Hyperliquid governance vote aims to permanently sideline $1B Assistance Fund

cointelegraphPubblicato 2025-12-17Pubblicato ultima volta 2025-12-17

Introduzione

The Hyper Foundation has proposed a validator vote to formally recognize approximately $1 billion worth of HYPE tokens held in the protocol’s Assistance Fund as permanently inaccessible. These tokens, accumulated through automated conversion of trading fees, are stored in a system address designed without retrieval mechanisms. A "Yes" vote would classify these tokens as burned, excluding them from circulating and total supply calculations. This move aims to clarify HYPE’s true supply amid growing institutional interest, as the protocol has generated around $874 million in fees year-to-date, with 99% routed to the Assistance Fund for token repurchases. The proposal does not reduce existing supply but formalizes the treatment of fee-derived tokens. Hyperliquid remains a major perpetual DEX, with over $205 billion in trading volume in the last 30 days.

The Hyper Foundation proposed a validator vote to formally recognize HYPE tokens held in the Hyperliquid protocol’s Assistance Fund system address as permanently inaccessible, excluding them from the asset’s circulating and total supply.

According to the Foundation, the Assistance Fund is a protocol-level mechanism embedded in the layer-1 network’s execution. It automatically converts trading fees into HYPE tokens and routes them to a designated system address. At the time of writing, the wallet contains about $1 billion in tokens.

The system address was designed without control mechanisms, making the funds irretrievable without a hard fork. “By voting ‘Yes,’ validators agree to treat the Assistance Fund HYPE as burned,” Hyper Foundation wrote.

Native Markets, the issuer of the Hyperliquid-native stablecoin USDH, reminded users that 50% of the stablecoin’s reserve yield is routed to the Assistance Fund and converted into HYPE tokens. “Should this validator vote pass, these contributions will then be formally recognized as burned,” the company wrote.

Source: Hyper Foundation

Clarifying supply amid institutional interest

While the proposal uses the term “burned,” it does not reduce the existing supply. Instead, it formalizes how the fee-derived tokens are treated for governance purposes, reducing ambiguity around Hyper’s effective supply.

The distinction has become more relevant as Hyperliquid’s fee-driven model has been attracting institutional attention.

In a research note covering Hyperliquid-focused digital asset treasuries (DATs), financial services firm Cantor Fitzgerald framed Hyperliquid as a protocol that returns nearly all of its fee revenue to tokenholders through automated repurchases.

Cantor estimated that Hyperliquid generated approximately $874 million in fees year-to-date (YTD) as of 2025. The company said 99% of protocol fees are routed through the Assistance Fund mechanism to repurchase HYPE.

The firm characterized the repurchases as a contributor to a declining circulating supply. However, the Hyper Foundation’s proposal drew a clear line by recognizing that the Assistance Fund balances were never intended to be spendable or recoverable.

The vote aims to align supply metrics with protocol design, rather than creating scarcity retroactively.

Related: 1.75M Hyperliquid tokens unlocked today, but was the price impacted?

Hyperliquid volume and HYPE DAT holdings

Hyperliquid remains one of the top contenders in the perpetual decentralized exchange (DEX) space. In the last 30 days, DefiLlama data shows that the protocol recorded over $205 billion in perpetuals trading volume, making it the third-largest perps DEX in the time frame.

Furthermore, a growing ecosystem of DAT companies has emerged around HYPE. According to Cantor, Hyperion DeFi (HYPD) holds about $46 million in HYPE tokens in its treasury, while Hyperliquid Strategies (PURR) holds about $340 million.

Magazine: Ethereum’s Fusaka fork explained for dummies: What the hell is PeerDAS?

Letture associate

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

The article "a16z: AI's 'Amnesia' – Can Continual Learning Cure It?" explores the limitations of current large language models (LLMs), which, like the protagonist in the film *Memento*, are trapped in a perpetual present—unable to form new memories after training. While methods like in-context learning (ICL), retrieval-augmented generation (RAG), and external scaffolding (e.g., chat history, prompts) provide temporary solutions, they fail to enable true internalization of new knowledge. The authors argue that compression—the core of learning during training—is halted at deployment, preventing models from generalizing, discovering novel solutions (e.g., mathematical proofs), or handling adversarial scenarios. The piece introduces *continual learning* as a critical research direction to address this, categorizing approaches into three paths: 1. **Context**: Scaling external memory via longer context windows, multi-agent systems, and smarter retrieval. 2. **Modules**: Using pluggable adapters or external memory layers for specialization without full retraining. 3. **Weights**: Enabling parameter updates through sparse training, test-time training, meta-learning, distillation, and reinforcement learning from feedback. Challenges include catastrophic forgetting, safety risks, and auditability, but overcoming these could unlock models that learn iteratively from experience. The conclusion emphasizes that while context-based methods are effective, true breakthroughs require models to compress new information into weights post-deployment, moving from mere retrieval to genuine learning.

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a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

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