Debates on human behavioral evolution have largely focused on African and European records, while Asia’s contribution remains underrepresented. Despite the significance of the Asian Pleistocene fossil record, its behavioral insights have been hindered by limited taphonomic research, restricted dissemination, and shifting academic trends. Many key Chinese archaeofaunal sites, particularly in karstic contexts, contain complex palimpsests that challenge traditional taphonomic methods prone to equifinality.

Advancements in artificial intelligence and computational archaeology now offer new ways to address these challenges. Machine learning classifiers, computer vision through convolutional neural networks, and 3D deep learning architectures enable precise discrimination of bone surface modifications. These techniques refine carnivore agency identification down to the taxon level and provide mathematical certainty in agency attribution, aiding in disentangling complex palimpsests.

This study highlights key Chinese archaeofaunal records, particularly Zhoukoudian, and proposes methodological approaches to improve their resolution. By integrating these cutting-edge techniques, the Asian Pleistocene record can take a more central role in discussions on early human behavioral variability. This research aims to establish a model for applying the “new taphonomy” globally, enhancing our understanding of hominin activities and their ecological contexts.