arXiv subjects

Aviv Yaish

Publications and source records attributed to Aviv Yaish.

At least 19 recordsLinked to original sources

arXiv2026

Authenticated Data Structures for Dynamic Workloads

We introduce the Huffman-Merkle Tree (HMT), an authenticated data structure (ADS) for dynamic workloads where items may differ in access frequencies, and access frequencies can change over time. An ADS allows proving item membership against a short commitment to a large mutable state, with applications including verifiable storage, Internet transparency services, and blockchains. Optimizing ADS performance under continuously changing access frequencies has not been fully addressed before, neither in theory nor in practice. HMT addresses dynamically changing access skew through two complementary mechanisms. The first is a Huffman-coding-based Merkle-tree layout, with a novel extension to support evolving access frequencies. The second is an elastic tiering regime that partitions items across separate trees, such as hot and cold tiers, with adaptive migration between them. The key insight in this approach is to place frequently accessed items closer to the root, while assigning less frequently accessed items to progressively larger and deeper trees. This reduces the overall frequency-weighted access cost. Our scheme is designed to scale to gigabytes of data spanning millions of items. To handle dynamism efficiently, layout updates are applied in batches, access frequencies are tracked using a count-min sketch, and the system employs a tier-promotion cache while exploring multiple tier-migration policies. We implement HMT and compare it on real-world data with Ethereum's Merkle Patricia Trie (MPT) ADS and its proposed replacement, the Unified Binary Tree (UBT). Our evaluation considers two metrics: the amount of hashing per update and access-weighted membership-proof size. The latter captures both item access cost and frequency. We find that the best HMT policy uses about 2.4x and 0.34x less average hash operations than MPT and UBT respectively, and has 0.18x and 0.55x shorter proofs.

cs.CR↗

arXiv2026

Order Auctions with Private Position Preferences

We study auctions where two positions are sold to unit-demand bidders with private heterogeneous order preferences: some are specialists} who value only the first position, while others are generalists indifferent between the two. First, we consider a first-price rule which allocates the first and second items to the highest and second-highest bidders, respectively. We show that no strategy profile ex-post implements the efficient allocation at every type profile, irrespective of payments, and provide a distribution-free equilibrium welfare guarantee of $\frac{1}{2}$. To augment this result, we prove that for deterministic one-round auctions and discrete bids, the efficient allocation requires each bidder to communicate at least one bit more than its bid's binary representation. We next ask what the same bit accomplishes in winner-pays-bid formats where bidders can also specify specific item preferences. In particular, we show that this strengthens our distribution-free equilibrium welfare guarantee to $1-\frac{1}{e}$. Finally, we discuss the applicability to priority service, blockchain transaction ordering, and cloud compute and artificial intelligence (AI) marketplaces.

cs.GT↗

arXiv2026

Freemium Is All You Need

Free access to a generative AI (GenAI) service requires costly compute, yet can also produce data that improve future service quality. We study a service provider whose users differ in request value and privacy preference, under the constraint that paid requests are kept private, while free requests can be used to improve model quality which otherwise reverts toward a baseline. In particular, we characterize service demand and prove that quality converges to a unique steady state for every stationary service strategy. Next, we analyze optimal pricing and quality policies and show that these can be expressed using two endogenous user value thresholds. For uniform values, we provide a closed-form optimal service strategy and characterize the sufficient conditions for offering free services as dependent on inference cost.

cs.GT↗

arXiv2026

Modern Portfolio Theory in the Crypto-Wilderness

Modern Portfolio Theory (MPT) prescribes how to maximise the return of an asset portfolio for a given level of risk. The optimal trade-off between return and variance defines the efficient frontier. Whether actual cryptoasset portfolios approximate this prescription and whether proximity to the frontier translates into realised performance remain difficult to test at large scale in traditional markets due to their opaque nature and the inaccessibility of data. As we show, public blockchains make these questions measurable: every token transfer is recorded, thus enabling complete portfolio reconstruction for every account at any point in time. We leverage this transparency to reconstruct cryptoasset portfolios for over 116M Ethereum accounts across the full chain history (2015-2025), measure their distance to the constrained efficient frontier, and quantify how deviations translate into realised performance. Here we show that market entry timing, not allocation choice, is the dominant predictor of realised cryptoasset returns. On-chain wealth is highly concentrated and portfolios are pervasively under-diversified, with single-asset holdings accounting for 83.35% of accounts. Two-asset portfolios sit closest to the efficient frontier defined by their held assets, a proximity that reflects the narrowness of their opportunity set rather than deliberate optimisation. Passive market-capitalisation weighting outperforms every MPT optimisation strategy in median realised return, and entry month alone explains 70-79% of the variance in returns, far exceeding the contribution of allocation choice. Mean-variance optimisation therefore appears neither descriptive of observed behaviour nor prescriptively useful in the cryptoasset domain, even if MPT retains its value as a normative benchmark.

cs.CE↗

arXiv2026

Your Loss is My Gain: Low Stake Attacks on Liquid Staking Pools

Permissionless Proof-of-Stake (PoS) economic security is predicated on the high cost of violating consensus safety or liveness. We show that liquid staking introduces additional risks that are not captured by standard PoS economic security arguments. Through an empirical study of Ethereum data, we find that the operational performance of liquid staking pools is positively associated with subsequent normalized liquid staking token (LST) returns. Motivated by this, we present a cross-layer attack: a low-stake adversary can manipulate the consensus protocol to degrade a target pool's performance and take application-layer positions that profit if the market reprices the corresponding \gls{LST} in-line with the historically observed association. To make the consensus layer manipulation concrete, we develop a deep reinforcement learning (DRL) framework to automatically discover attack strategies. Our evaluation shows that the learned strategies can recover near-optimal theoretical attacks and uncover new manipulation behaviors that significantly degrade target pool performance. We further characterize feasible application-layer monetization channels and analyze leveraged shorting in detail using Monte Carlo simulations, showing that such attacks can be profitable with over one-half probability for LSTs of major staking pools. Our findings reveal a previously overlooked attack surface in PoS systems with liquid staking and expose a gap between consensus and economic security.

cs.GT↗

arXiv2026

Perils of Parallelism: Transaction Fee Mechanisms under Execution Uncertainty

Modern blockchains increasingly rely on parallel execution to improve throughput. We show several industry and academic transaction fee mechanisms (TFMs) struggle to simultaneously account for execution parallelism while remaining performant and fair. First, if parallelism affects fees, adversarial protocol manipulations that offset possible benefits to throughput by introducing fake transactions become rational: users can insert functionally useless parallel transactions solely to reduce fees, and schedulers can create useless sequential transactions to increase revenue. Execution contingency, a core feature of expressive programming languages, both exacerbates the aforementioned threats and introduces new ones: (1) users may overpay for unused resources, and (2) scheduler revenue is harmed when reserved scheduling slots go unused due to contingency. We introduce a framework for this challenging setting, and prove an impossibility, highlighting an inherent tension: both parallelism and contingency involve a trade-off between minimizing risks for users and schedulers, as favoring one comes at the expense of the other. To complete the picture, we introduce a fee mechanisms and prove that they achieve the boundaries of this trade-off. Our results provide rigorous foundations for evaluating designs advanced by notable blockchains, such as Sui and Monad.

cs.CR↗

arXiv2025

PoliFi Tokens and the Trump Effect

Cryptoassets launched by political figures, e.g., political finance (PoliFi) tokens, have recently attracted attention. Chief among them are the eponymous tokens backed by the 47th president and first lady of the United States, TRUMPandMELANIA. We empirically analyze both, and study their impact on the broad decentralized finance (DeFi) ecosystem. Via a comparative longitudinal study, we uncover a "Trump Effect": the behavior of these tokens correlates positively with presidential approval ratings, whereas the same tight coupling does not extend to other cryptoassets and administrations. We additionally quantify the ecosystemic impact, finding that the fervor surrounding the two assets was accompanied by capital flows towards associated platforms like the Solana blockchain, which also enjoyed record volumes and fee expenditure.

physics.soc-ph↗

arXiv2025

Persistent BitTorrent Trackers

Private BitTorrent trackers enforce upload-to-download ratios to prevent free-riding, but suffer from three critical weaknesses: reputation cannot move between trackers, centralized servers create single points of failure, and upload statistics are self-reported and unverifiable. When a tracker shuts down, users lose their contribution history and cannot prove their standing to new communities. We address these problems by storing reputation in smart contracts and replacing self-reports with cryptographic attestations. Peers sign receipts for received pieces; the tracker aggregates them via BLS signatures and updates reputation. If a tracker is unavailable, peers fall back to an authenticated distributed hash table (DHT): stored reputation acts as a public key infrastructure (PKI), preserving access control without the tracker. Reputation is portable across tracker failures through single-hop migration in factory-deployed contracts. We also address the privacy implications of publishing public keys and reputations tied to private trackers on a public ledger: we propose ephemeral session keys to prevent linking peer identities, zero-knowledge membership proofs for anonymous DHT participation, and confidential reputation using homomorphic commitments. We formalize the security requirements, prove four security properties under standard cryptographic assumptions, and evaluate a prototype. Measurements show that transfer receipts add less than 5\% end-to-end overhead with typical piece sizes. To minimize signing overhead, we adopt a hybrid signature scheme: ECDSA signs individual piece receipts at transfer time for low per-operation latency, while BLS serves as the overarching scheme, enabling compact aggregation of many receipts into a single proof at report time. This design reduces client-side signing cost by an order of magnitude compared to using BLS throughout.

cs.CR↗

arXiv2025

Inequality in the Age of Pseudonymity

Inequality measures such as the Gini coefficient are used to inform and motivate policymaking, and are increasingly applied to digital platforms. We analyze how measures fare in pseudonymous settings that are common in the digital age. One key challenge of such environments is the ability of actors to create fake identities under fictitious false names, also known as ``Sybils.'' While some actors may do so to preserve their privacy, we show that this can hamper inequality measurements: it is impossible for measures satisfying the literature's canonical set of desired properties to assess the inequality of an economy that may harbor Sybils. We characterize the class of all Sybil-proof measures, and prove that they must satisfy relaxed version of the aforementioned properties. Furthermore, we show that the structure imposed restricts the ability to assess inequality at a fine-grained level. We then apply our results to prove that popular measures are not Sybil-proof, with the famous Gini coefficient being but one example out of many. Finally, we examine dynamics leading to the creation of Sybils in digital and traditional settings.

cs.GT↗

arXiv2025

Airdrops: Giving Money Away Is Harder Than It Seems

Airdrops are a popular mechanism used by blockchain protocols to bootstrap communities, reward early adopters, and decentralize token distribution. Despite their widespread adoption, the effectiveness of airdrops in achieving long-term user engagement and ecosystem growth remains poorly understood. In this paper, we present the first comprehensive empirical study of nine major airdrops across Ethereum and Layer-2 ecosystems. Our analysis reveals that a substantial share of tokens--up to 66% in some cases--are rapidly sold, often in recipients' first post-claim transaction. We show that this behavior is largely driven by "airdrop farmers," who strategically optimize eligibility criteria to extract value without contributing meaningfully to the ecosystem. We complement our quantitative findings with a case study of the Arbitrum airdrop, illustrating how short-term activity spikes fail to translate into sustained user involvement. Based on these results, we discuss common design pitfalls--such as Sybil vulnerability, poor incentive alignment, and governance token misuse--and propose actionable guidelines for designing more effective airdrop strategies.

cs.CR↗

arXiv2025

Advancing AI Research Assistants with Expert-Involved Learning

Large language models (LLMs) and large multimodal models (LMMs) promise to accelerate biomedical discovery, yet their reliability remains unclear. We introduce ARIEL (AI Research Assistant for Expert-in-the-Loop Learning), an open-source evaluation and optimization framework that pairs a curated multimodal biomedical corpus with expert-vetted tasks to probe two capabilities: full-length article summarization and fine-grained figure interpretation. Using uniform protocols and blinded PhD-level evaluation, we find that state-of-the-art models generate fluent but incomplete summaries, whereas LMMs struggle with detailed visual reasoning. We later observe that prompt engineering and lightweight fine-tuning substantially improve textual coverage, and a compute-scaled inference strategy enhances visual question answering. We build an ARIEL agent that integrates textual and visual cues, and we show it can propose testable mechanistic hypotheses. ARIEL delineates current strengths and limitations of foundation models, and provides a reproducible platform for advancing trustworthy AI in biomedicine.

cs.AI↗

arXiv2025

Insecurity Through Obscurity: Veiled Vulnerabilities in Closed-Source Contracts

Most blockchains cannot hide the binary code of programs (i.e., smart contracts) running on them. To conceal proprietary business logic and to potentially deter attacks, many smart contracts are closed-source and in many cases exhibit code obfuscation, either intentionally introduced to hide internal logic or unintentionally produced by optimizations. However, we demonstrate that such obfuscation can obscure critical vulnerabilities rather than enhance security, a phenomenon known as insecurity through obscurity. To systematically analyze these risks on a large scale, we present SKANF, a novel EVM bytecode analysis tool tailored for closed-source and obfuscated contracts. SKANF combines control-flow deobfuscation with symbolic execution based on historical transactions to identify and exploit asset management vulnerabilities. Our evaluation on real-world Maximal Extractable Value (MEV) bots reveals that SKANF detects vulnerabilities in 1,046 contracts and successfully generates exploits for 394 of them, with potential losses of \$10.6M. Additionally, we uncover 104 real-world MEV bot attacks that collectively resulted in \$2.76M in losses.

cs.CR↗

arXiv2025

$Prooφ$: A ZKP Market Mechanism

Zero-knowledge proofs (ZKPs) are computationally demanding to generate. Their importance for applications like ZK-Rollups has prompted some to outsource ZKP generation to a market of specialized provers. However, existing market designs either do not fit the ZKP setting or lack formal description and analysis. In this work, we propose a formal ZKP market model that captures the interactions between users submitting ZKP tasks and provers competing to generate proofs. Building on this model, we introduce $Prooφ$, an auction-based ZKP market mechanism. We prove that $Prooφ$ is incentive compatible for users and provers, and budget balanced. We augment $Prooφ$ with system-level designs to address the practical challenges of our setting, such as Sybil attacks, misreporting of prover capacity, and collusion. We analyze our system-level designs and show how they can mitigate the various security concerns.

cs.GT↗

arXiv2024

TierDrop: Harnessing Airdrop Farmers for User Growth

Blockchain platforms attempt to expand their user base by awarding tokens to users, a practice known as issuing airdrops. Empirical data and related work implies that previous airdrops fall short of their stated aim of attracting long-term users, partially due to adversarial farmers who game airdrop mechanisms and receive an outsize share of rewards. In this work, we argue that given the futility of fighting farmers, the airdrop business model should be reconsidered: farmers should be harnessed to generate activity that attracts real users, i.e., strengthens network effects. To understand the impact of farmers on airdrops, we analyze their performance in a market inhabited by two competing platforms and two tiers of users: real users and farmers. We show that counterintuitively, farmers sometimes represent a necessary evil-it can be revenue-optimal for airdrop issuers to give some tokens to farmers, even in the hypothetical case where platforms could costlessly detect and banish all farmers. Although we focus on airdrops, our results generally apply to activity-based incentive schemes.

cs.GT↗

arXiv2024

Discrete & Bayesian Transaction Fee Mechanisms

Cryptocurrencies employ auction-esque transaction fee mechanisms (TFMs) to allocate transactions to blocks, and to determine how much fees miners can collect from transactions. Several impossibility results show that TFMs that satisfy a standard set of "good" properties obtain low revenue, and in certain cases, no revenue at all. In this work, we circumvent previous impossibilities by showing that when desired TFM properties are reasonably relaxed, simple mechanisms can obtain strictly positive revenue. By discretizing fees, we design a TFM that satisfies the extended TFM desiderata: it is dominant strategy incentive-compatible (DSIC), myopic miner incentive-compatible (MMIC), side-contract-proof (SCP) and obtains asymptotically optimal revenue (i.e., linear in the number of allocated bids), and optimal revenue when considering separable TFMs. If instead of discretizing fees we relax the DSIC and SCP properties, we show that Bitcoin's TFM, after applying the revelation principle, is Bayesian incentive-compatible (BIC), MMIC, off-chain-agreement (OCA) proof, and approximately revenue-optimal. We reach our results by characterizing the class of multi-item OCA-proof mechanisms, which may be of independent interest.

cs.GT↗

arXiv2024

Strategic Vote Timing in Online Elections With Public Tallies

We study the effect of public tallies on online elections, in a setting where voting is costly and voters are allowed to strategically time their votes. The strategic importance of choosing \emph{when} to vote arises when votes are public, such as in online event scheduling polls (e.g., Doodle), or in blockchain governance mechanisms. In particular, there is a tension between voting early to influence future votes and waiting to observe interim results and avoid voting costs if the outcome has already been decided. Our study draws on empirical findings showing that "temporal" bandwagon effects occur when interim results are revealed to the electorate: late voters are more likely to vote for leading candidates. To capture this phenomenon, we analyze a novel model where the electorate consists of informed voters who have a preferred candidate, and uninformed swing voters who can be swayed according to the interim outcome at the time of voting. In our main results, we prove the existence of equilibria where both early and late voting occur with a positive probability, and we characterize conditions that lead to the appearance of "last minute" voting behavior, where all informed voters vote late.

cs.GT↗

arXiv2024

Scheduling With Time Discounts

We study a \emph{financial} version of the online problem of scheduling weighted packets with deadlines. The main novelty is that, while prior works assume packets have \emph{fixed} weights, we consider packets with \emph{time-decaying} values. Such considerations are natural in financial environments, where the present value of future actions may be discounted. We analyze the competitive ratios of scheduling algorithms under a range of discount rates encompassing the traditional undiscounted case where weights are fixed (i.e., a discount rate of 1), the fully discounted myopic case (i.e., a rate of 0), and those in between. We show how existing methods from the literature perform suboptimally in the more general discounted setting. Notably, we devise a novel memoryless deterministic algorithm, and prove that for discount factors up to $\approx 0.77$, it guarantees the best competitive ratio attainable by deterministic algorithms. Moreover, we develop a randomized algorithm and prove that it outperforms the best possible deterministic algorithm for any discount rate.

cs.GT↗

arXiv2024

Barriers to Collusion-resistant Transaction Fee Mechanisms

To allocate transactions to blocks, cryptocurrencies use an auction-like transaction fee mechanism (TFM). A conjecture of Roughgarden [44] asks whether there is a TFM that is incentive compatible for both the users and the miner, and is also resistant to off-chain agreements (OCAs) between these parties, a collusion notion that captures the ability of users and the miner to jointly deviate for profit. The work of Chung and Shi [12] tackles the problem using the different collusion resistance notion of side-channel proofness (SCP), and shows an impossibility given this notion. We show that OCA-proofness and SCP are different, with SCP being strictly stronger. We then fully characterize the intersection of deterministic dominant strategy incentive-compatible (DSIC) and OCA-proof mechanisms, as well as deterministic MMIC and OCA-proof ones, and use this characterization to show that only the trivial mechanism is DSIC, myopic miner incentive-compatible (MMIC) and OCA-proof. We also show that a randomized mechanism can be at most 0.842-efficient in the worst case, and that the impossibility of a non-trivial DSIC, MMIC and OCA-proof extends to a couple of natural classes of randomized mechanisms.

cs.GT↗