Agentic Knowledgeable Self-awareness
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.
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Shuofei Qiao
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Zhisong Qiu
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Baochang Ren
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Xiaobin Wang
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Xiangyuan Ru
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Ningyu Zhang
claimed_verified
2025-05-21T12:27:18.477Z
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Ningyu Zhang
user
Ningyu
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Xiang Chen
67f345c983edbd64f15deeba
Yong Jiang
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Pengjun Xie
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Fei Huang
67f345c983edbd64f15deebd
Huajun Chen
Agentic Knowledgeable Self-awareness
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.
KnowSelf, a data-centric approach, enables LLM-based agents to autonomously regulate knowledge utilization and achieve optimal planning with minimal costs by switching between situations.
LLMs agentic planning LLM-based agents autonomous knowledge regulation heuristic situation judgement special tokens self-explored trajectories two-stage training