Muhammad Zeeshan Akram

arXiv2026

Engineering Lessons from Authorized YouTube-to-Blockchain Content Replication at Scale

We present an experience report on YouTube-Synch, a production system that replicates content from 10,000+ creator-authorized YouTube channels to a blockchain-based decentralized platform. Although replication is authorized, the system must still defeat YouTube's anti-automation defenses - API quota restrictions (10,000 units/day), IP-based rate limiting, behavioral bot detection, and OAuth token lifecycle policies - which do not distinguish authorized bulk access from abuse. Our central observation is a coupled-defense phenomenon: these protection layers are not independent, so circumventing one (e.g., API quotas) silently activates another (e.g., OAuth token expiration), producing cascading, delayed failures. We ground this in three production incidents with concrete impact - 28 duplicate on-chain objects from a database throughput failure, 10,000+ channels lost to a single OAuth mass-expiration, and 719 daily errors from queue pollution - observed over 15 releases and 3.5 years of operation. We further argue, from the system's own concurrency and rate parameters alone, that detection-driven anti-bot measures impose a download-bound throughput ceiling on the order of 10^3 videos/day per instance - at or below the steady-state demand of 10,000 channels. This analysis shows why priority-based triage and horizontal scaling are structural necessities rather than optimizations, and explains the survivability-over-throughput tradeoff that drove a 25x reduction in download concurrency. We detail the resulting architecture - a four-stage DAG pipeline, a Write-Ahead-Log fault-tolerance model with cross-system state reconciliation, and a trust-minimized ownership-verification protocol that eliminates OAuth - and distill design principles that generalize to extraction against other heavily-defended centralized platforms.

cs.CR↗

arXiv2025

Domain Specific Benchmarks for Evaluating Multimodal Large Language Models

Large language models (LLMs) are increasingly being deployed across disciplines due to their advanced reasoning and problem solving capabilities. To measure their effectiveness, various benchmarks have been developed that measure aspects of LLM reasoning, comprehension, and problem-solving. While several surveys address LLM evaluation and benchmarks, a domain-specific analysis remains underexplored in the literature. This paper introduces a taxonomy of seven key disciplines, encompassing various domains and application areas where LLMs are extensively utilized. Additionally, we provide a comprehensive review of LLM benchmarks and survey papers within each domain, highlighting the unique capabilities of LLMs and the challenges faced in their application. Finally, we compile and categorize these benchmarks by domain to create an accessible resource for researchers, aiming to pave the way for advancements toward artificial general intelligence (AGI)

cs.LG↗