paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
NO

Naoaki Okazaki

ICML 2026×4ACL 2026×2对齐与偏好推理检索与知识可解释性多模态
新星分资深
23
一作贡献0
核心/参与10
方向广度13
合作网络3
0 一作 · 6 篇 · ICML 2026 / 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

6
作者位次 #3/3ICML 2026Poster· Deep Learning / Large Language Models

Aligning Tree-Search Policies with Fixed Token Budgets in Test-Time Scaling of LLMs

Sora Miyamoto · Daisuke Oba · Naoaki Okazaki

推理对齐与偏好检索与知识OpenReview
作者位次 #5/5ACL 2026Findings· Findings of ACL

E xa GPT : Example-Based Machine-Generated Text Detection for Human Interpretability

Ryuto Koike · Masahiro Kaneko · Ayana Niwa · Preslav Nakov · Naoaki Okazaki

摘要

Detecting texts generated by Large Language Models (LLMs) could cause grave mistakes due to incorrect decisions, such as undermining student’s academic dignity. LLM text detection thus needs to ensure the interpretability of the decision, which can help users judge how reliably correct its predic...

评测与基准对齐与偏好可解释性OpenReview 项目页
作者位次 #2/2ACL 2026Findings· Findings of ACL

From Interpretability to Performance: Optimizing Retrieval Heads for Long-Context Language Models

Youmi Ma · Naoaki Okazaki

摘要

Advances in mechanistic interpretability have identified special attention heads, known as retrieval heads, that are responsible for retrieving information from the context. However, the role of these retrieval heads in improving model performance remains unexplored. This work investigates whethe...

可解释性检索与知识效率与系统OpenReview 项目页

常见合作者