AsymSpec通过非对称上下文访问，实现了推理加速，准确率高达90%，计算成本降低，值得一看。
AsymSpec提出了一种非对称的推测解码框架，通过轻量级起草者和大型验证者的协同工作，在保持验证稳定性和高起草接受率的同时，实现了推理加速。在四个智能体能力和两个端到端智能体基准测试中，AsymSpec的平均准确率达到了全上下文准确率的90%，同时实现了1.3-1.7倍的吞吐量提升和0.2-0.3倍的计算成本降低。
Agentic LLM pipelines face escalating inference costs as context accumulates across retrieval, tool use, and multi-turn interactions. To control latency, deployments routinely compress inputs, but this degrades task accuracy. Speculative decoding (SD) accelerates generation losslessly, yet it assumes the drafter and verifier share an identical context, preventing SD from resolving the accuracy-overhead trade-off. We propose AsymSpec, an asymmetric speculative decoding framework that breaks this symmetry: a lightweight drafter reads the full input while the large verifier operates on the compressed view. The drafter steers the verifier via a contrastive $δ$-fusion of logits, modulated by a divergence-aware acceptance gate that preserves verification stability and high draft acceptance rates. Evaluated across four agentic capabilities and two end-to-end agent benchmarks, AsymSpec reaches $\approx 90\%$ of full-context accuracy on average, delivering $1.3$--$1.7\times$ throughput speedups at $0.2$--$0.3\times$ the compute cost on isolated text capabilities. These results show that asymmetric context access yields substantial gains precisely when compression discards critical reasoning signals.