arXiv subjects

Iliya Mirzaei

Publications and source records attributed to Iliya Mirzaei.

4 recordsLinked to original sources

arXiv2026

Competition, Collusion, and Corruption: The Spectrum of MEV Attacks on DAG-Based BFT Consensus Protocols

Byzantine Fault-Tolerant (BFT) protocols guarantee safety and liveness despite the malicious failure of nodes. However, they do not prevent adversarial manipulation of transaction order, where the order a proposer assigns diverges from the order in which clients submitted their transactions. Exploiting this discretion for profit is known as maximal extractable value (MEV), and it is intensified in DAG-based BFT protocols, where every replica proposes blocks concurrently rather than routing transactions through a single designated proposer each round. The proliferation of MEV attacks on DAG-based BFT protocols has made the resulting landscape difficult to navigate: attacks are reported individually, on different protocols, and under different metrics, making it unclear whether two attacks differ fundamentally or merely in how they are described. This paper closes that gap by presenting an attack space for MEV on DAG-based BFT protocols, organized around four families: the adversary, the protocol, the target, and the deployment. For each family, we identify the dimensions that shape an attack's impact. Each point in the attack space fixes one value per dimension, thereby representing a distinct, potential MEV attack, which can then be instantiated on a specific DAG-based BFT protocol. We perform a set of experiments, each isolating a single dimension where the protocol permits it, to empirically measure its effect on the success rate of MEV attacks against six production DAG-based BFT protocols. Our experimental evaluation reveals that every protocol we evaluate is vulnerable to at least a subset of the MEV attacks in this space, and that which attacks succeed is mostly dictated by the protocol's own design rather than by attacker effort.

cs.CR↗

arXiv2026

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B

Methods that make a language model plan, criticise and rewrite its own answer, reflect on mistakes, pick the best of several attempts, or debate with copies of itself nearly all make it generate far more text than a single chain of thought. Because generating more text raises accuracy by itself, a gain over one chain of thought does not show the method's idea is what helped. Wang et al. (2024) reported that a simple baseline, sampling the same question repeatedly and keeping the most common answer, often wins once budgets are comparable, but gave point estimates with no confidence intervals or significance tests. We rerun that comparison as a designed experiment: seven methods, open models of 1.5B, 3B and 7B parameters, two mathematics benchmarks, 150 questions each. We count every generated token, including those spent on critiques, reflections, debate turns and checking, and compare each method against repeated sampling at its own measured cost. All 36 comparisons are paired by question, with bootstrap intervals and multiplicity correction. No method is reliably better than repeated sampling at equal cost anywhere. Ten are reliably worse, all of them methods where the model inspects its own output, and all 18 self-inspection comparisons are negative. The two kinds of self-inspection part company as models grow. Choosing stops hurting: taking Best-of-N's eight samples and just counting the most common answer beats letting the model pick by 8.0 and 11.3 points at 1.5B, but only 2.0 and 1.3 at 7B, no longer distinguishable from zero. Rewriting does not recover: Self-Refine and a forced Reflexion stay 3.6 to 10.1 points below baseline at 7B. Reflexion as published never triggered its own retry on the smallest model. It judged itself correct every time and silently became a single chain of thought. We release code, prompts, all generations, and our verification scripts.

cs.CL↗

arXiv2026

Fair on the Surface: Transaction-Ordering Bias and MEV in Mysticeti DAG-based BFT Protocol

Distributed systems deployed in untrustworthy environments agree on a common transaction order through Byzantine fault-tolerant (BFT) consensus protocols, and that order has real financial value in many decentralized applications: whoever influences it can profit at other users' expense, a problem known as maximal extractable value (MEV). Mysticeti is a state-of-the-art DAG-based BFT protocol in which many validators propose blocks in parallel, and the total order is derived from the resulting DAG afterward. Mysticeti is the consensus protocol powering Sui, a production blockchain with a market capitalization of roughly $3 billion, and it is widely believed to order transactions fairly, since many validators propose blocks in parallel and committed transactions are re-sorted by gas price before execution. We show this fairness assumption breaks down in practice, and the effect is already present on Sui's live network. First, when vertices of the committed graph are merged into a single total order, blocks from the same round are sorted by validator index, giving lower-indexed validators a permanent head start. In our evaluation on a 13-validator network with no attacker, the lower-indexed side wins same-round ordering about 89% of the time. Second, the gas-price re-sort intended to remove this bias uses a stable sort, so transactions paying equal fees (common at the reference gas price) retain the original biased order, letting an attacker profit without paying extra. Third, a validator can amplify this advantage by choosing when to stay silent, a fully legitimate action that violates no protocol rule; this raises its ordering win rate above 94%. We measure all three exploitations, verify that Mysticeti otherwise remains resilient below the standard Byzantine fault threshold, and propose a simple fix: replace the validator-index tiebreaker with an unpredictable, per-commit random key.

cs.DC↗

arXiv2026

Stateless Network-Aware Adaptive Bitrate Streaming over IPFS

Modern content delivery is increasingly decentralized, improving availability, cost, and reach for geographically distributed users. The InterPlanetary File System (IPFS) is a promising approach that uses content-based identifiers distributed across a global peer-to-peer network. Although IPFS improves fault tolerance, resilience, and censorship resistance, its unpredictable environment introduces significant performance variability that limits conventional Adaptive Bitrate (ABR) streaming and degrades Quality of Experience (QoE). Recent network-aware ABR solutions address this by incorporating IPFS-specific information into bitrate decisions. However, they rely on maintaining continuously synchronized state across consumers and providers, which can quickly become stale under peer churn, provider migrations, network partitions, and changing content distributions, making existing policies less effective. We investigate whether network-aware ABR can remain effective without synchronized adaptation state, and present a stateless network-aware ABR policy for IPFS-based video streaming. Our approach replaces provider-stateful adaptation with an observation-driven policy that recomputes the bitrate for each segment using only locally observable request-time signals. To preserve adaptation context without provider-side state, the client embeds its adaptation state in HTTP headers, keeping it under client control and carried transparently across requests. By eliminating cross-provider state synchronization, the framework improves robustness to failures and network reconfigurations while simplifying deployment at scale. Early results show the approach maintains high QoE in faulty conditions, improving it by up to roughly 6x over existing solutions. These findings demonstrate that stateless network-aware adaptation provides a practical and scalable foundation for decentralized video delivery.

cs.NI↗