activity
20232026
most citedSparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers

3 citations · 7 across the 14 of their papers we have counts for

collaborators

14 papers

cs.SD2026

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Ziyang Ma, Zhikang Niu, Wenming Tu +30

We introduce AuK, an open-source foundational model that unifies speech generation and editing through a common interface of natural-language instructions and audio context. To sup…

cs.AI2026

Xetrieval: Mechanistically Explaining Dense Retrieval

Zhixin Cai, Jun Bai, Yang Liu +7

Explaining why dense retrievers assign high relevance scores remains challenging because retrieval decisions are made through opaque high-dimensional embeddings. Existing explanati…

cs.LG2026

$OneMillion-Bench: How Far are Language Agents from Human Experts?

Qianyu Yang, Yang Liu, Jiaqi Li +19

As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured…

cs.HC2026

NarrativeLoom: Enhancing Creative Storytelling through Multi-Persona Collaborative Improvisation

Yuxi Ma, Yongqian Peng, Fengyuan Yang +5

Large Language Models show promise for AI-assisted storytelling, yet current tools often generate predictable, unoriginal narratives. To address this limitation, we present Narrati…

cs.LG2025

Adaptive Preference Optimization with Uncertainty-aware Utility Anchor

Xiaobo Wang, Zixia Jia, Jiaqi Li +2

Offline preference optimization methods are efficient for large language models (LLMs) alignment. Direct Preference optimization (DPO)-like learning, one of the most popular approa…

cs.CL2025

Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs

Jun Bai, Minghao Tong, Yang Liu +2

Context faithfulness is essential for reliable reasoning in context-dependent scenarios. However, large language models often struggle to ground their outputs in the provided conte…