Transformer layer specialization enables fixed-cost long-context retrieval
Researchers have identified a structural pattern in transformer models where semantic understanding concentrates in lower layers while upper layers specialize for prediction. CoMem exploits this division by caching intermediate representations and recomputing only the task-specific upper layers, enabling context windows to scale without proportional memory or compute overhead. The technique, validated on Qwen3-8B through continued training with minimal parameter updates, achieves strong performance on long-context benchmarks while keeping retrieval costs fixed relative to stored context length. This work addresses a fundamental efficiency bottleneck in scaling LLM context windows.
MentionsQwen3-8B · CoMem · RULER · PG19
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