arXiv subjects

Ujval Misra

Publications and source records attributed to Ujval Misra.

3 recordsLinked to original sources

arXiv2024

PROF: Protected Order Flow in a Profit-Seeking World

Users of decentralized finance (DeFi) applications face significant risks from adversarial actions that manipulate the order of transactions to extract value from users. Such actions -- an adversarial form of what is called maximal-extractable value (MEV) -- impact both individual outcomes and the stability of the DeFi ecosystem. MEV exploitation, moreover, is being institutionalized through an architectural paradigm known Proposer-Builder Separation (PBS). This work introduces a system called PROF (PRotected Order Flow) that is designed to limit harmful forms of MEV in existing PBS systems. PROF aims at this goal using two ideas. First, PROF imposes an ordering on a set ("bundle") of privately input transactions and enforces that ordering all the way through to block production -- preventing transaction-order manipulation. Second, PROF creates bundles whose inclusion is profitable to block producers, thereby ensuring that bundles see timely inclusion in blocks. PROF is backward-compatible, meaning that it works with existing and future PBS designs. PROF is also compatible with any desired algorithm for ordering transactions within a PROF bundle (e.g., first-come, first-serve, fee-based, etc.). It executes efficiently, i.e., with low latency, and requires no additional trust assumptions among PBS entities. We quantitatively and qualitatively analyze incentive structure of PROF, and its utility to users compared with existing solutions. We also report on inclusion likelihood of PROF transactions, and concrete latency numbers through our end-to-end implementation.

cs.CR↗

arXiv2024

Specular: Towards Secure, Trust-minimized Optimistic Blockchain Execution

An optimistic rollup (ORU) scales a blockchain's throughput by delegating computation to an untrusted remote chain (L2), refereeing any state claim disagreements between mutually distrusting L2 operators via an interactive dispute resolution protocol. State-of-the-art ORUs employ a monolithic dispute resolution protocol that tightly couples an L1 referee with a specific L2 client binary--oblivious to the system's higher-level semantics. We argue that this approach (1) magnifies monoculture failure risk, by precluding trust-minimized and permissionless participation using operator-chosen client software; (2) leads to an unnecessarily large and difficult-to-audit TCB; and, (3) suffers from a frequently-triggered, yet opaque upgrade process--both further increasing auditing overhead, and broadening the governance attack surface. To address these concerns, we outline a methodology for designing a secure and resilient ORU with a minimal TCB, by facilitating opportunistic 1-of-N-version programming. Due to its unique challenges and opportunities, we ground this work concretely in the context of the Ethereum ecosystem--where ORUs have gained significant traction. Specifically, we design a semantically-aware proof system, natively targeting the EVM and its instruction set. We present an implementation in a new ORU, Specular, that opportunistically leverages Ethereum's existing client diversity with minimal source modification, demonstrating our approach's feasibility.

cs.CR↗

arXiv2021

C5T5: Controllable Generation of Organic Molecules with Transformers

Methods for designing organic materials with desired properties have high potential impact across fields such as medicine, renewable energy, petrochemical engineering, and agriculture. However, using generative modeling to design substances with desired properties is difficult because candidate compounds must satisfy multiple constraints, including synthetic accessibility and other metrics that are intuitive to domain experts but challenging to quantify. We propose C5T5, a novel self-supervised pretraining method that enables transformers to make zero-shot select-and-replace edits, altering organic substances towards desired property values. C5T5 operates on IUPAC names -- a standardized molecular representation that intuitively encodes rich structural information for organic chemists but that has been largely ignored by the ML community. Our technique requires no edited molecule pairs to train and only a rough estimate of molecular properties, and it has the potential to model long-range dependencies and symmetric molecular structures more easily than graph-based methods. C5T5 also provides a powerful interface to domain experts: it grants users fine-grained control over the generative process by selecting and replacing IUPAC name fragments, which enables experts to leverage their intuitions about structure-activity relationships. We demonstrate C5T5's effectiveness on four physical properties relevant for drug discovery, showing that it learns successful and chemically intuitive strategies for altering molecules towards desired property values.

cs.LG↗