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    <abstract>While draft-model-free speculative decoding offers a promising path to efficient LLM inference, it is frequently constrained by stale draft candidates and the high computational cost of the verification. To address these challenges, we propose ECHO, a hierarchical dual-loop framework that exploits the functional asymmetry between LLM layers. Leveraging the high discriminative efficiency of early layers and the authoritative distribution of final layers, ECHO bifurcates inference into a high-frequency inner loop and a low-frequency outer loop. Within the inner loop, early-layer bonus logits drive rapid, multi-step draft-tree exploration at a minimal cost. Simultaneously, the outer loop performs authoritative full-model verification through a state-reuse mechanism. Crucially, the outer loop also utilizes final-layer bonus logits to correct existing paths and supplement the tree with high-confidence candidates for subsequent cycles. Experimental results across diverse benchmarks demonstrate that ECHO significantly boosts mean accepted tokens and achieves a 2.4\times to 2.9\times speedup, outperforming existing state-of-the-art baselines with negligible engineering overhead and no extra deployment parameters, albeit with a one-shot fine-tuning dependency for optimal acceleration. The code is available at https://github.com/whucs21Mzy/ECHO.</abstract>
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%0 Conference Proceedings
%T ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding
%A Ma, Ziyang
%A Zhang, Zihong
%A Li, Zuchao
%A Zhang, Lefei
%A Qi, Baoyuan
%A Li, Siqi
%A Yu, Simin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ma-etal-2026-echo
%X While draft-model-free speculative decoding offers a promising path to efficient LLM inference, it is frequently constrained by stale draft candidates and the high computational cost of the verification. To address these challenges, we propose ECHO, a hierarchical dual-loop framework that exploits the functional asymmetry between LLM layers. Leveraging the high discriminative efficiency of early layers and the authoritative distribution of final layers, ECHO bifurcates inference into a high-frequency inner loop and a low-frequency outer loop. Within the inner loop, early-layer bonus logits drive rapid, multi-step draft-tree exploration at a minimal cost. Simultaneously, the outer loop performs authoritative full-model verification through a state-reuse mechanism. Crucially, the outer loop also utilizes final-layer bonus logits to correct existing paths and supplement the tree with high-confidence candidates for subsequent cycles. Experimental results across diverse benchmarks demonstrate that ECHO significantly boosts mean accepted tokens and achieves a 2.4\times to 2.9\times speedup, outperforming existing state-of-the-art baselines with negligible engineering overhead and no extra deployment parameters, albeit with a one-shot fine-tuning dependency for optimal acceleration. The code is available at https://github.com/whucs21Mzy/ECHO.
%U https://aclanthology.org/2026.emnlp-main.177/
%P 4047-4067

Markdown (Informal)

[ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding](https://aclanthology.org/2026.emnlp-main.177/) (Ma et al., EMNLP 2026)

• ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding (Ma et al., EMNLP 2026)

ACL

• Ziyang Ma, Zihong Zhang, Zuchao Li, Lefei Zhang, Baoyuan Qi, Siqi Li, and Simin Yu. 2026. ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 4047–4067, Budapest, Hungary. Association for Computational Linguistics.