Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition
Paper • 2610.12341 • Published • 43
Shengyuan
THUCS
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authored a paper 4 days ago
AlignBench: Benchmarking Chinese Alignment of Large Language Models authored a paper 4 days ago
UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence
with Spatial Reasoning and UnderstandingOrganizations
upvoted a paper 1 day ago
authored 8
papers 4 days ago
AlignBench: Benchmarking Chinese Alignment of Large Language Models
Paper • 2311.18743 • Published • 1
UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understanding
Paper • 2506.23219 • Published • 7
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
Paper • 2507.19586 • Published
RAISECity: A Multimodal Agent Framework for Reality-Aligned 3D World Generation at City-Scale
Paper • 2511.18005 • Published • 1
SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenes
Paper • 2605.31148 • Published • 4
A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science
Paper • 2504.09848 • Published • 1
CritiqueLLM: Scaling LLM-as-Critic for Effective and Explainable Evaluation of Large Language Model Generation
Paper • 2311.18702 • Published
Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition
Paper • 2610.12341 • Published • 43