CoSTAast: Cost-Sensitive Toolpath Agent for Multi-turn Image Editing
Text-to-image models like stable diffusion and DALLE-3 still struggle with
multi-turn image editing. We decompose such a task as an agentic workflow
(path) of tool use that addresses a sequence of subtasks by AI tools of varying
costs. Conventional search algorithms require expensive exploration to find
tool paths. While large language models (LLMs) possess prior knowledge of
subtask planning, they may lack accurate estimations of capabilities and costs
of tools to determine which to apply in each subtask. Can we combine the
strengths of both LLMs and graph search to find cost-efficient tool paths? We
propose a three-stage approach "CoSTA*" that leverages LLMs to create a subtask
tree, which helps prune a graph of AI tools for the given task, and then
conducts A* search on the small subgraph to find a tool path. To better balance
the total cost and quality, CoSTA* combines both metrics of each tool on every
subtask to guide the A* search. Each subtask's output is then evaluated by a
vision-language model (VLM), where a failure will trigger an update of the
tool's cost and quality on the subtask. Hence, the A* search can recover from
failures quickly to explore other paths. Moreover, CoSTA* can automatically
switch between modalities across subtasks for a better cost-quality trade-off.
We build a novel benchmark of challenging multi-turn image editing, on which
CoSTA* outperforms state-of-the-art image-editing models or agents in terms of
both cost and quality, and performs versatile trade-offs upon user preference.
67d393ca336d57afb21bbf63
Advait Gupta
claimed_verified
2025-03-14T08:56:36.855Z
67a99ec47b754f038d110926
/avatars/e1ff318a42ccb75b094bbe7dae0cabec.svg
Advait Gupta
user
advaitgupta
67d393ca336d57afb21bbf64
NandaKiran Velaga
claimed_verified
2025-03-14T08:56:34.327Z
672f89e6d7f4171f374dacea
/avatars/4a8b378e13e862586bb428fdf000b3cc.svg
NandaKiran Velaga
user
nandakiran09
67d393ca336d57afb21bbf65
Dang Nguyen
67d393ca336d57afb21bbf66
Tianyi Zhou
claimed_verified
2025-03-14T08:56:39.157Z
647f5af5b0e96764589f3b2a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
Tianyi Zhou
user
zhoutianyi
CoSTAast: Cost-Sensitive Toolpath Agent for Multi-turn Image Editing
Text-to-image models like stable diffusion and DALLE-3 still struggle with
multi-turn image editing. We decompose such a task as an agentic workflow
(path) of tool use that addresses a sequence of subtasks by AI tools of varying
costs. Conventional search algorithms require expensive exploration to find
tool paths. While large language models (LLMs) possess prior knowledge of
subtask planning, they may lack accurate estimations of capabilities and costs
of tools to determine which to apply in each subtask. Can we combine the
strengths of both LLMs and graph search to find cost-efficient tool paths? We
propose a three-stage approach "CoSTA*" that leverages LLMs to create a subtask
tree, which helps prune a graph of AI tools for the given task, and then
conducts A* search on the small subgraph to find a tool path. To better balance
the total cost and quality, CoSTA* combines both metrics of each tool on every
subtask to guide the A* search. Each subtask's output is then evaluated by a
vision-language model (VLM), where a failure will trigger an update of the
tool's cost and quality on the subtask. Hence, the A* search can recover from
failures quickly to explore other paths. Moreover, CoSTA* can automatically
switch between modalities across subtasks for a better cost-quality trade-off.
We build a novel benchmark of challenging multi-turn image editing, on which
CoSTA* outperforms state-of-the-art image-editing models or agents in terms of
both cost and quality, and performs versatile trade-offs upon user preference.
A three-stage approach, CoSTA*, combines LLMs and A* search to efficiently find cost-effective tool paths for multi-turn text-to-image editing, outperforming state-of-the-art models in cost and quality.
stable diffusion DALLE-3 multi-turn image editing agentic workflow tool use subtasks large language models (LLMs) graph search A* search subtask tree subgraph vision-language model (VLM) cost-efficient tool paths cost-quality trade-off multi-turn image editing benchmark