FaSTA^*: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient
  Multi-turn Image Editing
Authors: name: Advait Gupta, name: Rishie Raj, name: Dang Nguyen, name: Tianyi Zhou

We develop a cost-efficient neurosymbolic agent to address challenging
multi-turn image editing tasks such as "Detect the bench in the image while
recoloring it to pink. Also, remove the cat for a clearer view and recolor the
wall to yellow.'' It combines the fast, high-level subtask planning by large
language models (LLMs) with the slow, accurate, tool-use, and local A^*
search per subtask to find a cost-efficient toolpath -- a sequence of calls to
AI tools. To save the cost of A^* on similar subtasks, we perform inductive
reasoning on previously successful toolpaths via LLMs to continuously
extract/refine frequently used subroutines and reuse them as new tools for
future tasks in an adaptive fast-slow planning, where the higher-level
subroutines are explored first, and only when they fail, the low-level A^*
search is activated. The reusable symbolic subroutines considerably save
exploration cost on the same types of subtasks applied to similar images,
yielding a human-like fast-slow toolpath agent "FaSTA^*'': fast subtask
planning followed by rule-based subroutine selection per subtask is attempted
by LLMs at first, which is expected to cover most tasks, while slow A^*
search is only triggered for novel and challenging subtasks. By comparing with
recent image editing approaches, we demonstrate FaSTA^* is significantly more
computationally efficient while remaining competitive with the state-of-the-art
baseline in terms of success rate.