arXiv subjects

Elliot Hill

Publications and source records attributed to Elliot Hill.

2 recordsLinked to original sources

arXiv2026

Gasp: A DeFi Application Specic Rollup as a Consolidation Layer for All Assets

Gasp is a decentralized exchange designed as an application-specific Layer 2 (L2) rollup with omnichain connectivity, leveraging EigenLayer's restaked ETH for computation correctness and finalization. With a goal of being a consolidation layer for all crypto assets, the Gasp platform employs optimistic rollup technology to facilitate gas-free, native cross-chain swaps without reliance on traditional bridges, ensuring tokens retain their original L1 grade security. By combining an app-chain architecture with escape hatch mechanisms, Gasp guarantees withdrawal, while MEV minimization through Themis architecture reduces value extraction risks. Gasp's proof-of-liquidity framework unlocks staked liquidity, enhancing capital efficiency and liquidity depth by integrating staking with liquidity provisioning. Additionally, the protocol introduces a time-based reward mechanism, incentivizing long-term liquidity commitment via an asymptotic reward curve. This paper examines the current challenges in cross-chain communication, delineates Gasp's architectural innovations and security guarantees, and examines novel approaches to optimizing DeFi ecosystems.

cs.CR↗

arXiv2023

Scaling Monte-Carlo-Based Inference on Antibody and TCR Repertoires

Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to compute each model's partition function ($Z$), the normalization constant required for calculating the probability that the model will generate a given sequence. Specifically, the method required generating approximately $10^{10}$ sequences via Monte-Carlo simulations for each model. This is impractical for large numbers of models. Here we propose an alternative method that requires estimating $Z$ this way for only a few models: it then uses these expensive estimates to estimate $Z$ more efficiently for the remaining models. We demonstrate that this new method enables the generation of accurate estimates for 27 models using only three expensive estimates, thereby reducing the computational cost by an order of magnitude. Importantly, this gain in efficiency is achieved with only minimal impact on classification accuracy. Thus, this new method enables larger-scale investigations in computational immunology and represents a useful contribution to energy-based modeling more generally.

q-bio.QM↗