VLTR: Vision-Language Tool Reasoning for Instruction-Guided Image Editing

Multi-tool agents for instruction-guided image editing re-main brittle under open-loop pipelines, where early routing errors propa-gate unchecked through the entire workflow. We propose VLTR (Vision-Language Tool Reasoning), a training-free framework that reformulatesediting as closed-loop tool reasoning over a directed acyclic graph (DAG)of atomic primitives. A generative decomposer first converts each natural-language instruction into executable primitives. A Bayes-UCB routerthen adaptively selects tools by combining contextual statistics withsemantic priors, balancing exploration and exploitation online. Aftereach execution, a heteroscedastic verifier produces a calibrated quality–uncertainty signal (q, σ 2 ) through inverse-variance fusion of three com-plementary assessment tiers. A verifier-guided scheduler uses this jointsignal to retry, reroute, or replan only the failed subgraph while preserv-ing validated ancestors, turning full-pipeline restarts into efficient localrepair. On PIE-Bench++, VLTR achieves the best overall VLM rankingamong all evaluated baselines. On MagicBrush, it delivers a 4.8× run-time speedup over the strongest multi-tool competitor with no qualityloss. Pilot evaluations on GEdit-Bench and RISEBench further confirmgeneralization to challenging edits.