Universal YOCO: Recursive Depth Scaling for Efficient Test-Time Compute in Reasoning LLMs

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Combines YOCO decoder-decoder architecture with recursive computation to address the KV cache inflation problem as model depth scales during test-time reasoning. Standard Transformers struggle to scale inference compute efficiently — conventional looping inflates KV cache proportionally. YOCO-U achieves a synergistic effect where recursive depth is added without proportional memory cost, directly addressing the compute bottleneck in reasoning model deployment.