collaborators

6 papers

cs.LG2026

PMOPD: Task Ordering, Cycling, and Parameter-Update Subspace Protection in Multi-Teacher On-Policy Distillation

Youzhi Liu, Ruobing Zheng, Boyuan Tong +5

Multi-teacher on-policy distillation (MOPD) has emerged as a popular post-training paradigm for integrating specialized capabilities in frontier language models. Existing OPD resea…

cs.CV2026

Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding

Hongyu Qu, Guangming Yao, Ling Xing +7

Streaming video understanding requires multimodal large language models (MLLMs) to process continuous visual inputs and respond to user queries under strict causality and bounded m…

cs.CV2026

VideoGAIA: A Benchmark for General AI Assistants on Agentic Video Understanding

Fan Zhang, Guangming Yao, Jinyang Wu +6

Video understanding is a fundamental task for evaluating the capabilities of multimodal large language models (MLLMs). However, existing leading models have already achieved approx…

cs.CV2026

MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism

Cong Chen, Guo Gan, Kaixiang Ji +7

Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and attention dilution. To overco…

cs.LG2026

SPRI: SVD-Partitioned Residual Initialization for Data-Constrained MoE Upcycling

Weiqiao Shan, Ruixiang Mao, Yuang Li +10

Mixture-of-Experts (MoE) models enable efficient scaling, but training them from scratch remains prohibitively expensive. MoE upcycling mitigates this cost by converting pretrained…

cs.CL2026

Dual Tuning for Reasoning Efficacy-Driven Data Curation in Multimodal LLM Training

Ruobing Zheng, Tianqi Li, Jianing Li +3

Reasoning post-training improves Large Language Models (LLMs) on complex tasks such as mathematics and coding, but its benefits across diverse multimodal tasks remains uncertain. T…