title: DynaSaur: Large Language Agents Beyond Predefined Actions
summary: Existing LLM agent systems typically select actions from a fixed and
  predefined set at every step. While this approach is effective in closed,
  narrowly-scoped environments, we argue that it presents two major challenges
  when deploying LLM agents in real-world scenarios: (1) selecting from a fixed
  set of actions significantly restricts the planning and acting capabilities of
  LLM agents, and (2) this approach requires substantial human effort to
  enumerate and implement all possible actions, which becomes impractical in
  complex environments with a vast number of potential actions. In this work, we
  propose an LLM agent framework that enables the dynamic creation and
  composition of actions in an online manner. In this framework, the agent
  interacts with the environment by generating and executing programs written in
  a general-purpose programming language at each step. Furthermore, generated
  actions are accumulated over time for future reuse. Our extensive experiments
  on the GAIA benchmark demonstrate that this framework offers significantly
  greater flexibility and outperforms previous methods. Notably, it allows an LLM
  agent to recover in scenarios where no relevant action exists in the predefined
  set or when existing actions fail due to unforeseen edge cases. At the time of
  writing, we hold the top position on the GAIA public leaderboard. Our code can
  be found in
  https://github.com/adobe-research/dynasaur{https://github.com/adobe-research/dynasaur}.
paper_authors._id: 6729bd832f893196814b31b3
paper_authors.name: Dang Nguyen
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paper_authors.fullname: Dang Nguyen
paper_authors.type: user
paper_authors.user: dangmn
paper_authors._id: 6729bd832f893196814b31b4
paper_authors.name: Viet Dac Lai
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paper_authors.fullname: Viet Lai
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paper_authors.user: laiviet
paper_authors._id: 6729bd832f893196814b31b5
paper_authors.name: Seunghyun Yoon
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paper_authors.fullname: David Seunghyun Yoon
paper_authors.type: user
paper_authors.user: mysmilesh
paper_authors._id: 6729bd832f893196814b31b6
paper_authors.name: Ryan A. Rossi
paper_authors._id: 6729bd832f893196814b31b7
paper_authors.name: Handong Zhao
paper_authors._id: 6729bd832f893196814b31b8
paper_authors.name: Ruiyi Zhang
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paper_authors.fullname: Ruiyi Zhang
paper_authors.type: user
paper_authors.user: zhangry868
paper_authors._id: 6729bd832f893196814b31b9
paper_authors.name: Puneet Mathur
paper_authors._id: 6729bd832f893196814b31ba
paper_authors.name: Nedim Lipka
paper_authors._id: 6729bd832f893196814b31bb
paper_authors.name: Yu Wang
paper_authors._id: 6729bd832f893196814b31bc
paper_authors.name: Trung Bui
paper_authors.status: claimed_verified
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paper_authors._id: 6036eb46413a78f892e733a0
paper_authors.avatarUrl: /avatars/b15925f89631323c176ecbd0eab99feb.svg
paper_authors.fullname: Trung Bui
paper_authors.type: user
paper_authors.user: Trung
paper_authors._id: 6729bd832f893196814b31bd
paper_authors.name: Franck Dernoncourt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T07:57:10.629Z
paper_authors._id: 62c5947524171688a9feb992
paper_authors.avatarUrl: /avatars/5a151713b9eae8dc566f5957acee3475.svg
paper_authors.fullname: Franck Dernoncourt
paper_authors.type: user
paper_authors.user: Franck-Dernoncourt
paper_authors._id: 6729bd832f893196814b31be
paper_authors.name: Tianyi Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:37:59.974Z
paper_authors._id: 647f5af5b0e96764589f3b2a
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paper_authors.fullname: Tianyi Zhou
paper_authors.type: user
paper_authors.user: zhoutianyi
paper_title: DynaSaur: Large Language Agents Beyond Predefined Actions
paper_summary: Existing LLM agent systems typically select actions from a fixed and
  predefined set at every step. While this approach is effective in closed,
  narrowly-scoped environments, we argue that it presents two major challenges
  when deploying LLM agents in real-world scenarios: (1) selecting from a fixed
  set of actions significantly restricts the planning and acting capabilities of
  LLM agents, and (2) this approach requires substantial human effort to
  enumerate and implement all possible actions, which becomes impractical in
  complex environments with a vast number of potential actions. In this work, we
  propose an LLM agent framework that enables the dynamic creation and
  composition of actions in an online manner. In this framework, the agent
  interacts with the environment by generating and executing programs written in
  a general-purpose programming language at each step. Furthermore, generated
  actions are accumulated over time for future reuse. Our extensive experiments
  on the GAIA benchmark demonstrate that this framework offers significantly
  greater flexibility and outperforms previous methods. Notably, it allows an LLM
  agent to recover in scenarios where no relevant action exists in the predefined
  set or when existing actions fail due to unforeseen edge cases. At the time of
  writing, we hold the top position on the GAIA public leaderboard. Our code can
  be found in
  https://github.com/adobe-research/dynasaur{https://github.com/adobe-research/dynasaur}.
paper_ai_summary: A new LLM agent framework enables dynamic action creation and reuse by generating and executing programs, offering greater flexibility and improved performance in complex, real-world scenarios.
paper_ai_keywords: LLM agent GAIA benchmark dynamic action creation program generation action reuse unforeseen edge cases