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Tianyu Liu

ACL 2026×2ICLR 2026效率与系统代码可解释性评测与基准检索与知识
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一作贡献30
核心/参与17
方向广度13
合作网络12
1 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

3
作者位次 #4/6ICLR 2026Poster· Deep Learning / Generative Models and Autoencoders

SpecBranch: Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch Parallelism

Yuhao Shen · Junyi Shen · Quan Kong · Tianyu Liu · Yao Lu · Cong Wang

效率与系统代码OpenReview
作者位次 #2/7ACL 2026Poster· Natural Language Processing

Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism

Yuhao Shen · Tianyu Liu · Junyi Shen · Jinyang Wu · Quan Kong · Huan Li · Cong Wang

摘要

Parallel Speculative Decoding (PSD) accelerates traditional Speculative Decoding (SD) by overlapping draft generation with verification. However, it remains hampered by two fundamental challenges: (1) a theoretical speedup ceiling dictated by the speed ratio between the draft and target models, a...

效率与系统代码可解释性OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

L ogit S pec: Accelerating Retrieval-based Speculative Decoding via Next Next Token Speculation

Tianyu Liu · Qitan Lv · Hao Li · Xing Gao · Xiao Sun · Xiaoyan Sun

摘要

Speculative decoding (SD), where a small draft model is employed to propose draft tokens in advance and then the target model validates them in parallel, has emerged as a promising technique for LLM inference acceleration. Many endeavors to improve SD are to eliminate the need for a draft model a...

效率与系统评测与基准检索与知识OpenReview 项目页

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