paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
SY

Sho Yokoi

ACL 2026×2强化学习与后训练评测与基准数据与预训练长上下文与架构对齐与偏好
新星分资深
10
一作贡献0
核心/参与0
方向广度10
合作网络0
0 一作 · 2 篇 · ACL 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

2
作者位次 #5/5ACL 2026Poster· Natural Language Processing

Why Mean Pooling Works: Quantifying Second-Order Collapse in Text Embeddings

Tomomasa Hara · Hiroto Kurita · Masaaki Imaizumi · Kentaro Inui · Sho Yokoi

摘要

For constructing text embeddings, mean pooling, which averages token embeddings, is the standard approach. This paper examines whether mean pooling actually works well in real models. First, we note that mean pooling can collapse information beyond the first-order statistics of the token embeddin...

强化学习与后训练评测与基准数据与预训练OpenReview 项目页
作者位次 #4/4ACL 2026Findings· Findings of ACL

Timesteps of Mamba Align with Human Reading Times

Yuji Yamamoto · Shinnosuke Isono · Yoshinobu Kawahara · Sho Yokoi

摘要

This study demonstrates an alignment of per-word processing time in a popular state-space language model Mamba and human readers. In Mamba, the recurrent state transition at each layer conceptually takes some duration of time, the discretization timestep \Delta_t , determined dynamically in respo...

长上下文与架构对齐与偏好可解释性OpenReview 项目页

常见合作者