Decentralized Finance (DeFi) is a paradigm shift in financial architecture—a fully permissionless, composable ecosystem where cryptographic guarantees enable global access to sophisticated financial primitives. The magic of DeFi lies in its composability, transparency, and 24/7 operation, creating unprecedented opportunities for innovation.

But there’s a catch: DeFi’s logic is fundamentally constrained by what can be expressed directly on-chain. While secure and decentralized, the Ethereum Virtual Machine and similar blockchain environments impose strict limitations on computational complexity to maintain their security guarantees. This bottleneck stifles DeFi’s ability to adapt dynamically to market conditions, leaving it reliant on rigid, outdated parameters and worse, third-party intermediaries for risk management—a deviation from the trustless ethos at DeFi’s core.

Current DeFi protocols operate with static configurations—swap fees, interest rates, collateral ratios—that are updated via governance proposals every three to six months, if at all. This lag renders protocols perpetually misaligned with real-time market dynamics, undermining efficiency and resilience. A truly decentralized financial system demands protocols that continuously ingest market data to adjust parameters intelligently and instantaneously, leveraging advanced statistical models to reflect volatility, liquidity, and risk—all without compromising security or introducing trusted third parties. Verifiable computation is the technical breakthrough that eliminates this dependency and enables complex off-chain models to integrate seamlessly with on-chain trustlessness.

Enter EZKL. EZKL simplifies turning statistical models for decision making into verifiable equivalents with smart contracts for verification. This capability is why Balancer selected EZKL to pioneer dynamic fees on their pools thus bringing the power of real-time adaptive parameters to DeFi without compromising decentralization or security.

Before we get into technical details, let’s examine the mechanics of current DeFi systems.

Static Parameters: DeFi’s Fundamental Flaw

DeFi’s greatest paradoxes is that it inherently is a digital-native ecosystem capable of instant execution yet governed by parameters that remain static for months at a time. This rigidity creates fundamental inefficiencies that ripple across protocols and erodes their competitiveness with traditional finance’s adaptive systems.

Consider Balancer’s liquidity pools as a case study. Despite crypto’s notorious volatility, swap fees remain constant and liquiidty pools typically maintain the same swap fee percentage regardless of market conditions. This is akin to a traditional market maker applying uniform spreads to both a flash crash and a calm trading day.

The consequencers are quantifiable. Here’s what this means in practice:

1. Low Volatility Overpricing: Excessively high fees deter trading volume, reducing protocol utility and driving users away.
2. High Volatility Undercompensation: Liquidity providers (LPs) face amplified impermanent loss (IL) without fee adjustments. This leads to capital flight when liquidity is most needed because LPs aren’t adequately compensated for their risk.
3. Governance Latency: Parameter updates via proposals lag weeks behind market shifts, rendering adjustments obsolete upon enactment.
4. Persistent Suboptimality: Fees misaligned with risk-adjusted returns degrade capital efficiency ecosystem wide.

This rigid parameter problem extends beyond just swap fees—it affects interest rates in lending protocols, collateralization ratios, liquidation thresholds, and virtually every adjustable parameter in DeFi. While traditional finance has sophisticated systems that adjust these values in real-time based on market conditions, DeFi has been stuck with what amounts to manual adjustments on quarterly or yearly schedules.

The root cause isn’t a lack of understanding about what should be done—it’s the technical limitations of what can be efficiently expressed and executed on-chain. Complex statistical models that analyze market data and output optimal parameters, such as a multivariate regressions or volatility forecasts, are prohibitively expensive to run directly in smart contracts.

The Technical Barriers to Expressive DeFi

The root of this problem lies in the fundamental architecture of blockchain systems. While Ethereum and similar platforms have created the foundation for permissionless finance, they impose severe constraints on computational complexity.

Every operation in a smart contract costs gas and this cost scales with computational complexity. Statistical models that would be trivial to run on a laptop—like regression analyses, volatility prediction, or machine learning classifiers—become prohibitively expensive when implemented directly on-chain. A simple linear regression might cost millions in gas; a neural network is unthinkable. The EVM was designed for security and determinism, not for running complex financial models.

This creates an impossible trilemma for protocol designers:

1. Implement simple but suboptimal models that can run on-chain
2. Rely on governance to periodically update parameters while accepting delays and human errors.
3. Use centralized oracles or off-chain systems, compromising decentralization and undermining trustlessness.

Workarounds like Uniswap v3’s concentrated liquidity to give LPs more control over capital efficiency, Curve’s specialized formulas for stablecoin pairs, or Balancer’s weighted pools with customizable parameters enhanced efficiency but fail to address the core problem. DeFi lacks a mechanism to execute complex, adaptive computations without sacrificing decentralization. Third party risk managers, while effective, introduce opacity as well as single points of failure and are antithetical to DeFi’s vision. The inability to continuously adapt protocol behavior based on real-time market conditions through complex statistical analysis severely handicaps allocators’ willingness to deploy capital.

The solution lies in decoupling computation from execution: perform the intensive analysis that underlies sophisticated closed financial systems off-chain then anchor its integrity on-chain using cryptographic proofs, thus guaranteeing DeFi’s ethos of transparency and trustlessness.

Systemic Risks of Static DeFi

Static parameters don’t solely hamper efficiency, they expose DeFi to cascading vulnerabilities:

Liquidity Provider Erosion: When market volatility spikes, LPs face increased impermanent loss without compensating fee increases, driving LPs away and thinning liquidity buffers.

MEV Exploitation: Fixed fee structures create predictable arbitrage opportunities that can be exploited by sophisticated MEV bots, siphoning value from regular users and liquidity providers.

Market Inefficiency: Inability to adjust fees based on market conditions means protocols operate sub-optimally most of the time—either overcharging users during calm periods or undercharging (and thus undercompensating LPs) during volatile ones.

Capital Lockup: Static models force over-collateralization and conservative parameters to account for worst-case scenarios, immobilize billions in capital that could otherwise be deployed productively.

As noted in our previous post about Decentralized Finance Protocols:

In DeFi, automated market makers, lending platforms, and derivatives exchanges collectively manage hundreds of billions in assets through algorithmic systems. Aave and Compound, two major lending protocols, use complex statistical models to determine safe loan-to-value ratios and liquidation thresholds. The on-chain nature of these systems means verification isn’t just about correctness—it’s about maintaining the trust assumptions that allow these permissionless systems to function at all. Unlike traditional finance, there are no central authorities to roll back transactions or compensate victims if models behave unexpectedly: failures are permanent.

Verifiable Computation: Trustless Computation for Real-Time Adaptive DeFi

Verifiable computation, underpinned by ZKPs, resolves this impasse. By executing complex models off-chain and proving their correctness on-chain, zkML liberates DeFi from EVM constraints without intermediaries.

The mechanism is elegant: a zero-knowledge proof attests that a computation (eg a volatility forecast) was performed correctly, revealing only the output (eg an optimal fee) and a succinct verification key. The blockchain validates this proof at a fraction of the original computation’s cost, preserving gas efficiency and decentralization.

When applied to DeFi, this approach enables protocols to incorporate arbitrarily complex statistical and decision models while maintaining their security guarantees:

• Unbounded Complexity: Freed from on-chain constraints, protocols can employ sophisticated regression analyses, machine learning models, or any statistical approach that best captures market nuances and dynamics.
• Real-Time Adaptation: Parameters can adjust continuously in response to market conditions rather than waiting for governance votes.
• Gas Efficiency: Verification of a proof is typically orders of magnitude cheaper than running the original computation on-chain.
• Permissionless Execution: Anyone can generate valid proofs if they have the correct model and data—no central authority needed.
• Cryptographic Assurances: Users don’t need to trust that an off-chain system is calculating parameters correctly; the cryptographic proof guarantees correctness.

This approach maintains the core ethos of DeFi—transparency, permissionlessness, and trustlessness—while dramatically expanding what these systems can do. This is not an incremental improvement; it’s a fundamental expansion of what’s possible in decentralized systems and aligns DeFi with traditional finance’s sophistication while preserving trustlessness.

EZKL & Balancer: Bringing Dynamic Fees to DeFi Today

Balancer’s implementation of EZKL to support dynamic fees for their liquidity pools exemplifies this paradigm shift. Balancer recognized that static fees left value on the table and created suboptimal experiences for both users and liquidity providers. Balancer contributors needed a way to make fees responsive to market conditions without compromising the security and decentralization of their protocol.

EZKL made this possible by providing a streamlined system for turning statistical models into verifiable computations. Our expertise in zero-knowledge technology allowed us to create a Dynamic Fee Manager that seamlessly integrates with Balancer’s existing architecture while enabling sophisticated statistical analysis that would be impossible to perform on-chain.

Here’s how our Dynamic Fee Manager operates:

1. Data Ingestion: Historical price data from Chainlink oracles is cached in our PriceCache contract
2. zk Circuit: This data is fed into an EZKL ZK Circuit that uses LASSO regression to predict market volatility
3. Fee Calculation: Based on this volatility prediction, the circuit calculates an optimal fee that balances LP returns with user costs
4. Proof Generation: A cryptographic proof is generated attesting to the correctness of this calculation
5. On-Chain Update: The proof and optimal fee are submitted to the Balancer pool via FeeManager.setStaticSwapFeePercentage

The beauty of this system is that it requires minimal changes to Balancer’s existing infrastructure. We simply utilize the existing fee manager interface but govern it with our verifiable computation system instead of manual governance. The result is a pool that can automatically adjust its fees in response to market conditions:

• When volatility increases, fees rise to compensate LPs for their increased risk
• When markets calm, fees lower to attract more volume and improve capital efficiency
• All changes happen automatically, without governance delays
• Every fee change is backed by a cryptographic proof that anyone can verify
• The core security of the pool is unaffected as there are no changes to the pool code

No governance delays, no third-party oracles dictating terms—just cryptographic math enforcing real-time optimality.

You can see the details of the mechanism in this video.

The Future of Responsive DeFi

The integration of EZKL’s verifiable computation technology with Balancer is the first step in a reimagining of what’s possible in DeFi. Static parameters throughout the ecosystem are ripe for replacement with intelligent, adaptive algorithms that respond to real-world conditions in real-time.

We’re already working with other major protocols to implement verifiable computation across a range of use cases:

• Adaptive Lending: Lending protocols can adjust interest rates based on sophisticated utilization models that adapt to market conditions far beyond the simple curves used today.
• Risk Precision: Collateralization ratios and liquidation thresholds that adapt based on asset volatility, correlation, and market liquidity.
• Smart AMMs: Order book DEXes with sophisticated pricing algorithms that mimic the adaptive strategies used by traditional market makers.
• Portfolio Intelligence: Yield aggregators that use complex statistical models to continuously rebalance positions based on risk-adjusted return predictions.

At EZKL, we believe zk will fundamentally transform what’s possible in permissionless finance. By removing the technical barriers that have limited on-chain computation, we’re enabling a new generation of protocols that combine the best of traditional finance’s sophisticated modeling with DeFi’s transparency and accessibility.

Our mission is to empower developers to bring any statistical model, no matter how complex, into the on-chain world through easy-to-use verifiable computation tools. The result will be a DeFi ecosystem that’s not just more efficient and capital-effective, but more responsive and resilient—a financial system truly built for the digital age.