arXiv:2607. 24082v1 Announce Type: new Abstract: Recent advances in AI have substantially expanded its cognitive and reasoning capabilities.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
Recent advances in AI have substantially expanded its cognitive and reasoning capabilities. From the perspective of semantic complexity, the development of AI reveals a clear trajectory from simple to complex semantic processing.
arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested...
arXiv:2608. 19794v1 Announce Type: new Abstract: The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajectory toward general embodied intelligence (GEI).
AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents. These methods are impressive stochastic predictors, but they are resource-hungry, opaque, and known to make arbitrary decisions in novel situations due to the narrow set of underlying representation and processing choices.
arXiv:2608. 10330v1 Announce Type: new Abstract: AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents.
The study investigates how large language models (LLMs) can assist humans in semantic memory search tasks. By using the semantic fluency task (SFT), the researchers evaluate whether LLMs can follow and enhance human mental trajectories during generative semantic retrieval. Results show that an LLM’s ability to track and predict human memory trajectories in this task surpasses that of other humans.