Vision foundation tools such as open-vocabulary detectors, segmentation models, and post-processing operators are powerful building blocks for computer vision, but their effectiveness depends heavily on how they are orchestrated: which tools are used, in what order, with what parameters, and under what visual conditions. Existing visual-programming agents typically generate a fixed solution pipeline, making them brittle under dense objects, occlusion, small targets, and domain shift. We introduce VTOS (Vision Tools Orchestration Search), a framework for adaptive visual tool orchestration through joint solution–observer search. VTOS co-searches executable solution programs that compose vision tools such as Grounding DINO, SAM, NMS, and slice-and-detect, together with observer programs that diagnose candidate solutions, identify failure modes, and generate actionable feedback. These observations are accumulated in a shared VisionThoughts knowledge base to guide subsequent search. We evaluate VTOS through two case studies: dense object counting on LVIS-Count and zero-shot plant-disease segmentation on PlantSeg-OOD, which stress different orchestration challenges including threshold calibration, NMS, slicing, mask refinement, and domain generalization. Across both tasks, VTOS outperforms static tool pipelines and agentic visual-programming baselines, specifically in complex settings such as dense, occluded scenes and out-of-distribution segmentation where static pipelines leave measurable headroom, rather than in standard tasks where a single well-calibrated tool already approaches its ceiling.

Large vision-language models (LVLMs) often hallucinate due to over-reliance on language priors. Existing methods implicitly assume that visual tokens maintain consistent semantics across layers and thus indiscriminately inject shallow-layer visual information into deeper layers. We identify a new phenomenon, termed correlation flip, where a visual token positively correlated with the decoding context in an intermediate layer becomes negatively correlated in the final layer, indicating that correct visual evidence is suppressed before prediction. Based on this finding, we propose Correlation-Flip Visual Injection (CFVI), a simple and training-free decoding-time method that selectively identifies correlation-flip tokens and injects corrective visual signals into the final-layer hidden state to restore suppressed evidence and mitigate hallucinations. CFVI can be directly applied to existing LVLMs, and introduces minimal computational overhead. Experiments on multiple benchmarks demonstrate that CFVI achieves strong performance while preserving language fluency.