Solana推出SPL代币标准,自带13项新功能,意在放大B端市场?

Odaily星球日报Published on 2024-01-25Last updated on 2024-01-25

Abstract

常规需求标准化并内置,适用于资产功能升级,包括稳定币、游戏资产、RWA等。

原创 | Odaily星球日报

作者 | 南枳

Solana推出SPL代币标准,自带13项新功能,意在放大B端市场?

Solana 基金会宣布推出网络 SPL 代币的新标准“Token Extensions”,Token Extensions 是下一代 Solana 程序库标准,旨在帮助企业在 Solana 网络上提供更高效的安全性和合规性服务,为 Solana 上的数字和 RWA 资产提供灵活、安全的工具。

Token Extensions 引入了一组新的方法来扩展常规的代币功能,最初的 Token 标准引入了转账、冻结、铸造代币等基本功能,而 Token Extensions 包括相同的功能,但还包括如隐私转账、自定义传输逻辑、扩展元数据等。它们为企业和开发人员解锁了新功能,在某些情况下,这些功能以前在公链上是不可能实现的。

Solana 表示,加密服务公司 Paxos 和稳定币发行商 GMO-Z.com Trust Company 目前已经采用了 Solana 的 Token Extensions 来发行稳定币。

Token Extensions 带来了哪些功能?

据 Solana 文档指出,共有 13 种 Token Extensions,其具体功能和官方推荐用例,Odaily星球日报根据宣发文档和技术手册整理如下:

  • 隐私转账:保护在转账过程中用户余额的隐秘性,同时隐藏交易金额。用于链上支付、B2B支付、财库管理等。

  • 转账 Hook:代币发行者控制哪些钱包可以与其代币互动,以及代币和用户如何互动。用于 KYC 验证、代币使用限制、强制版税等。

  • 转账费用:协议级别收费能力。用于永久版税、交易费用等。

  • Metadata 指针:在代币和 Metadata 间建立可验证链接。用于代币验证、资产分发等。

  • 永久授权:允许程序对代币拥有不可撤销的权限。用于自动订阅服务,更新 RWA(数据)以反映现实,根据合规要求进行稳定币冻结和扣押操作。

  • Metadata:允许 Metadata 与消费场景原生协调。

  • 默认帐户状态:配置和强制实施代币帐户权限。用于 KYC 验证等。

  • 不可转让性:除了发行者外无法变更代币所有者。用于管理外部数据库、不可转移的 NFT 或其他资产。

  • 转账强制备注:转账时必须附上备注。用于合规、报告、审计追溯等。

  • CPI 防护:通过禁止跨程序调用内部的某些操作,限制其他程序与 Token Extensions 代币进行交互的方式。

  • 生息代币:允许在代币内设置利息并展示。

  • 不可变所有者:账户所有者不可变更。

  • 关闭 Mint 权限。

为什么需要 Token Extensions?

Solana 表示,可以将 Token Extensions 视为 Solana token 程序最新内置的一系列选项和功能。代币发行商包括游戏开发商、可编程代币发行商等,可以组合选择启用任意的 Token Extensions,从而获得以前在公链上无法实现的高级功能。

  • 灵活性:Token Extensions 提供对企业级功能的本地支持,无需任何额外的工具并沟通外部协议采用。也不需要面对开发、审核和部署自己的自定义代币合约的复杂性。

  • 低风险:使用经过审核和测试的 Token Extensions 可以减少攻击媒介,并有助于保护协议和资金,可以获得企业级的安全性和可靠性。

  • 降低测试成本:因为 Token Extensions 是通过简单地在代码中指定扩展来添加的,所以大大减少了缺陷和人为错误的可能性,从而节省了测试时间和成本。

  • 减少了开发时间:因为扩展是统一的和可重复使用的,所以使用扩展开发应用程序所需的时间显著减少。

Solana Foundation 政策主管 Amira Valliani 表示:“越来越多的企业对区块链的好处感兴趣,但希望确保以一种负责任的方式采用这项技术,符合其内部合规流程。代币扩展的诸多好处之一在于它简化了这些流程。像转账 Hook、隐私转账和永久授权等扩展使合规义务变得更加简便,确保公司不必花费资源定制智能合约以实施其合规框架。”

技术用例

Solana 公告宣称,一些使用 Solana 的稳定币发行商已经开始应用 Token Extensions。例如,Paxos 最近利用永久授权、Metadata 指针、转账 Hook 等来构建他们的 USDP 稳定币。GMO Trust 宣布在 Solana 网络上推出首个受监管的日元稳定币和他们自己的美元稳定币,使用了永久授权、默认账户状态和 Metadata 指针。目前 Phantom、Solflare、Fluxbeam 均已支持 Token Extensions。 此外,Token Extensions 除了构建更先进的稳定币之外,还适用于游戏资产升级、RWA 资产发行和治理等方面。

Related Reads

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbit39m ago

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbit39m ago

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit46m ago

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit46m ago

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit2h ago

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit2h ago

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbit3h ago

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbit3h ago

Trading

Spot
Futures
活动图片