A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Sertaç Karaman, Emilio Frazzoli
Introduces PRM* and RRT*, provably asymptotically optimal sampling-based motion planning algorithms, with analysis via random geometric graphs.
Brian A. Paden, Michal Čáp, Sze Zheng Yong, et al.
Surveys motion planning and control algorithms for self-driving urban vehicles, comparing approaches by mobility model, environment assumptions, and computational requirements.
Unknown
An empirical study showing how text quality interventions reshape neural scaling laws for LLMs, affecting performance projection and resource planning.
Unknown
A closed-loop long-horizon robotic planning method using equilibrium sequence modeling that more than doubles baseline success rates on long-horizon tasks.
Unknown
A decade-long review of evolving architectures and long-horizon planning in multi-agent conversational AI, synthesizing progress and open challenges.
Unknown
GraphThink uses task graphs to enhance LLM planning for long-horizon embodied tasks, with GRPO-based reward design.
Unknown
ActSWM introduces action-sensitive world models for long-horizon planning in open-world games, improving latent model-predictive control.
Unknown
The paper proposes an LLM-driven symbolic planning combined with hierarchical imitation learning framework for long-horizon deformable object assembly tasks.
Yan Deng, Fei Xu
DreamFly introduces causal memory and receding-horizon diffusion planning to improve aerial vision-language navigation, achieving state-of-the-art results on the OpenFly benchmark.
Guancheng Zeng, Xueyi Chen, Jiawang Hu, et al.
Routine is a structured multi-step agent planning framework that significantly improves tool-calling accuracy in enterprise scenarios, boosting GPT-4o from 41.1% to 96.3% and enabling smaller models to approach that performance via fine-tuning.
Kuan Xing, Ye Wang, Changyi Gan, et al.
Atelier is a shortcut-aware control-state planning framework that improves artist-grounded text-to-image generation by explicitly modeling artistic intent and avoiding canonical style shortcuts.
Daniel Armstrong, Xuan-Vu Nguyen, Octavian Susanu, et al.
SynthEx, an agentic LLM framework, plans retrosynthetic routes to complex natural products that expert chemists judge comparable to human-designed syntheses.