LLMs are being increasingly used for planning-style tasks, but their capabilities for planning and reasoning are poorly understood. We present a novel method for automatically converting planning benchmarks written in PDDL into textual descriptions and offer a benchmark dataset created with our method. We show that while the best LLM planners do well on many planning tasks, others remain out of reach of current methods.

Well, I am not that old, but I have been involved with computer vision for almost two decades now. I have started publishing papers when about 250 papers were submitted per year to the major and most selective conferences in computer vision (ICCV, CVPR, ECCV). At that time the conference boards were approx 60-80 people and there were 300-400 participants. Computer vision conferences (even up to 2010) were organized in a number of thematic areas reasonably well represented both in terms of content as well as in terms of approaches. Early vision, grouping/segmentation, motion analysis/tracking, recognition & 3D vision are some examples.

Well, I am not that old, but I have been involved with computer vision for almost two decades now. I have started publishing papers when about 250 papers were submitted per year to the major and most selective conferences in computer vision (ICCV, CVPR, ECCV). At that time the conference boards were approx 60-80 people and there were 300-400 participants. Computer vision conferences (even up to 2010) were organized in a number of thematic areas reasonably well represented both in terms of content as well as in terms of approaches. Early vision, grouping/segmentation, motion analysis/tracking, recognition & 3D vision are some examples.