1 citations · 1 across the 8 of their papers we have counts for
9 papers
SAGA: Scene-Aware, Goal-Evolving Agents for Long-Horizon Strategy Game Planning
Tianyu Jin, Shuo Chen, Yida Wang +5
Grand-strategy games such as Civilization pose a distinctive long-horizon planning problem: an agent must divide one shared resource pool among six competing domains -- technology,…
AirGroundBench: Probing Spatial Intelligence in Multimodal Large Models under Heterogeneous Multi-View Embodied Collaboration
Haotian Li, Yida Wang, Leyuan Wang +7
In recent years, multimodal large language models (MLLMs) have shown strong potential for embodied intelligence, yet their ability to maintain geometrically consistent spatial unde…
Intelligent Automation for Embodied Benchmark Construction: Pipelines, Embodiments, Simulators, and Trends
Jinshan Lai, Jianwei Hu, Baoyang Jiang +7
Embodied intelligence now spans navigation, household assistance, manipulation, autonomous driving, aerial agents, and multimodal large-model control. This expansion has made bench…
Embodied-BenchClaw: An Autonomous Multi-Agent System for Embodied Spatial Intelligence Benchmark Construction
Baoyang Jiang, Fengchun Zhang, Leyuan Wang +7
Benchmarks are essential for evaluating embodied spatial intelligence, yet their construction is labor-intensive, hard to reuse, and difficult to maintain. Existing embodied benchm…
CRAFT: Counterfactual-to-Interactive Reinforcement Fine-Tuning for Driving Policies
Keyu Chen, Nanfei Ye, Yida Wang +4
Open-loop imitation learning has advanced modern autonomous driving policy architectures, but closed-loop deployment remains vulnerable to policy-induced distribution shift. Existi…
OccDirector: Language-Guided Behavior and Interaction Generation in 4D Occupancy Space
Zhuding Liang, Tianyi Yan, Dubing Chen +6
Generative world models increasingly rely on 4D occupancy for realistic autonomous driving simulation. However, existing generation frameworks depend on rigid geometric conditions…
CRAFT: Fine-Grained Cost-Aware Expert Replication For Efficient Mixture-of-Experts Serving
Adrian Zhao, Zhenkun Cai, Zhenyu Song +5
Mixture-of-Experts (MoE) has recently emerged as the mainstream architecture for efficiently scaling large language models while maintaining near-constant computational cost. Exper…
Inference Optimization of Foundation Models on AI Accelerators
Youngsuk Park, Kailash Budhathoki, Liangfu Chen +7
Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry an…
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
Haozheng Fan, Hao Zhou, Guangtai Huang +6
Getting large language models (LLMs) to perform well on the downstream tasks requires pre-training over trillions of tokens. This typically demands a large number of powerful compu…