A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Subbarao Kambhampati
Argues that LLMs lack genuine reasoning and planning capabilities, despite their impressive language generation, and that their apparent success is due to memorization and pattern matching.
Kazuki Nonoyama, Ziang Liu, Tomofumi Fujiwara, et al.
This paper optimizes energy-efficient motion planning for dual-arm industrial robots by fine-tuning PID controllers with Genetic Algorithms and Particle Swarm Optimization.
Craig Boutilier, Taraneh Dean, Steve Hanks
This paper surveys how structural properties of MDPs can be exploited via AI-style representations to ease the computational burden of planning under uncertainty.
Ruicheng Ao, Siyang Gao, David Simchi-Levi
This paper establishes fundamental reliability limits of LLM-based multi-agent planning by modeling it as a delegated decision network and proving it is dominated by a centralized Bayes decision maker.
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.
Unknown
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.