activity
20202026
most citedMitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

24 citations · 74 across the 27 of their papers we have counts for

collaborators

29 papers

cs.LG2026

Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards

Xi Li, Shu Zhao, Xiaohan Zou +6

Multi-modal large language models (MLLMs) integrate heterogeneous modalities through modality alignment and fusion, enabling stronger understanding and reasoning. However, this arc…

cs.AI2026

COMFYCLAW: Self-Evolving Skill Harnesses for Image Generation Workflows

Zongxia Li, Dawei Liu, Fuxiao Liu +6

Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently. As these workflows become repeated and domain-specific, agent…

cs.CL2026

Not All Skills Help: Measuring and Repairing Agent Knowledge

Yixuan Wang, Yiyang Zhou, Yiming Liang +4

LLM agents can improve without weight updates by accumulating natural-language skills from experience, but current systems entrust every decision about which skills to keep and how…

cs.CV2026

STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models

Yiming Liang, Yixiao Chen, Yiyang Zhou +8

Many video reasoning tasks require tracking motion, temporal order, and evolving visual states across frames. Existing methods built on large vision-language models (LVLMs) often a…

cs.LG2026

Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence

NVIDIA, :, Amala Sanjay Deshmukh +204

We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…

cs.CV2026

MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data

Zongxia Li, Hongyang Du, Chengsong Huang +8

Self-evolving has emerged as a key paradigm for improving foundational models such as Large Language Models (LLMs) and Vision Language Models (VLMs) with minimal human intervention…