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Wei Wu

ACL 2026×2ICLR 2026×2AAAI 2026可解释性推理代码理论与学习动力学效率与系统
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0 一作 · 5 篇 · ACL 2026 / ICLR 2026 / AAAI 2026

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2026 顶会论文

5
作者位次 #3/4ICLR 2026Poster· Deep Learning / Generative Models and Autoencoders

ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding

Jia-Nan Li · Jian Guan · Wei Wu · Chongxuan Li

作者位次 #5/6ACL 2026Poster· Natural Language Processing

Every Token Counts: Generalizing 16 M Ultra-Long Context in Large Language Models

Xiang Hu · Zhanchao Zhou · Ruiqi Liang · Zehuan Li · Wei Wu · Jianguo Li

摘要

This work explores efficient ultra-long context modeling. We posit that an effective solution requires three fundamental properties: sparsity, random-access flexibility, and length generalization. To achieve this, we leverage Hierarchical Sparse Attention (HSA), a novel attention mechanism that s...

效率与系统长上下文与架构检索与知识OpenReview 项目页
作者位次 #4/5ACL 2026Poster· Natural Language Processing

From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Jia-Nan Li · Jian Guan · Songhao Wu · Wei Wu · Rui Yan

摘要

Large language models (LLMs) have traditionally been aligned through one-size-fits-all approaches that assume uniform human preferences, fundamentally overlooking the diversity in user values and needs. This paper introduces a comprehensive framework for scalable personalized alignment of LLMs. W...

对齐与偏好推理可解释性OpenReview 项目页

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