The Future of AMMs is MEV-Resistant Curves

The Problem: LPs are passive price takers, vulnerable to JIT liquidity attacks and sandwich bots. The Solution: Programmable hooks let pools enforce custom logic for swaps, deposits, and withdrawals.

• Dynamic Fees can spike during volatile periods to deter arbitrage bots.
• Time-Weighted Liquidity prevents JIT attacks by enforcing minimum deposit durations.
• Custom Oracles can bypass public mempools, routing orders directly to builders.

The Problem: Stable pools bleed value during depegs; volatile pools suffer massive impermanent loss. The Solution: An internal oracle and exponential moving average adjust the curve shape in real-time.

• Internal Oracle reduces reliance on external price feeds, a common MEV vector.
• Auto-Repegging dynamically concentrates liquidity around the current price, capturing fees from volatility.
• Low-Slippage Core acts as a built-in, MEV-resistant on-chain market maker for large trades.

The Problem: Uniswap V3 positions are static and gas-inefficient, requiring constant manual management. The Solution: A global liquidity curve with continuous concentration, managed by a single smart contract.

• Gas Efficiency: One contract manages all pools, reducing LP management costs by ~90%.
• MEV Resistance: Atomic arbitrage across the entire curve reduces profitable sandwich attack windows.
• Passive Concentration: LPs earn fees across a range automatically, no more manual rebalancing.

The Problem: Active LP management (e.g., Uniswap V3) is complex and exposes strategies to front-running. The Solution: Automate and privatize strategy execution via intent-based solvers and confidential compute.

• Solver Network: Off-chain solvers compete to optimize rebalancing, paying users for order flow.
• Strategy Privacy: Execution logic is hidden until settlement, preventing front-running.
• Position NFT: Encapsulates strategy and yield, making LP positions composable DeFi assets.

Traditional constant-product AMMs broadcast price updates, creating a predictable profit opportunity for arbitrage bots. This results in loss-versus-rebalancing (LVR), a multi-billion dollar annual tax on liquidity providers.

• LVR is estimated at ~$1B+ annually on major DEXs.
• Creates a perverse incentive where LPs compete against the very users they serve.
• Front-running and sandwich attacks are a direct consequence of this transparent pricing model.

Separate trade execution from price discovery. Users submit encrypted orders that are settled in a batch, neutralizing front-running and batching arbitrage for fairer prices.

• Eliminates on-chain latency races and predictable price updates.
• Enables cross-domain MEV capture (e.g., UniswapX, CowSwap).
• Batches compress gas costs and can use centralized sequencing for finality (see: Espresso, Astria).

The next evolution moves beyond static formulas. Curves become stateful functions that adapt based on market conditions and order flow, making extraction strategies computationally infeasible.

• Curves can incorporate time delays or randomness (Keller & Martin research).
• Privacy-preserving computation (ZKPs, TEEs) hides the pricing function itself.
• Shifts advantage from searchers with fast hardware back to LPs and users.

A canonical example of the intent-based, MEV-resistant architecture. It outsources routing to a network of fillers competing in off-chain auctions.

• Users get price guarantees before signing, eliminating slippage uncertainty.
• Fillers absorb MEV risk and compete on price, creating a negative-cost swap for users.
• Proves the model at scale, having settled billions in volume since launch.

MEV-resistant architectures don't just protect existing liquidity; they enable new forms of it. Cross-chain intent settlement (via Across, LayerZero) turns liquidity into a fungible, chain-agnostic resource.

• TVL becomes portable and not siloed per chain.
• Solver networks become the new liquidity aggregators, not AMM pools.
• Reduces fragmentation and improves capital efficiency across the entire ecosystem.

The core design principle shifts from maximal transparency to strategic opacity. Your system's value is now defined by what it hides from adversaries.

• Priority #1: Break the predictable price update cycle.
• Integrate with a solver network or build a commit-reveal mechanism.
• Audit for new failure modes: reliance on sequencers, filler centralization, and cryptographic assumptions.