AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs
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...
| Authors | Sheng Liang, Yongyue Zhang, Nathanael Brian et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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