8 citations · 20 across the 29 of their papers we have counts for
11 papers · 1 filter
PW-Pruning: Phase-Aware Expert Selection for Speech-Centric Omni-Modal Models
Junjie Cao, Fengping Tian, Wenjie Zhou +5
Sparse routing reduces per-token computation but leaves the full expert bank resident in memory. We study expert pruning for speech-centric omni-modal models, where pooling calibra…
Recall Begets Recall: Recurrent Memory Ranking for Personalized LLMs
Pengyue Jia, Shida Yan, Shunyu Liu +4
Memory ranking is the stage in personalized Large Language Models (LLMs) that decides which of the candidate memories returned by a retriever enter the limited context, so that the…
MVAgent: Multi-Agent Video Generation via Consistent Condition Construction and Shot-Level Policy Optimization
Xiangyu Kong, Wenjie Zhou, Fengping Tian +5
Multi-shot agentic video generation requires consistent character appearance, stable spatial layout across camera angles, and continuous character state between shots. When every s…
WnW: Waxing-and-Waning KV Cache for Long-Form Speech LLMs
Yiming Yao, Chenyang Lyu, Xuanfan Ni +4
Long-form audio inputs make the KV cache the dominant memory cost of speech LLMs. Prefill-only KV compression methods permanently discard audio KV positions once evicted, with no p…
Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
Lecheng Yan, Yichong Zhang, Xiantao Xu +12
Long-form video editing over heterogeneous footage requires agents to coordinate source selection, multimodal analysis, timeline construction, narration and subtitle alignment, ren…
Trust No Tool: Evaluating and Defending LLM Agents under Untrusted Tool Feedback
Lecheng Yan, Ruizhe Li, Xicheng Han +5
Tool-using LLM agents increasingly rely on external tools to make consequential decisions, yet most existing agent-security benchmarks and defenses implicitly assume that tool feed…
SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures
Nedjma Ousidhoum, Junho Myung, Carla Perez-Almendros +27
We present our shared task on evaluating the adaptability of LLMs and NLP systems across multiple languages and cultures. The task data consist of an extended version of our manual…
Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling
Fan Jiang, Yu Zhao, Chenyang Lyu +5
We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only around 5\% of the total par…
CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks
Peiqin Lin, Chenyang Lyu, Wenjiang Luo +22
Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities. However, these benchmarks prio…
Speech-XL: Towards Long-Form Speech Understanding in Large Speech Language Models
Haoqin Sun, Chenyang Lyu, Shiwan Zhao +5
Despite the growing success of Large Speech Language Models (LSLMs) in processing short-term acoustic signals, their extension to long-form audio understanding is severely bottlene…
LongSpeech: A Scalable Benchmark for Transcription, Translation and Understanding in Long Speech
Fei Yang, Xuanfan Ni, Renyi Yang +7
Recent advances in audio-language models have demonstrated remarkable success on short, segment-level speech tasks. However, real-world applications such as meeting transcription,…