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Yingqian Cui

ACL 2026×2NeurIPS 2026推理可解释性数据与预训练效率与系统对齐与偏好
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1 一作 · 3 篇 · ACL 2026 / NeurIPS 2026

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2026 顶会论文

3
作者位次 #5/9ACL 2026Poster· Natural Language Processing

Retrieval Heads are Dynamic

Yuping Lin · Zitao Li · Yue Xing · Pengfei He · Yingqian Cui · Yaliang Li · Bolin Ding · Jingren Zhou · Jiliang Tang

摘要

Recent studies have identified “retrieval heads” in Large Language Models (LLMs) responsible for extracting information from input contexts. However, prior works largely rely on static statistics aggregated across datasets, identifying heads that perform retrieval on average. This perspective ove...

推理可解释性数据与预训练OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning

Yingqian Cui · Zhenwei Dai · Pengfei He · Bing He · Hui Liu · Zhan Shi · Xianfeng Tang · Jingying Zeng · Suhang Wang · Yue Xing · Jiliang Tang · Benoit Dumoulin

摘要

Large Language Models (LLMs) have made strong progress in reasoning. To enhance the reasoning performance, a common inference-time approach is tree-based search, which decomposes the reasoning process into multiple steps, expands multiple reasoning paths, and uses reward models to prune and selec...

推理效率与系统对齐与偏好OpenReview 项目页

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