparency regarding the data provided, the platform cites several official sources, including De Lijn, NMBS/SNCB, STIB, and LETEC. The information presented is derived from the open data sets provided by these agencies, with specific attribution dates noted to signify the currency of the transit schedules being displayed to the user.

Gamers Nexus 最近与 Level1Techs 和独立安全研究人员合作进行的一项调查揭示了嵌入在 LG 智能电视中的令人担忧的数据收集行为。对包括 OLED G5 在内的零售机型的测试表明，这些电视会主动扫描本地网络以映射其他联网设备。通过使用 Wireshark 捕获网络数据包，研究人员发现电视会识别附近的设备（如智能手机和智能手表），并记录邻近 Wi‑Fi 网络的名称、信号强度以及位置信息。

这种大规模的数据收集似乎整合到了 LG Ad Solutions（公司的定向广告部门）。虽然 Automated Content Recognition（ACR）作为对屏幕音视频进行取样以生成数字指纹的技术早已为人所知，但这项新研究表明其监控范围远比此前认知的更广。该广告部门声称能接触到数以亿计的可寻址设备，这主要通过追踪与电视处于同一家庭网络的其它硬件实现。

更令人不安的是，调查发现这些电视即便在屏幕处于待机状态时仍会持续采集音频。在台架测试中，研究人员观察到电视在看似不活动时依然录得清晰的麦克风音频。如果电视与互联网断开，它会将语音输入保存在本地，待网络重连后再将这些文件上传到远程服务器。

除此之外，研究团队还在 webOS 中发现了多个远程代码执行漏洞，目前这些问题正通过负责任的漏洞披露程序处理。鉴于内置网络扫描和数据收集的激进性，研究人员建议消费者考虑完全断开 LG 智能电视的互联网连接，并改用外接且更安全的流媒体设备。 LG 尚未就此事发表评论。 A recent investigation by Gamers Nexus, conducted in collaboration with Level1Techs and independent security researchers, has revealed concerning data collection practices embedded in LG smart TVs. Testing performed on retail models, including the OLED G5, indicates that these televisions actively scan local networks to map other connected hardware. By utilizing network packet captures via Wireshark, researchers discovered that the TVs identify nearby devices like smartphones and smartwatches, while also logging the names and signal strengths of neighboring Wi-Fi networks along with location data.

This extensive data gathering appears to be integrated into LG Ad Solutions, the company's targeted advertising division. While Automated Content Recognition (ACR) has long been known as a method for sampling on-screen audio and video to generate digital fingerprints, this new research shows the scope of surveillance is significantly broader than previously understood. The ad division claims to have access to hundreds of millions of addressable devices, largely by tracking other hardware found on the same home network as the television.

Perhaps most alarmingly, the investigation found that these TVs continue to capture audio even when the screen is powered down in standby mode. During bench tests, researchers observed that the sets recorded clean microphone audio while seemingly inactive. If the television was disconnected from the internet, it would store this voice input locally and upload the collected files to remote servers once a network connection was re-established.

In addition to these tracking behaviors, the research team identified several remote code execution vulnerabilities within webOS. These findings are currently moving through a responsible disclosure process. Given the aggressive nature of the built-in network scanning and data harvesting, the researchers have advised consumers to consider disconnecting their LG smart TVs from the internet entirely, suggesting the use of external, more secure streaming devices instead. LG has not yet provided a comment regarding these revelations.

Swiss federal government 已启动一项重要试点，计划用开源替代方案替代 Microsoft 365 。该计划将把约 3,000 台工作站迁移到为协作与办公任务开发的 openDesk 套件。此次转型约占 federal administration 总体人员的 7%，此前的一项成功 proof-of-concept 研究已评估了脱离专有软件的可行性。

这次调整的动因在于对 digital sovereignty 的追求。专家指出，依赖单一外国供应商会给公共机构带来重大风险，主要包括：受海外云立法影响可能导致外国访问政府敏感数据、对单一供应商的运营依赖带来的脆弱性，以及专有许可费用不断上升且缺乏谈判空间。

目前 civilian 试点项目正在与现有 Microsoft 软件并行运行以确保平稳过渡，但 Swiss military 的动作更快。该国的 Cyber Command 已决定在 2026 年 10 月之前用 openDesk 完全取代 Microsoft 365 。军方加速推进的主要原因是必须严格保护敏感数据，防止潜在的外国监视或干预。

这些举措是更广泛联邦战略的一部分，该战略随着 2024 年 EMBAG Law 的出台而加速。该法要求所有 Swiss federal agencies 默认将政府开发的软件开源，以促进创新与协作。此外，Federal Council 已正式将 digital sovereignty 列为优先任务，将其定义为政府在不受外部国家或供应商制约的情况下履行基本职能的能力。

这一迁移使 Switzerland 与 France 和 Germany 等国并肩，积极探索减少对 big tech 的依赖。尽管 Microsoft 在该地区仍是重要参与者，并持续在本地 AI 和 cloud infrastructure 上大量投入，Swiss government 对开源解决方案的承诺表明了重新掌控其 digital infrastructure 的趋势。若试点取得成功，可能为其他希望摆脱专有生态系统的组织提供范例。 The Swiss federal government has embarked on a significant pilot program aimed at replacing Microsoft 365 with open-source alternatives. This initiative involves migrating 3,000 workstations to the openDesk suite, a platform developed to handle collaboration and office tasks. This transition represents approximately 7% of the federal administration's total workforce and follows a successful proof-of-concept study that evaluated the feasibility of moving away from proprietary software.

The motivation behind this shift is rooted in the pursuit of digital sovereignty. According to experts, reliance on a single foreign vendor creates substantial risks for public institutions. Key concerns include the potential for foreign access to sensitive government data due to overseas cloud legislation, the operational vulnerability inherent in depending on one supplier for core services, and the rising costs of proprietary licensing fees that offer no room for negotiation or leverage.

While the civilian pilot project is currently running in parallel with existing Microsoft software to ensure a smooth transition, the Swiss military is moving even faster. The nation's Cyber Command is already set to fully replace Microsoft 365 with the openDesk suite by October 2026. This accelerated timeline is primarily driven by the military's strict requirement to keep sensitive data protected from potential foreign surveillance or interference.

These actions are part of a broader federal strategy that gained momentum with the introduction of the EMBAG Law in 2024. This legislation mandates that all Swiss federal agencies make government-developed software open source by default to foster innovation and collaboration. Furthermore, the Federal Council has formally identified digital sovereignty as a top priority, defining it as the government's ability to carry out its essential functions without becoming beholden to external countries or suppliers.

This migration effort places Switzerland alongside other European nations, such as France and Germany, that are actively exploring ways to reduce their dependence on big tech. Although Microsoft remains a significant player in the region and continues to invest heavily in local AI and cloud infrastructure, the Swiss government's commitment to open-source solutions signals a growing trend toward reclaiming control over its digital infrastructure. Success in this pilot phase could serve as a model for other organizations seeking to move away from proprietary ecosystems.

Internet Archive 正在呼吁社区在今年 9 月通过一项特别筹款活动，支持其"为所有人提供普遍获取知识"的使命。作为一个提供免费访问海量藏书、不投放广告、不出售用户数据、也不受企业干预的数字图书馆，Internet Archive 在很大程度上依靠用户支持来维持其庞大基础设施。这些开支包括维持其 210 petabytes 存档数据所需的服务器、存储、电力和制冷等成本。

为鼓励长期支持，Internet Archive 在整个月内推出了匹配捐赠活动。凡是开始每月 25 美元或以上的新定期捐赠，首笔捐款将被三倍匹配——也就是说，每月 25 美元等同于 75 美元，每月 50 美元等同于 150 美元，每月 100 美元等同于 300 美元。通过鼓励定期捐赠，图书馆旨在锁定维持系统全年稳定运行所需的持续资金。

保持独立性是该组织的首要任务：它自行构建和运营技术，而不是把技术外包给第三方公司。这样可以确保这个数字图书馆作为一个中立且易于访问的资源，继续服务全球的求知者。每一笔捐款（平均约 25 美元）都对确保书籍可读、网站被存档以及数字历史为后代保存发挥着至关重要的作用。 The Internet Archive is calling on its community to support its mission of providing universal access to all knowledge through a special fundraising initiative this September. As a digital library that offers free access to vast collections without advertisements, selling user data, or corporate interference, the organization relies heavily on the support of its users to maintain its expansive infrastructure. This includes the essential costs of servers, storage, power, and cooling that underpin its 210 petabytes of archived data.

To encourage sustained support, the Internet Archive has introduced a matching gift campaign throughout the month. Individuals who begin a new recurring donation of $25 or more will have their initial contribution tripled. This means a $25 monthly gift effectively becomes $75, while a $50 gift grows to $150, and a $100 donation results in a total impact of $300. By focusing on recurring donations, the library aims to secure the dependable funding required to keep its systems operational year after year.

Maintaining this independence is a core priority for the organization, as it builds and operates its own technology rather than outsourcing to third-party corporations. This ensures that the digital library remains a neutral and accessible resource for curious learners globally. Every donation, with the average contribution hovering around $25, plays a vital role in ensuring that books remain readable, websites stay archived, and digital history is preserved for future generations.

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