Your question is Ultra-Long Context Attention. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Explain how attention mechanisms scale with sequence length and what architectural modifications you would recommend for ultra-long context windows in large models used by ByteDance.
Discuss computational and memory complexity, quality tradeoffs, and practical implementation considerations. Provide a small PyTorch prototype that compares full attention with an efficient long-context alternative, and define an evaluation plan covering language-model quality, long-range retrieval, throughput, memory, and latency.