  Large Language Models (LLMs) still face challenges in tasks requiring
understanding implicit instructions and applying common-sense knowledge. In
such scenarios, LLMs may require multiple attempts to achieve human-level
performance, potentially leading to inaccurate responses or inferences in
practical environments, affecting their long-term consistency and behavior.
This paper introduces the Internal Time-Consciousness Machine (ITCM), a
computational consciousness structure to simulate the process of human
consciousness. We further propose the ITCM-based Agent (ITCMA), which supports
action generation and reasoning in open-world settings, and can independently
complete tasks. ITCMA enhances LLMs' ability to understand implicit
instructions and apply common-sense knowledge by considering agents'
interaction and reasoning with the environment. Evaluations in the Alfworld
environment show that trained ITCMA outperforms the state-of-the-art (SOTA) by
9% on the seen set. Even untrained ITCMA achieves a 96% task completion rate on
the seen set, 5% higher than SOTA, indicating its superiority over traditional
intelligent agents in utility and generalization. In real-world tasks with
quadruped robots, the untrained ITCMA achieves an 85% task completion rate,
which is close to its performance in the unseen set, demonstrating its
comparable utility and universality in real-world settings.
