Avail Fusion: Unified Cryptoeconomic Security and Multi-Asset Consensus

1. Introduction

1.1 The Capital Allocation Problem in Web3

In our analysis, today's blockchain ecosystem remains fragmented across many isolated networks, each with its own validator set and economic base. That framing is consistent with shared-security work describing a landscape of fragmented trust networks that repeatedly bootstrap security from scratch (Kannan, 2023)¹. Avail Fusion is motivated by the idea that security capital is still mostly siloed by chain rather than pooled across the broader ecosystem.

This model creates several systemic inefficiencies:

• Isolated Capital Pools: In our analysis, large pools of economic security remain tied to individual chains instead of being reusable across the broader ecosystem.
• Heterogeneous Security Guarantees: Newer networks generally begin with less stake, thinner validator sets, and weaker security margins than mature networks.
• Redundant Infrastructure: Much of the ecosystem still maintains separate validator sets, bridges, and monitoring stacks for each chain.

These challenges are particularly acute for emerging networks and rollups that require robust security from inception but lack the established token economics of mature protocols.

1.2 Introducing Avail Fusion: Unified Cryptoeconomic Security

Avail Fusion is proposed here as a multi-asset consensus mechanism. In the design described in this paper, positions linked to BTC, ETH, liquid staking tokens (LSTs), and AVAIL contribute to the system's security budget (see Section 2 for the model).

The design goal is to let holders of major crypto assets help secure Avail while retaining exposure to those assets. We state that as a property of the architecture described here, not as a claim established by prior literature.

1.3 Avail's Vision: The Unification Layer

Avail's "Unification Layer" is the architectural framing used in this paper for a stack that combines data availability, interoperability, and shared security. The relevance of data availability to that stack is straightforward: light clients need strong assurance that on-chain data is available and valid (Al-Bassam et al., 2018)².

The Unification Layer addresses Web3's coordination challenges by providing:

• Shared Security Infrastructure with Avail Fusion: Aggregated cryptoeconomic protection accessible to multiple networks
• Interoperability Primitives with Avail Nexus: a proposed cross-ledger communication layer; communication across distributed ledgers is surveyed by Zamyatin et al. (2019)³
• Standardized Data Availability: Common primitives for cross-chain data verification and storage

1.4 Research Contributions

This paper presents the design, implementation, and economic modeling of Avail Fusion's multi-asset consensus system. Our primary contributions include:

1. Multi-Asset Consensus Architecture: A proposed design in which foreign crypto assets contribute to the system's modeled security budget
2. Mathematical Security Framework: Formal models quantifying security contributions from heterogeneous asset pools (see Section 2)
3. Economic Mechanism Design: Incentive structures that balance multi-asset participation with system stability (see Section 3-5)
4. Empirical Analysis: Simulation results demonstrating the security and economic properties of the proposed system (see Appendix A)

2. Theoretical Foundation: Additive Security Framework

2.1 Mathematical Model of Multi-Asset Security

Our additive-security model departs from conventional single-asset proof-of-stake designs. Instead of assuming that one native token defines the full security budget, we model total security as the sum of the economic weight committed across the participating asset pools.

The total security of the system is defined as:

Where:

• S_Avail = dollar value of staked Avail tokens
• S_External_i = dollar value of each external asset class (ETH, BTC, LSTs)
• n = total number of external asset types

Consider a practical example:

• S_Avail = $100 million (staked Avail)
• S_ETH = $200 million (staked ETH)
• S_BTC = $150 million (staked BTC)
• S_LSTs = $50 million (staked liquid staking tokens)

2.2 Market Coordination Complexity

The economic complexity of attacking multi-asset consensus systems increases non-linearly compared to traditional single-asset models. We identify three primary factors:

2.2.1 Single-Asset vs Multi-Asset Attack Models

Single-Asset Attack Model: As a baseline for this paper, we treat a conventional single-asset system as one where attack cost is concentrated in a single stake market. The exact threshold depends on protocol rules, but the comparison is useful because it isolates the effect of moving from one stake market to several.

Multi-Asset Attack Model: Avail Fusion's aggregated security model requires attackers to coordinate capital deployment across heterogeneous asset markets. The attack cannot succeed by controlling a single asset class, as each contributes independently to the total security calculation.

2.2.2 Liquidity Fragmentation Effects

In our analysis, attack cost depends on market depth, slippage, and the observability of large coordinated purchases, not just on quoted spot prices.

• Price Discovery Mechanisms: Bitcoin markets demonstrate different depth profiles compared to Ethereum or smaller altcoin markets
• Market Surveillance: Coordinated acquisitions create observable market signatures
• Liquidity Constraints: Slippage costs escalate non-linearly as acquisition size increases

2.2.3 Attack Cost Escalation

Figure 3 demonstrates how attack costs escalate differently between security models. In traditional Proof-of-Stake systems (red line), attack costs grow linearly. However, Avail Fusion's multi-asset model (green line) exhibits exponential cost growth due to market fragmentation and coordination complexity, creating a 5.1x security premium.

3. Phase 1: Points System and Initial Bootstrapping

3.1 Phased Rollout Strategy

The rollout of Avail Fusion follows a structured three-phase approach (see Figure 4):

Phase 1 deploys heterogeneous single-token staking pools designed to accommodate diverse asset classes and reduce participation barriers:

• Native Avail Pool: Core governance token staking with enhanced multiplier eligibility
• Ethereum (ETH) Pool: Primary external asset integration via secure bridge infrastructure
• Wrapped Bitcoin (WBTC) Pool: Bitcoin network liquidity incorporation
• Liquid Staking Token (LST) Pool: Support for stETH, wstETH to maximize capital efficiency

This heterogeneous pool structure enables immediate participation from holders of established crypto assets without requiring preliminary Avail token acquisition, thereby expanding the initial addressable liquidity base.

3.2 Points System Mathematical Framework

Phase 1 uses a deferred reward model in which participants accumulate points representing their proportional contribution to network security.

Point accumulation follows a multi-factor calculation model:

Base Point Rate (P₀): Points accrue proportionally to deposited asset value, normalized through FusionCurrencyBalance conversion to maintain cross-asset equivalency. The fundamental determinant of point accrual is the value of the assets a user stakes into a particular pool. The system calculates an equivalent FusionCurrencyBalance for the deposited tokens and assigns points proportionally based on this value. This ensures that larger stakes, representing a greater contribution to the pool's TVL and thus security, earn points at a correspondingly higher rate.

Time-Lock Multiplier (M_lock) — Avail Fusion incorporates incentives for long-term commitment, particularly for stakers in the native Avail pool. By locking their Avail tokens for predefined durations, users can receive a multiplier on their earned rewards (and by extension, on the value derived from their points). The documented multipliers, applied across all pools, are as follows:

• τ = 0 days: M_lock = 1.00
• τ = 30 days: M_lock = 1.05
• τ = 60 days: M_lock = 1.10
• τ = 180 days: M_lock = 1.50

This tiered system directly rewards users who signal a longer-term alignment with the Avail ecosystem, contributing to network stability and reducing token velocity.

Pool Share Multiplier (M_share) — Further incentivizing participation in the native Avail token pool, a "Pool Share Multiplier" provides an additional boost to users who hold a significant proportion of the total Avail staked in that specific pool. For instance, a user whose stake constitutes ≥1% of the total Avail in the Avail pool receives an additional 1.1x multiplier. This mechanism encourages substantial stakes in the native asset pool, which can be particularly important for governance participation and core security contributions.

The combined effect of these multipliers is multiplicative. Exclusive to Avail Pool:

• For stake proportion σ ≥ 1% of total Avail pool: M_share = 1.10
• Otherwise: M_share = 1.00

The multiplicative nature of these factors creates compounding incentives for long-term commitment and substantial native token participation. For example, a user locking Avail for 60 days (M_lock=1.1x) and holding ≥1% of the Avail pool (M_share=1.1x) would achieve a total boost of 1.1×1.1=1.21x on their base rewards from that pool.

3.3 User Journey and Participation Flow

The Phase 1 participation process involves:

1. Wallet Connection: EVM-compatible wallet integration
2. Account Initialization: Fusion-specific account creation
3. Pool Selection: Choice from available single-token pools
4. Asset Deposit: Secure bridge integration for cross-chain assets
5. Point Accrual: Automatic calculation based on contribution metrics

The point system quantifies participants' security contributions to the Avail network through their staked capital. Higher point accumulation rates correspond to greater security provision, with time-lock mechanisms reducing token velocity and pool share incentives encouraging substantial native token commitment. This Phase 1 design establishes the foundational security layer while systematically building stakeholder alignment prior to full economic mechanism activation in subsequent phases.

4. Phase 2: APY Mechanism and Economic Incentives

4.1 Transition from Points to Annual Percentage Yields

Phase 2 introduces explicit Annual Percentage Yields (APYs). The economic reason is that staking returns compete with alternative on-chain yields, and attractive lending rates can pull tokens out of staking and reduce network security (Chitra, 2020)⁴.

4.2 Reward Generation Mechanics

The reward system in Avail Fusion is meticulously designed to be sustainable, attractive, and aligned with the long-term growth of the Avail ecosystem.

A critical and distinctive feature of Avail Fusion's tokenomics is that all staking rewards, irrespective of the type of asset staked (e.g., BTC, ETH, LSTs, or Avail itself), are distributed exclusively in Avail's native token, Avail. This Avail-centric reward mechanism is fundamental to integrating all participants into the Avail economy. Beyond its direct economic function, the Avail token is positioned to play a key role in the ecosystem's governance, with a long-term vision that includes enabling staker-led decision-making. Additionally, holding and staking Avail may act as a base criterion for users to receive airdrop perks and other benefits from the growing number of chains building within the Avail Nexus ecosystem.

4.2.1 Inflation Model

The primary source of Avail rewards distributed to stakers in this model is a controlled inflation schedule. We use a target annual inflation rate of 5% as a simulation parameter rather than as a claim derived from prior literature.

Avail Fusion's inflation mechanism is not static. We model it as a target-staking-rate controller: if the staking rate falls below an ideal threshold, the reward rate increases; if staking rises above the target, the reward rate decreases. That control pattern is already used in deployed PoS tooling; the Cosmos SDK mint module, for example, adjusts inflation toward a goal bonded ratio rather than fixing a single permanent reward rate (Cosmos SDK Docs, n.d.)⁵. Polkadot's NPoS economics likewise define an ideal staking rate and vary inflation around that target (Cevallos & Gehrlein, 2023)⁶. In this paper, X_ideal=0.75 is a simulation parameter and therefore an analytical design choice rather than a claim of protocol finality.

4.2.3 Boosted APYs and Small Pool Incentives

Beyond the base APYs derived from the inflation model, Avail Fusion incorporates mechanisms for targeted incentivization. Specific staking pools may offer "Boosted APYs," which represent an additional reward percentage layered on top of the base APY. These boosts can be strategically deployed based on various factors, such as the network's immediate strategic priorities (e.g., attracting a particular type of asset) or specific liquidity demands within certain pools.

To foster decentralization and prevent the over-concentration of staked assets in a few large pools, Avail Fusion also implements "Small Pool Incentives." Smaller pools are prioritized for additional rewards, encouraging users to distribute their stakes more broadly across the ecosystem, thereby enhancing overall network resilience and fairness.

The above chart compares the simulated two-year reward outcomes—both in Avail tokens and in USD—for various portfolio allocations under different market conditions. Each point represents a unique combination of market scenario and allocation strategy, where $100 is invested on day one across Avail, ETH, and BTC. Portfolios with minimal Avail exposure consistently underperform both in Avail rewards and their USD equivalent, despite market fluctuations.

In contrast, portfolios with higher Avail stakes accrue higher rewards, both in Avail and USD. This illustrates the strategic tradeoff in Avail Fusion: optimizing for security contribution through Avail not only strengthens the network but also maximizes user reward potential.

4.3 Staking Inflow and Outflow Dynamics in Simulations

The movement of funds into (deposits/inflows) and out of (withdrawals/outflows) Avail Fusion's staking pools is governed by dynamic models that respond to prevailing market conditions, specifically the current APY offered by a pool relative to a predefined target APY threshold. These models are based on sigmoid functions to create smooth, S-shaped response curves.

The deposit flow (D_flow) into a pool is calculated as:

Where D_base is the base deposit amount, D_max_extra is the maximum additional deposit possible, k_d is the sigmoid's steepness factor for deposits, A_curr is the current APY of the pool, and A_thresh is the target APY threshold. Deposits are immediately halted (D_flow = 0) if a pool is deleted, manually paused by governance, or if its rewards budget is depleted, resulting in zero yield.

Conversely, the withdrawal flow (W_flow) is designed to be inversely proportional to the APY; as the APY drops below the threshold, withdrawals are expected to increase:

4.4 Withdrawal Process and Reward Management

The process for users to withdraw their staked assets and claim rewards in Phase 2 is designed to be secure and orderly:

4.4.1 Request Withdrawal

A user initiates a withdrawal request for their staked assets from a specific pool.

4.4.2 Unbonding Period

Upon requesting withdrawal, the assets enter a mandatory 7-day unbonding period. During this 7-day window, the assets are still technically locked and cannot be transferred or used elsewhere. Importantly, the staker stops earning rewards on these unbonding assets. This period is crucial for network stability, preventing sudden large-scale liquidity shocks and allowing time for any pending security checks or slashing conditions to be processed.

4.4.3 Withdraw Assets

After the 7-day unbonding period concludes, the user can instantly withdraw their original staked assets.

4.4.4 Reward Management

Rewards (in Avail) are distributed periodically. Users have the flexibility to manage their earned Avail tokens in several ways: they can manually claim them and choose to reinvest (compound) them back into a staking pool (potentially the Avail Pool to benefit from its specific boosts), hold them, or liquidate them on the open market. Users can also opt for auto-compounding where available, which automatically restakes their rewards to increase their share of the pool over time.

4.5 Simulation Results

This section presents an analytical walkthrough of the system's early-stage mechanics, building evidence for how Avail Fusion could serve not only as a staking platform, but as a trust layer for interchain liquidity and economic flow.

4.5.1 Solid Growth in TVL (Total Value Locked)

The first simulation traces the growth of TVL across three assets: AVAIL, ETH (stETH), and BTC (WBTC). Importantly, BTC was introduced only after Day 180, allowing for a "secure bootstrapping" of the system before onboarding higher-liquidity but more price-volatile capital.

The tiered adoption reinforces a layered security design, where native asset commitment precedes external capital inflow—a prudent step for any system attempting to balance decentralization with financial weight.

Our simulations evaluated Avail Fusion's TVL accumulation potential across diverse market conditions over several timesteps, representing approximately 1 year of protocol operation. The analysis encompassed four primary asset categories—aggregate security, Avail, BTC, and ETH pools—tested against 17 distinct market scenarios ranging from conservative base cases to extreme volatility conditions.

4.5.2 Aggregate TVL Metrics for Fusion Pools

Figure 9 illustrates the simulated performance of Total Value Locked (TVL) across a wide range of potential market conditions. The simulations are categorized into several scenario types to stress-test the system's robustness and growth potential:

• Default Scenario (Base): Represents the median expected outcome or benchmark case.
• Base Scenarios: Model general market movements (bullish, neutral, bearish) combined with different levels of volatility (low, high).
• Alpha & Beta Scenarios: Model outcomes where the protocol's specific strategies (alpha) or broader market sensitivity (beta) outperform under different conditions.
• Uncorrelated Scenarios: Model chaotic or mixed market conditions.
• Regime Scenarios: (Applicable to Total Security) Model major shifts in the market, such as from a bear to a bull market.

The total security TVL below demonstrates robust growth potential, with base case scenarios achieving steady accumulation from initial deployment. The base scenario shows a healthy and consistent increase in total security, finishing at approximately $150M. The system's potential is most evident in the alpha_outperforms_bull and all_bullish_high_vol regime shift scenarios, which drive the Total Security to peaks of over $600M. This proves the system is designed to capitalize effectively on favorable market conditions.

Even in the most adverse bearish scenarios, the Total Security stabilizes around $100M, demonstrating a strong floor and inherent resilience.

Figure 10 illustrates the simulated TVL for the Avail (Avail) vault, which appears to be a significant driver of the system's overall value. The base scenario results in modest growth, reaching just over $100M. The most notable result is the alpha_outperforms_bull scenario (orange dashed line), which exhibits explosive and volatile growth, peaking near $600M. This highlights the immense upside potential when the protocol's alpha-generating strategies are successful within a bull market.

The Avail vault's performance showcases the powerful potential for non-linear returns under favorable alpha and market conditions.

Figure 11 shows the simulated TVL for the Bitcoin (BTC) vault. The simulation begins with a rapid accumulation phase around Timestep 180, where TVL quickly grows to approximately $40M when the cold start period ends. Following this, the performance diverges based on market conditions.

The base scenario (red line) maintains a stable TVL of around $42M. In bullish scenarios, particularly uncorrelated_fully_mixed (purple dashed line), the TVL shows strong, sustained growth, approaching $60M. Conversely, bearish scenarios lead to a gradual decline in TVL, with the most pessimistic case dropping towards $30M.

This demonstrates that the BTC vault's value is correlated with market direction, with high volatility significantly amplifying both potential gains and losses.

Figure 12 displays the simulated TVL for the Ethereum (ETH) vault, which begins accumulating value from Timestep 0. The base scenario achieves steady growth, ending at approximately $17M. Bullish, high-volatility conditions propel the TVL to a high of $25M, representing the best-case outcome. Bearish conditions cause the TVL growth to flatten and slightly decline, with the worst-case scenarios bottoming out around $10M.

The ETH vault demonstrates a similar pattern to BTC, where its growth is heavily influenced by bullish tailwinds and dampened by bearish pressure.

Moving forward, these findings inform three critical development priorities: (1) implementing more sophisticated volatility modeling to better capture real market dynamics, (2) developing cross-protocol balancing mechanisms to mitigate concentration risks, and (3) creating adaptive strategies that can maintain performance across varying market regimes. The strong performance differential between bullish and bearish scenarios particularly highlights the need for bidirectional optimization in protocol design.

4.5.3 Yield Performance: Average and Compounding Returns

To validate the design of the reward system and assess its performance under various economic conditions, extensive simulations have been conducted. These simulations model the behavior of different asset pools (Avail, ETH, BTC) and staker archetypes across a wide range of market scenarios, including bull markets, bear markets, neutral conditions, and periods of high and low volatility.

4.5.3.1 Yield Performance: Average and Compounding Returns

Figure 13 illustrates the simulated yield dynamics for staking Avail, ETH, and BTC over a 700-day period across 17 checked-in market scenarios in the public simulation repository snapshot used for reproducibility (Avail Project, 2025)⁷. The analysis distinguishes between two reward strategies:

• Regular Yields (Figure 13): This represents a "stake-once" approach where rewards are claimed but not restaked. Avail yields remain highly stable around 15%, reflecting their direct link to the protocol's inflation model. ETH and BTC yields are lower and exhibit more volatility, as their Avail-denominated rewards are influenced by relative price fluctuations.
• Compounding Yields (Figure 14): This shows the outcome when all Avail rewards are continuously restaked. The effect is most pronounced for Avail stakers, whose yields steadily grow to over 20%, demonstrating the powerful wealth-creation effect of compounding within the native ecosystem.