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.

4.5.3.2 Return Ratio by Market Scenario

Figure 15 displays the final return ratio of a $100 initial investment across four distinct portfolio allocations, tested against the full spectrum of 18 market scenarios. The portfolios range from a low-Avail allocation ("10% Avail, 90% ETH") to a balanced, multi-asset strategy ("60% Avail, 20% ETH, 20% BTC").

The results consistently demonstrate that portfolios with a substantial allocation to Avail outperform those with minimal Avail exposure. The balanced portfolio ("60% Avail, 20% ETH, 20% BTC") proves to be the most robust, achieving the highest returns in the majority of scenarios, including a peak return of over 700% in the "alpha_outperforms_bull" scenario. This empirically validates the system's core principle: deeper alignment with the Avail ecosystem via Avail staking leads to superior financial outcomes.

4.5.3.3 Average Overall Yield Percentage

The below graph illustrates the average yield for all participants across the Avail Fusion ecosystem over a 350-day simulation period. It contrasts the average "basic" yield (dotted line) with the average "compounding" yield (solid line).

Rather than offering fixed, unsustainable APYs, Avail Fusion's architecture produces dynamically computed yields that respond to system-wide factors. The simulation shows that these yields quickly find a sustainable equilibrium. The average basic yield stabilizes in the 12-13% range, while the average compounding yield stabilizes at a higher level of approximately 15-17%. This confirms that the system is designed to provide attractive and stable returns for the average participant, with a clear, accessible benefit for those who choose to compound their rewards.

The inflow and outflow models, predicated on sigmoid responses to APY thresholds, represent a sophisticated attempt to programmatically anticipate and manage liquidity dynamics and user behavioral patterns. Such models typically rely on "rational agent" assumptions, where participants are expected to react predictably to changes in APY relative to some benchmark.

However, real-world crypto markets are characterized by high reflexivity, where sentiment, narratives, unexpected "black swan" events, and complex emergent behaviors like Maximal Extractable Value (MEV) strategies can significantly influence capital flows in ways not easily captured by simple APY comparisons. The inclusion of "panic withdrawal" conditions for scenarios like depleted reward budgets acknowledges some of these extreme edge cases.

While these models provide an essential baseline for simulations and system design, their inherent simplifications must be recognized. The robust governance framework, particularly the emergency powers vested in the Fusion Committee to pause operations or adjust parameters, serves as a crucial, practical safeguard against the limitations of these economic models.

Future iterations of Avail Fusion might explore even more adaptive algorithms, potentially incorporating machine learning or AI-driven risk assessment tools, to further enhance the system's responsiveness to complex market dynamics.

5. Avail Fusion Phase 3: Igniting the Economic Flywheel for the Avail Ecosystem

Phase 3 represents the maturation of Avail Fusion into a foundational economic engine powering the rollup-centric future of blockchain infrastructure. This phase transforms Avail from a staking platform into a comprehensive security marketplace serving:

5.1.1 Rollup-Centric Security Architecture

• Sequencer Security: Rollup sequencers can stake collateral through Fusion pools, inheriting multi-asset security for transaction ordering
• Liquidity Provider Integration: LPs across rollup bridges and DEXs can participate in security provision while maintaining capital efficiency
• Modular Security Layers: Rollups select specific security parameters based on their TVL and risk profiles

5.1.2 Core Infrastructure Components

• Avail DA: Enhanced data availability with pooled security backing, enabling rollups to achieve higher throughput with lower individual security requirements
• Avail Nexus: Fortified interoperability layer where cross-rollup messaging inherits the aggregate security of all participating chains
• Ecosystem Chains: Opt-in security inheritance model allowing new rollups to bootstrap with enterprise-grade protection from day one

In our analysis, this interplay creates an "economic flywheel": if Fusion improved the security profile of Avail DA and Nexus, that could make those services more attractive to new chains and rollups. More broadly, blockchain systems can lower the cost of networking by letting participants make joint investments in shared infrastructure rather than reproducing a separate platform stack for each new marketplace (Catalini & Gans, 2016)⁸. Shared-security systems are likewise motivated by the cost of requiring every new service to bootstrap its own trust network (Kannan, 2023)¹. If Fusion worked as modeled here, more adoption of Avail DA and Nexus would increase demand for Fusion-backed security services, which could in turn increase staking participation and the modeled security budget.

6. Technical Architecture and Implementation

Avail Fusion's robust cryptoeconomic model is brought to life through a carefully designed technical architecture, centered around the Fusion Staking pallet and its associated mechanisms for multi-asset management, bridging, and user interaction.

6.1 The Fusion Staking Pallet

As technical lineage rather than implementation proof, Avail Fusion follows the broader idea of a heterogeneous multi-chain base layer that provides shared security and cross-chain transport across diverse connected systems (Wood, 2016)⁹.

At the heart of Avail Fusion's on-chain logic resides the Fusion Staking pallet. This Substrate-based pallet is the core component responsible for managing all aspects of the multi-asset staking system. It enables external cryptocurrencies, such as wrapped Bitcoin (wBTC), Ethereum (ETH), and other compatible ERC20 tokens, to be staked on the Avail chain. In return for contributing these assets to secure the network, stakers generate rewards, which are distributed in Avail's native token (Avail). The pallet orchestrates the bonding of assets, tracking of contributions, calculation of rewards, and management of unbonding and withdrawal processes.

A critical function of the Fusion Staking pallet is the dynamic management of exchange rates between the various staked assets and the Avail rewards they generate. Exchange rates are recalculated periodically by the system. This process utilizes a metric referred to as FusionCurrencyBalance to represent asset values within the system. Contributions and ownership shares within pools are tracked via "points." These points, assigned based on the value of assets staked at the time of deposit, effectively represent a staker's proportional claim on the pool's rewards.

6.2 Bridging Architecture and Cross-Chain Asset Management

Given that Avail Fusion facilitates the staking of assets from external blockchains like Ethereum and Bitcoin, a secure and efficient bridging architecture is paramount.

6.2.1 Trusted-Minimized Bridges

The design assumes light-client-based bridges that verify remote consensus state and Merkle proofs rather than relying solely on custodial attestations (IBC Protocol Docs, n.d.)¹⁰. In Fusion, that is a bridge requirement of the proposed architecture.

6.2.2 Cross-Chain Verification

In light-client-based interoperability systems, the destination chain accepts an event only after the proof is checked against a sufficiently recent source-chain consensus root (IBC Protocol Docs, n.d.)¹⁰. In the Fusion context, that means a wrapped staking position should only be credited after the source-chain lock is proven.

6.2.3 Key Data Structures and Types

The Fusion Staking pallet defines several important data types and structures to manage staking assets, rewards, and user accounts. These provide a glimpse into the on-chain data organization crucial for developers and auditors:

• CurrencyId: u32; — An identifier for different types of staked currencies (e.g., one ID for Avail, another for ETH, another for wBTC).
• PoolId: u32; — An identifier for distinct staking pools, each potentially corresponding to a specific CurrencyId or a mixed-asset pool if designed so.
• FusionAddress — Represents an Ethereum-compatible address (e.g., a user's MetaMask address), facilitating interaction for users from the EVM ecosystem.
• Points: u128; — A numerical representation of a user's percentage ownership or share within a staking pool.
• FusionCurrencyBalance: u128; — A type used for representing balances of various currencies within the Fusion system, likely normalized or value-adjusted.
• BondingDuration: Get<EraIndex>; — Defines the length of the bonding/unbonding period, specified as an equivalent of 7 days in terms of Avail's "eras" (epochs).
• HistoryDepth: Get<EraIndex>; — Specifies the duration for which on-chain historical data (e.g., reward rates, staking amounts) is maintained for calculations and audits, set to 84 eras/days.

An illustrative data structure for managing user participation is FusionMembership:

struct FusionMembership {
    evm_address: FusionAddress,           // Address of the user
    pool_id: PoolId,                      // ID of the pool the user selected
    active_points: Points,                // The stake of the user represented by points
    unbonding_chunks: BoundedVec<(EraIndex, FusionCurrencyBalance), MaxUnbondingChunks>,
    is_compounding: bool,                 // If true, rewards will go to AVAIL pool
}

This structure securely maps each staker to their respective staking pools, balances, and preferences, relying on cross-chain proofs for the verification of externally sourced assets.

7. Governance, Security, and Sustainability

7.1 The Fusion Committee: Decentralized Oversight

The governance of Avail Fusion, particularly concerning root-level changes to the Fusion Staking pallet and its core parameters, is entrusted to a dedicated Fusion Committee. This body is established to ensure transparency, security, and adaptability of the system.

The committee is designed to operate through a decentralized governance model. Proposed changes are expected to undergo a process of discussion and voting among committee members, ensuring that decisions are not made unilaterally.

In the event of critical vulnerabilities (e.g., bridge exploits), the committee has the authority to pause staking operations, ensuring user funds remain protected.

7.2 Slashing Mechanism: Enforcing Network Integrity

To maintain the security and reliability of the Avail network, and by extension Avail Fusion, a slashing mechanism is implemented to penalize bad behavior by validators and, consequently, the staking pools that nominate them. This system ensures that participants who act maliciously or fail to meet their operational responsibilities are penalized, thereby protecting honest participants and the integrity of the consensus process.

7.2.1 Types of Offenses

Slashing can be triggered by several types of offenses, including:

• Equivocation: A validator submitting conflicting votes that could support incompatible finalized checkpoints is a canonical slashable offense in PoS finality systems (Buterin & Griffith, 2017)¹¹.
• Downtime: In the proposed Fusion design, prolonged validator unavailability could carry partial penalties.
• Invalid Proofs: In the proposed Fusion design, submission of incorrect or tampered proofs could also trigger penalties.

7.2.2 Validator Slashing

• Equivocation Penalties: Severe slashing for conflicting votes that could justify incompatible finalized checkpoints (Buterin & Griffith, 2017)¹¹
• Downtime Penalties: Partial penalties for extended validator unavailability in the proposed Fusion design
• Invalid Proof Penalties: Penalties for submitting incorrect or tampered data in the proposed Fusion design

7.2.3 Pool Slashing

When a validator commits a slashable offense, the penalty cascades to all Fusion pools that have delegated stake to that validator:

• Pools face proportional penalties based on their stake allocation to the offending validator
• Individual stakers within affected pools share the slashing penalty proportionally
• This shared risk model incentivizes pools to carefully select and monitor their validator nominations

7.2.4 Safeguards and Oversight

The slashing mechanism includes several safeguards: limits on the maximum amount that can be slashed in a single event or over a short period, oversight by the Fusion Committee (which can adjust penalty thresholds or pause slashing in critical situations), and potentially grace periods for validators to rectify issues before penalties are enforced. The entire process is designed to be event-driven and auditable for transparency.

7.3 Unbonding Period

Avail Fusion implements a 7-day unbonding period. When a staker initiates an unstake request, their assets enter this 7-day waiting phase. During this time, the assets are locked and no longer accrue staking rewards.

This "fair exit" period provides a necessary window for the network to detect any malicious behavior or network disruptions that might be associated with the staker's activities or the validators they supported before allowing the assets to be fully withdrawn.

8. Conclusion

Avail Fusion is presented here as a shared-security architecture that aims to move blockchain security away from isolated silos and toward a pooled security model. In that design, Bitcoin-, Ethereum-, and Avail-linked positions contribute to a common security budget rather than each chain relying only on its own native stake.

The three-phase rollout provides a clear path from current fragmentation to future unification. Our simulations demonstrate robust performance across market conditions, with TVL scaling from $150M to over $600M while maintaining system stability.

In our view, the value of a shared-security system is that it can reduce the need for each chain or rollup to bootstrap security independently.

Appendix

A.1 Detailed Theorem Derivations

The mathematical modeling of multi-token dynamics provides the theoretical framework for understanding security contributions and risk in a multi-token staking environment. This involves defining cumulative return ratios (p_i,t), portfolio value (V_t), and token proportions (θ_t).

For a two-token model, V_t = α p_1,t + (1 - α)p_2,t and θ_t = (α * p_1,t) / V_t. Logarithmic returns (r_i,t = log(p_i,t / p_i,t-1)) are used to derive linear relationships and approximations under conditions of small price variations. The model extends to three or more tokens, incorporating calculations for variance and standard deviation to assess portfolio stability and risk. A key objective is to determine the conditions for c₁ < (θ_T / θ₀) < c₂, leading to the derivation of feasible ranges for initial investment proportions (α) to ensure balanced participation and mitigate risks from extreme allocations, using relationships like (θ_T / θ₀) = 1 / (α + (1 - α) * R) where R = p_2,T / p_1,T.

A.2 Full Simulation Specifications and Market Condition Parameters

The Avail Fusion economic model has been subjected to extensive simulation across a diverse range of market conditions to assess its robustness and performance characteristics. For reproducibility, we tie this appendix to the public simulation repository snapshot at commit 3343653f29c980998df1e0fee68afe8b74f0f099, whose checked-in scenario file defines 17 active named scenarios (Avail Project, 2025)⁷.

• Base Market Scenarios (Scenarios 1-6): Uniform market movements (All Bearish, All Neutral, All Bullish) with both Low and High Volatility. Correlation: High (ρ=0.9).
• Alpha Outperformance Scenarios (Scenarios 7-9): Avail token outperforms ETH/BTC in Bull, Bear, and Higher Volatility contexts. Correlation: Medium (ρ=0.6).
• Beta Outperformance Scenarios (Scenarios 10-12): ETH/BTC outperform Avail in Bull, Bear, and Higher Volatility contexts. Correlation: Medium (ρ=0.6).
• Uncorrelated Market Scenarios (Scenarios 13-15): Tokens exhibit independent price movements (Bull/Bear Mix, Fully Mixed Performance, Divergent Volatilities). Correlation: Low (ρ=0.3) or Medium (ρ=0.6).
• Market Regime Transition Scenarios (Scenarios 16-17): Significant mid-simulation shifts (Bull to Bear, Bear to Bull). Correlation: High (ρ=0.9) or Medium (ρ=0.6).

Each checked-in scenario is defined by annual return, annual volatility, and inter-token correlation inputs for Avail, ETH, and BTC in the public codebase (Avail Project, 2025)⁷.

A.2.1 Table of Market Conditions

A.3 Simulation Modeling and Assumptions

This section details the mathematical models, behavioral assumptions, and system parameters used to simulate the Avail Fusion cryptoeconomic system. The reproducibility source for the implementation is the public repository snapshot cited in Reference 7, which includes the scenario definitions, runner scripts, notebooks, and model code inspected for this paper (Avail Project, 2025)⁷.

A.3.1 Mathematical Frameworks for Price Trajectories

To ensure robust testing, the checked-in public code supports two relevant price-path approaches:

1. Brownian-motion-based bounded trajectories: The simulation runner includes Brownian-motion-derived generators with configurable trajectory shape, volatility, and minimum/maximum price bounds.
2. Correlated scenario price paths: The market-scenario module generates correlated token paths from scenario-level annual return, volatility, and correlation assumptions.

Stylized Price-Path Model — In analytical terms, we reason about token paths using a stochastic price model with scenario-specific drift and volatility inputs. In the public repository, these paths are generated programmatically from scenario assumptions and bounded trajectory controls rather than from a single uniform implementation used everywhere in the appendix (Avail Project, 2025)⁷.

where the drift term μ_i(t) encodes the market regime:

The drift term μ_i(t) is used here as a compact way to describe regime-dependent return assumptions. In the checked-in simulation code, prices are constrained by configured bounds and updated according to the specific generator selected for the run; we therefore treat the equation here as a stylized description rather than a verbatim transcription of every implementation path in the codebase.

Correlated Scenario Model — The checked-in market-scenario module updates Avail, ETH, and BTC prices from annual return, annual volatility, and correlation inputs, using correlated normal shocks in the discrete-time price update (Avail Project, 2025)⁷:

Where:

The correlation between the Wiener processes is modeled using a Cholesky decomposition of the correlation matrix R:

Where L is the Cholesky decomposition of R, and ε_t is a vector of independent standard normal variables. The correlation matrix is defined as:

A.3.2 Simulated Price Trajectories Under Various Market Conditions

The following graphs illustrate the simulated price trajectories for Avail, ETH, and BTC. In the checked-in scenario module, these paths form the basis for testing the system across 17 active named market scenarios, all beginning from initial prices of $0.05 for Avail, $2,500 for ETH, and $100,000 for BTC (Avail Project, 2025)⁷.

A.3.3 Agent Behavioral Assumptions

To simplify the analysis while capturing the full spectrum of potential outcomes, the agent-based simulations operate on a key assumption: every agent is a "maxi" who stakes only a single token type (e.g., Avail-maxi or ETH-maxi). The rationale is that the yield for any hybrid staker can be represented as a convex combination of these monolithic strategies, meaning their returns will be bounded by the returns of the single-asset stakers.

Monolithic Staker Strategies: Each agent a is assumed to follow one of the exclusive staking strategies:

where a strategy indicates staking only Avail, and indicates staking only ETH. This simplification reduces the number of agent archetypes while still allowing for the modeling of complex population-level behavior.

A.3.4 Staking Inflow and Outflow Modeling

The system governs the flow of funds into and out of staking pools using a dynamic model responsive to the pool's current Annual Percentage Yield (APY).

Deposit Model (Inflow) — The deposit flow is determined by a sigmoid function that modulates the amount based on the pool's current APY (A_curr) relative to a predefined threshold (A_thresh).

Deposits are immediately halted (D_flow = 0) if a pool is deleted, paused, or its rewards budget is depleted.

Withdrawal Model (Outflow) — The withdrawal flow is inversely proportional to the APY, increasing as the yield drops below the threshold. This is modeled using an inverse sigmoid function:

Special Withdrawal Conditions — The model incorporates accelerated withdrawal mechanisms for specific risk scenarios:

• Deleted Pools: If a pool is marked as deleted, a rapid withdrawal is triggered, calculated as 30% of the pool's current TVL. W_accelerated = TVL_current * 0.30
• Budget Depleted Pools: If a pool's rewards budget is depleted, a "panic withdrawal" is initiated. The withdrawal amount is the greater of the standard flow or 15% of the current TVL. W_panic = max(W_flow, TVL_current * 0.15)

A.3.5 Avail Reward Boosting Specifications

The Avail Boosting mechanism is designed to enhance user rewards based on two factors: long-term commitment via token locking and the user's proportional stake in the Avail pool.

Time-Lock Period Multiplier (M_lock) — This multiplier rewards users for locking their Avail tokens for specific durations.

Pool Share Multiplier (P_share) — This multiplier provides an additional boost to users who hold a significant share of the total Avail pool:

Total Boost Calculation (B_total) — The final boosting multiplier is the product of the individual time-lock and pool-share multipliers:

For example, a user locking Avail for 60 days (M_lock=1.1x) and holding a 1.5% share of the pool (P_share=1.1x) would receive a total boost of 1.1 * 1.1 = 1.21x, a 21% enhancement on their base rewards.

A.3.6 Expected Analytical Outcomes

This comprehensive simulation framework enables the analysis of:

1. Cross-Token Flow Correlations: How staking flows between tokens correlate under different market conditions.
2. Regime Sensitivity: Which tokens are most sensitive to market regime changes.
3. Volatility Impact: How volatility affects staking decisions independently of returns.
4. Platform Stability: Under which conditions the platform faces the greatest risk.
5. Optimal Yield Strategies: How to adjust APY rates for different market conditions.

The results will inform strategic decisions regarding risk management, capital allocation, and yield optimization across the multi-token staking platform.

A.4 Avail Fusion Parameter Specifications