arXiv:2607. 16465v1 Announce Type: new Abstract: Edmund C.
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.
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).
arXiv:2607. 18985v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.
arXiv:2607. 18985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.
arXiv:2607. 05168v1 Announce Type: new Abstract: Why do intelligent systems need to perform explicit symbolic reasoning?
arXiv:2607. 03449v1 Announce Type: cross Abstract: Current Vision-Language-Action (VLA) models excel at robotic manipulation but often struggle with non-Markovian tasks requiring long-term memory and reasoning due to their reliance on immediate observations.
The paper proposes a new paradigm called Code-Only-as-Policy (COAP), treating the embodied world as a Turing machine where the robot and environment state are the tape and the policy is the code. COAP replaces traditional vision-language agents by using code to measure, track, and decide based on camera images and proprioception, enabling explicit state storage, fast execution, and extensibility across tasks. On RoboDojo’s 42 bimanual tasks, the COAP library achieved a 70.24% success rate without a model at test time.