The field of artificial intelligence is moving towards developing more general, open-ended, and creative reasoning systems. Recent research has focused on multimodal reasoning, physical understanding, and embodied cognition, with a emphasis on benchmarks and datasets that can evaluate the performance of models in these areas. The introduction of new benchmarks such as PuzzleWorld and PhyBlock has highlighted the limitations of current models in solving complex, multi-step puzzles and understanding physical phenomena. Furthermore, research has shown that current models struggle with spatial reasoning, dependency reasoning, and intuitive physics understanding. To address these limitations, researchers are exploring the use of neuro-symbolic architectures, Bayesian inference, and meta-learning to develop more robust and adaptive models.

Noteworthy papers include: PuzzleWorld, which introduces a large-scale benchmark for multimodal, open-ended reasoning in puzzlehunts and demonstrates the value of reasoning annotations in improving model performance. PhyBlock, which presents a progressive benchmark for physical understanding and planning via 3D block assembly tasks and highlights the limitations of current vision-language models in physically grounded, multi-step planning. SlotPi, which introduces a physics-informed object-centric reasoning model that integrates physical knowledge into models and demonstrates strong adaptability across diverse scenarios.