HarnessPainter: Grounding Visual Reasoning in Executable Processes for In-Context Stroke Organization

Stroke-based rendering algorithms generate complete painting programs via pixel-level reconstruction objectives, but they frequently produce disordered execution sequences, inverted structural layers, and redundant stroke fragments. We present HarnessPainter, a tool-augmented framework that formulates stroke reorganization as closed-loop, in-context program editing. Using an initial stroke sequence, a vision–language agent inspects target images, dynamic intermediate keyframes, and localized visual evidence to propose discrete structural edits, such as reordering procedural stages and consolidating adjacent contour fragments. A deterministic execution harness verifies these proposals: an integrated differentiable rendering backend locally optimizes continuous curve parameters while preserving global topology and unedited execution orders. Proposals are deterministically replayed and accepted only upon passing local, global, and event-aligned consistency checks. Evaluations show HarnessPainter rectifies fragmented artifacts and enforces coherent long-range drawing dependencies while preserving fidelity, demonstrating how executable visual environments ground high-level reasoning for procedural graphics generation.