Agentic Knowledgeable Self-awareness
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909626821246976 |
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| author | Qiao, Shuofei Qiu, Zhisong Ren, Baochang Wang, Xiaobin Ru, Xiangyuan Zhang, Ningyu Chen, Xiang Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun |
| author_facet | Qiao, Shuofei Qiu, Zhisong Ren, Baochang Wang, Xiaobin Ru, Xiangyuan Zhang, Ningyu Chen, Xiang Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun |
| contents | Large Language Models (LLMs) have achieved considerable performance across various agentic planning tasks. However, traditional agent planning approaches adopt a "flood irrigation" methodology that indiscriminately injects gold trajectories, external feedback, and domain knowledge into agent models. This practice overlooks the fundamental human cognitive principle of situational self-awareness during decision-making-the ability to dynamically assess situational demands and strategically employ resources during decision-making. We propose agentic knowledgeable self-awareness to address this gap, a novel paradigm enabling LLM-based agents to autonomously regulate knowledge utilization. Specifically, we propose KnowSelf, a data-centric approach that applies agents with knowledgeable self-awareness like humans. Concretely, we devise a heuristic situation judgement criterion to mark special tokens on the agent's self-explored trajectories for collecting training data. Through a two-stage training process, the agent model can switch between different situations by generating specific special tokens, achieving optimal planning effects with minimal costs. Our experiments demonstrate that KnowSelf can outperform various strong baselines on different tasks and models with minimal use of external knowledge. Code is available at https://github.com/zjunlp/KnowSelf. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03553 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic Knowledgeable Self-awareness Qiao, Shuofei Qiu, Zhisong Ren, Baochang Wang, Xiaobin Ru, Xiangyuan Zhang, Ningyu Chen, Xiang Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multiagent Systems Large Language Models (LLMs) have achieved considerable performance across various agentic planning tasks. However, traditional agent planning approaches adopt a "flood irrigation" methodology that indiscriminately injects gold trajectories, external feedback, and domain knowledge into agent models. This practice overlooks the fundamental human cognitive principle of situational self-awareness during decision-making-the ability to dynamically assess situational demands and strategically employ resources during decision-making. We propose agentic knowledgeable self-awareness to address this gap, a novel paradigm enabling LLM-based agents to autonomously regulate knowledge utilization. Specifically, we propose KnowSelf, a data-centric approach that applies agents with knowledgeable self-awareness like humans. Concretely, we devise a heuristic situation judgement criterion to mark special tokens on the agent's self-explored trajectories for collecting training data. Through a two-stage training process, the agent model can switch between different situations by generating specific special tokens, achieving optimal planning effects with minimal costs. Our experiments demonstrate that KnowSelf can outperform various strong baselines on different tasks and models with minimal use of external knowledge. Code is available at https://github.com/zjunlp/KnowSelf. |
| title | Agentic Knowledgeable Self-awareness |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2504.03553 |