How do long-context transformers degrade in performance as sequence length increases?

Performance degrades due to attention dilution, memory bottlenecks, and optimization instability.

As sequence length grows, attention becomes noisy and diluted, making it harder for the model to focus on relevant tokens. Positional encoding extrapolation errors, gradient instability, and KV cache saturation also degrade performance. Solutions include RoPE scaling, chunking, and memory-augmented transformers.