A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Yunxin Li, Zhenyu Liu, Zitao Li, et al.
This survey introduces Large Multimodal Reasoning Models (LMRMs) and proposes the concept of native LMRMs (N-LMRMs) that integrate perception, reasoning, and planning.
Sungwon Lee, Joon-Yong Jung, Akaworn Mahatthanatrakul, et al.
This review evaluates AI's impact on spinal imaging and care, covering image quality, diagnosis, surgical planning, and personalized predictions.
Danijar Hafner, J. Pašukonis, Jimmy Ba, et al.
Dreamer learns a world model from latent representations to enable planning and strong task performance across diverse domains.
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This paper investigates whether large reasoning models plan reasoning strength (number of reasoning tokens) before generating answers using linear probing.
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An action case study demonstrates effective integration of collaborative planning with long-range foresight in a hierarchical government research organization.
Jiayu Liu, Qihan Lin, Cheng Qian, et al.
Evaluates LLM tool-use agents on long-horizon planning in large-scale tool ecosystems, finding most models below two-thirds accuracy.
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Instructflow uses adaptive symbolic constraints to guide code generation for long-horizon robotic planning.
Jiejing Shao, Haoran Hao, Xiaowen Yang, et al.
This paper introduces a neuro-symbolic abductive imitation learning framework for long-horizon planning.
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IALP system integrates grounding mechanisms into long-horizon planning for embodied mobile manipulation.
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This paper uses language models to enable long-horizon planning for multi-agent robots in partially observable environments, overcoming traditional RL and HRL limitations.
Joseph Clinton, Robert Lieck
Planning Transformer introduces planning tokens for dual time-scale prediction to enable long-horizon offline reinforcement learning with implicit planning.
Brian Ichter, Pierre Sermanet, Corey Lynch
This paper introduces a tree-based planning method that combines broad exploration with local policy reuse for long-horizon tasks in high-dimensional state spaces.