activity
20232026
collaborators

10 papers

cs.CV2026

Making MLLM Latents Matter: Factor-Aware Predictive Learning for Cinematic Video Generation

Songlin Yang, Zhe Wang, Xiaotong Zhao +8

Cinematic video generation requires both temporal inference from limited inputs (i.e., sparse rather than frame-by-frame guidance) and factor-aware control of character, scene, act…

cs.AI2026

Provenance-Aware Belief Updating in Human-AI Feedback Loops

Tianyi Huang, Eric Liu, Jay Chong +2

In repeated human-AI interaction, a single observation can be quoted, summarized, and returned to a model as multiple reports, making repetition look like independent corroboration…

cs.CV2026

Adaptive Reward Routing: Dynamic Multi-Reward Optimization for Joint Audio-Video Diffusion via Forward-Process RL

Songlin Yang, Xiaotong Zhao, Jiacheng Zhang +5

Multi-reward guided reinforcement learning (i.e., RL) offers a promising way to improve joint audio-video diffusion models along several complementary objectives, including modalit…

cs.CV2026

OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation

Wenxue Li, Peiyan Guan, Haoyang Jiang +15

Omni reference-to-video (R2V) generation is emerging as a general paradigm that enables flexible video customization from diverse and compositional visual references. However, exis…

cs.CV2026

Temporally Grounded Compositional Camera Motion Understanding via Geometric Knowledge Distillation

Dazhao Du, Shiyan Du, Jian Liu +8

Understanding camera motion is fundamental to video perception, with applications in spatial intelligence and controllable video generation. Multimodal large language models (MLLMs…

cs.CV2026

Learning Spatiotemporal Sensitivity in Video LLMs via Counterfactual Reinforcement Learning

Dazhao Du, Jian Liu, Jialong Qin +7

Video large language models (Video LLMs) can achieve strong video-QA accuracy without reliably tracking spatiotemporal dynamics. A model may answer a motion question from static cu…

cs.CV2026

MLLMs Know When Before Speaking: Revealing and Recovering Temporal Grounding via Attention Cues

Dazhao Du, Liao Duan, Jian Liu +5

Video temporal grounding (VTG), which localizes the start and end times of a queried event in an untrimmed video, is a key test of whether multimodal large language models (MLLMs)…

cs.AI2026

SimWorld: An Open-ended Realistic Simulator for Autonomous Agents in Physical and Social Worlds

Jiawei Ren, Yan Zhuang, Xiaokang Ye +20

While LLM/VLM-powered AI agents have advanced rapidly in math, coding, and computer use, their applications in complex physical and social environments remain challenging. Building…

cs.NE2025

Evolutionary and Coevolutionary Multi-Agent Design Choices and Dynamics

Erik Hemberg, Eric Liu, Lucille Fuller +2

We investigate two representation alternatives for the controllers of teams of cyber agents. We combine these controller representations with different evolutionary algorithms, one…

cs.CV2023

MotionScript: Natural Language Descriptions for Expressive 3D Human Motions

Payam Jome Yazdian, Rachel Lagasse, Hamid Mohammadi +3

We introduce MotionScript, a novel framework for generating highly detailed, natural language descriptions of 3D human motions. Unlike existing motion datasets that rely on broad a…