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Tianyi Wu

ACL 2026×2推理可解释性检索与知识效率与系统评测与基准
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #3/11ACL 2026Poster· Natural Language Processing

R e P robe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models

Jingwei Ni · Ekaterina Fadeeva · Tianyi Wu · Mubashara Akhtar · Jiaheng Zhang · Elliott Ash · Markus Leippold · Timothy Baldwin · See-Kiong Ng · Artem Shelmanov · Mrinmaya Sachan

摘要

LLMs can solve complex tasks by generating long, multi-step reasoning chains. Test-time scaling (TTS) can further improve LLM performance by sampling multiple variants of intermediate reasoning steps, verifying their correctness, and strategically choosing the best steps for continuation. However...

推理可解释性检索与知识OpenReview 项目页
作者位次 #11/17ACL 2026Workshop· ACL System Demonstrations

T hink B ooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning

Vladislav Smirnov · Quang-Chieu Nguyen · Sergey Senichev · Minh Ngoc Ta · Ekaterina Fadeeva · Artem Vazhentsev · Daria Galimzianova · Nikolai Rozanov · Viktor Mazanov · Jingwei Ni · Tianyi Wu · Igor Kiselev · Mrinmaya Sachan · Iryna Gurevych · Preslav Nakov · Timothy Baldwin · Artem Shelmanov

摘要

Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.g., via multi-sample generation and verifier-based reranking. Existing TTC scaling strategies and reasoning scorers remain fra...

推理效率与系统评测与基准OpenReview 项目页

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