title: Agent Workflow Memory
summary: Despite the potential of language model-based agents to solve real-world
  tasks such as web navigation, current methods still struggle with long-horizon
  tasks with complex action trajectories. In contrast, humans can flexibly solve
  complex tasks by learning reusable task workflows from past experiences and
  using them to guide future actions. To build agents that can similarly benefit
  from this process, we introduce Agent Workflow Memory (AWM), a method for
  inducing commonly reused routines, i.e., workflows, and selectively providing
  workflows to the agent to guide subsequent generations. AWM flexibly applies to
  both offline and online scenarios, where agents induce workflows from training
  examples beforehand or from test queries on the fly. We experiment on two major
  web navigation benchmarks -- Mind2Web and WebArena -- that collectively cover
  1000+ tasks from 200+ domains across travel, shopping, and social media, among
  others. AWM substantially improves the baseline results by 24.6% and 51.1%
  relative success rate on Mind2Web and WebArena while reducing the number of
  steps taken to solve WebArena tasks successfully. Furthermore, online AWM
  robustly generalizes in cross-task, website, and domain evaluations, surpassing
  baselines from 8.9 to 14.0 absolute points as train-test task distribution gaps
  widen.
paper_authors._id: 66e254c524df851611e7f5a6
paper_authors.name: Zora Zhiruo Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-07T16:14:37.227Z
paper_authors._id: 6356c77dd18276171735985f
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paper_authors.fullname: Zora Zhiruo Wang
paper_authors.type: user
paper_authors.user: zorazrw
paper_authors._id: 66e254c524df851611e7f5a7
paper_authors.name: Jiayuan Mao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-12T07:19:15.447Z
paper_authors._id: 62f168243e0991a8ab14311d
paper_authors.avatarUrl: /avatars/a5c118a5bd7ce4a49af752068d60ccea.svg
paper_authors.fullname: Mao
paper_authors.type: user
paper_authors.user: Jiayuan
paper_authors._id: 66e254c524df851611e7f5a8
paper_authors.name: Daniel Fried
paper_authors._id: 66e254c524df851611e7f5a9
paper_authors.name: Graham Neubig
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-12T07:19:25.474Z
paper_authors._id: 60de14638bedd2315529d43f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1625166923504-noauth.png
paper_authors.fullname: Graham Neubig
paper_authors.type: user
paper_authors.user: gneubig
paper_title: Agent Workflow Memory
paper_summary: Despite the potential of language model-based agents to solve real-world
  tasks such as web navigation, current methods still struggle with long-horizon
  tasks with complex action trajectories. In contrast, humans can flexibly solve
  complex tasks by learning reusable task workflows from past experiences and
  using them to guide future actions. To build agents that can similarly benefit
  from this process, we introduce Agent Workflow Memory (AWM), a method for
  inducing commonly reused routines, i.e., workflows, and selectively providing
  workflows to the agent to guide subsequent generations. AWM flexibly applies to
  both offline and online scenarios, where agents induce workflows from training
  examples beforehand or from test queries on the fly. We experiment on two major
  web navigation benchmarks -- Mind2Web and WebArena -- that collectively cover
  1000+ tasks from 200+ domains across travel, shopping, and social media, among
  others. AWM substantially improves the baseline results by 24.6% and 51.1%
  relative success rate on Mind2Web and WebArena while reducing the number of
  steps taken to solve WebArena tasks successfully. Furthermore, online AWM
  robustly generalizes in cross-task, website, and domain evaluations, surpassing
  baselines from 8.9 to 14.0 absolute points as train-test task distribution gaps
  widen.
paper_ai_summary: Agent Workflow Memory (AWM) enhances language model-based agents' performance on complex tasks by inducing and using reusable task workflows, resulting in improved success rates and reduced steps compared to baselines.
paper_ai_keywords: Agent Workflow Memory AWM long-horizon tasks complex action trajectories task workflows reusable routines offline scenarios online scenarios Mind2Web WebArena relative success rate cross-task generalization website generalization domain evaluation