From the 1 of 8 linked papers with an AI index.
8 papers
SAGA: Scene-Aware, Goal-Evolving Agents for Long-Horizon Strategy Game Planning
Tianyu Jin, Shuo Chen, Yida Wang +6
SAGA is a multi-agent framework that uses large language models to plan long‑term strategies in complex games by representing the game world as a scene graph, retrieving state on d…
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: 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…
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…