KUET at StanceNakba Shared Task: StanceMoE: Mixture-of-Experts Architecture for Stance Detection

This is a 5-day-old shared task submission with zero stars, forks, or usage signals. The project is a paper-derived reference implementation, not a production tool or library. While the MOE architecture for stance detection represents a reasonable combination of known techniques (transformer backbone + mixture-of-experts routing for multi-signal fusion), it addresses a narrow NLP task (geopolitical actor stance) with no apparent ecosystem adoption or independent replication. The novelty is in architecture design for a specific task, not in fundamental algorithmic breakthrough. FRONTIER RISK is high because: (1) stance detection is a well-studied NLP task that frontier labs (Anthropic, OpenAI) already handle via their base models, (2) MOE architectures are increasingly standard components in frontier model design, (3) fine-tuning or prompting their existing models would be a straightforward alternative to adopting this specialist tool. DEFENSIBILITY is minimal because the project has no users, no independent validation outside the shared task, and replicating the MoE design requires only standard toolkit knowledge. The shared task context suggests this is academic validation work, not production infrastructure. No evidence of CLI tools, APIs, or composable modules that would increase defensibility.