17 citations · 22 across the 14 of their papers we have counts for
15 papers
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
Jingbo Xu, Jianjie Fang, Zhuohang Li +9
World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively. Although these models learn shared physical dynam…
IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training
Rongze Tang, Jianjie Fang, Zhaolu Wang +8
World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet still struggle to model physically plausible interactions. Existing appr…
CAER: Causal Action Effect Reweighting for World Model Training
Jianjie Fang, Xvyuan Liu, Ziyou Wang +9
World models are becoming core infrastructure for embodied intelligence, with action-conditioned video generation providing controllable predictions of how scenes evolve after agen…
DensityKV: Density-Guided KV Cache Compression for Long Video Generation
Wenqu Zhao, Xuemin Chi, Xin Zhang +6
Autoregressive video diffusion models enable streaming generation through sliding-window attention, but each generated block is conditioned on previously generated content, causing…
WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory and In-Context Learning
Haisheng Su, Zongdai Liu, Xin Jin +13
World Action Models (WAMs) offer a promising paradigm for robotic manipulation by jointly modeling visual state transitions and robot actions. However, existing WAMs are constraine…
Worldscape-MoE: A Unified Mixture-of-Experts World Model for Scalable Heterogeneous Action Control
Jianjie Fang, Yongyan Xu, Ziyou Wang +13
World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, fo…
WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation
Baining Zhao, Jiacheng Xu, Weicheng Feng +13
Aerial vision-language navigation (VLN) requires agents to follow natural-language instructions through closed-loop perception and action in 3D environments. We argue that aerial V…
iWorld-Bench: A Benchmark for Interactive World Models with a Unified Action Generation Framework
Jianjie Fang, Yingshan Lei, Qin Wan +8
Achieving Artificial General Intelligence (AGI) requires agents that learn and interact adaptively, with interactive world models providing scalable environments for perception, re…
How Far Are Large Multimodal Models from Human-Level Spatial Action? A Benchmark for Goal-Oriented Embodied Navigation in Urban Airspace
Baining Zhao, Ziyou Wang, Jianjie Fang +8
Large multimodal models (LMMs) show strong visual-linguistic reasoning but their capacity for spatial decision-making and action remains unclear. In this work, we investigate wheth…
Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents
Fanhang Man, Huandong Wang, Jianjie Fang +4
User sentiment on social media reveals the underlying social trends, crises, and needs. Researchers have analyzed users' past messages to trace the evolution of sentiments and reco…
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning
Baining Zhao, Ziyou Wang, Jianjie Fang +7
Humans can perceive and reason about spatial relationships from sequential visual observations, such as egocentric video streams. However, how pretrained models acquire such abilit…
The Point, the Vision and the Text: Does Point Cloud Boost Spatial Reasoning of Large Language Models? A Bias-Controlled Study
Weichen Zhang, Ruiying Peng, Xin Zeng +9
3D Large Language Models (LLMs) leveraging spatial information in point clouds for 3D spatial reasoning attract great attention. Despite some promising results, the advantages of p…
UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces
Baining Zhao, Jianjie Fang, Zichao Dai +8
Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban 3D space remain to be explored. We introduce a ben…
Open3D-VQA: A Benchmark for Comprehensive Spatial Reasoning with Multimodal Large Language Model in Open Space
Weichen Zhang, Zile Zhou, Xin Zeng +7
Spatial reasoning is a fundamental capability of multimodal large language models (MLLMs), yet their performance in open aerial environments remains underexplored. In this work, we…
EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment
Chen Gao, Baining Zhao, Weichen Zhang +9
Embodied artificial intelligence emphasizes the role of an agent's body in generating human-like behaviors. The recent efforts on EmbodiedAI pay a lot of attention to building up m…