FaSTA^*: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient
  Multi-turn Image Editing
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.
685e151c71131fa43be08afe
Advait Gupta
claimed_verified
2025-06-27T20:31:01.825Z
67a99ec47b754f038d110926
/avatars/e1ff318a42ccb75b094bbe7dae0cabec.svg
Advait Gupta
user
advaitgupta
685e151c71131fa43be08aff
Rishie Raj
685e151c71131fa43be08b00
Dang Nguyen
685e151c71131fa43be08b01
Tianyi Zhou
claimed_verified
2025-06-27T08:56:47.348Z
647f5af5b0e96764589f3b2a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
Tianyi Zhou
user
zhoutianyi
FaSTA^*: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient
  Multi-turn Image Editing
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.
A neurosymbolic agent combines language models for fast subtask planning with A$^*$ search for detailed toolpaths, creating a cost-efficient multi-turn image editing solution.
neurosymbolic agent LLM A$^*$ search subtask planning toolpath inductive reasoning symbolic subroutines adaptive fast-slow planning