title: Agentic Knowledgeable Self-awareness
summary: 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.
paper_authors._id: 67f345c983edbd64f15deeb3
paper_authors.name: Shuofei Qiao
paper_authors._id: 67f345c983edbd64f15deeb4
paper_authors.name: Zhisong Qiu
paper_authors._id: 67f345c983edbd64f15deeb5
paper_authors.name: Baochang Ren
paper_authors._id: 67f345c983edbd64f15deeb6
paper_authors.name: Xiaobin Wang
paper_authors._id: 67f345c983edbd64f15deeb7
paper_authors.name: Xiangyuan Ru
paper_authors._id: 67f345c983edbd64f15deeb8
paper_authors.name: Ningyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T12:27:18.477Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_authors._id: 67f345c983edbd64f15deeb9
paper_authors.name: Xiang Chen
paper_authors._id: 67f345c983edbd64f15deeba
paper_authors.name: Yong Jiang
paper_authors._id: 67f345c983edbd64f15deebb
paper_authors.name: Pengjun Xie
paper_authors._id: 67f345c983edbd64f15deebc
paper_authors.name: Fei Huang
paper_authors._id: 67f345c983edbd64f15deebd
paper_authors.name: Huajun Chen
paper_title: Agentic Knowledgeable Self-awareness
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: LLMs agentic planning LLM-based agents autonomous knowledge regulation heuristic situation judgement special tokens self-explored trajectories two-stage training