DynaSaur: Large Language Agents Beyond Predefined Actions
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}.
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Dang Nguyen
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2024-11-05T16:09:14.338Z
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Dang Nguyen
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Viet Dac Lai
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Viet Lai
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Seunghyun Yoon
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David Seunghyun Yoon
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mysmilesh
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Ryan A. Rossi
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Handong Zhao
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Ruiyi Zhang
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2024-11-05T20:17:32.104Z
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Ruiyi Zhang
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zhangry868
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Puneet Mathur
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Nedim Lipka
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Yu Wang
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Trung Bui
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Trung Bui
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Trung
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Franck Dernoncourt
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2024-11-05T07:57:10.629Z
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Franck Dernoncourt
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Tianyi Zhou
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2024-11-15T09:37:59.974Z
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Tianyi Zhou
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zhoutianyi
DynaSaur: Large Language Agents Beyond Predefined Actions
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}.
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.
LLM agent GAIA benchmark dynamic action creation program generation action reuse unforeseen edge cases