Large language models (LLMs) have significantly advanced artificial intelligence (AI) by excelling in tasks such as understanding, generation, and reasoning across multiple modalities. Despite these achievements, LLMs have inherent limitations including outdated information, hallucinations, inefficiency, lack of interpretability, and challenges in domain-specific accuracy. To address these issues, this survey explores three promising directions in the post-LLM era: knowledge empowerment, model collaboration, and model co-evolution. First, we examine methods of integrating external knowledge into LLMs to enhance factual accuracy, reasoning capabilities, and interpretability, including incorporating knowledge into training objectives, instruction tuning, retrieval-augmented inference, and knowledge prompting. Second, we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging, functional model collaboration, and knowledge injection. Third, we delve into model co-evolution, in which multiple models collaboratively evolve by sharing knowledge, parameters, and learning strategies to adapt to dynamic environments and tasks, thereby enhancing their adaptability and continual learning. We illustrate how the integration of these techniques advances AI capabilities in science, engineering, and society—particularly in hypothesis development, problem formulation, problem-solving, and interpretability across various domains. We conclude by outlining future pathways for further advancement and applications.