arXiv:2607.03530 proposes the think-with-SVG reasoning paradigm, treating scalable vector graphics as the MLLM's intermediate "mental image", through two-stage training (SFT cold start + GRPO multi-turn RL) on Qwen2.5-VL-7B achieving MindCube from 36.0% to 76.0%, VSIBench from 31.0% to 55.1%, surpassing GPT-5 and Gemini-2.5-pro. The "visual ungrounding" of multimodal large models in spatial reasoning has long been criticized — pure language CoT is prone to geometric-relationship drift, while Think-with-Image depends on external tools and breaks differentiability. ByteDance Seed's team, in MentalThink (2607.03530) posted to arXiv on July 3, gives a clever compromise: let the MLLM directly generate SVG code as the intermediate representation of the "mental image", which is deterministically rendered and fed back into itself, forming a closed loop of "hypothesize → render → reflect". The core design is think-with-SVG: each step's reasoning state is defined as a tuple of (text ℓ, SVG code σ), the model can "draw" a vector draft, have the SVG engine render it into pixels, and then put the pixels back into the visual context for the next round of reflection. Training is in two stages — first use 200k SFT data (visual-syntax alignment + thought externalization + general spatial perception) to let the model "write SVG", then use GRPO multi-turn RL with mixed reward of λ_fmt=0.25, λ_ans=0.75 to "draw correctly". The effect is quite explosive: on Qwen2.5-VL-7B base, MindCube is pulled from 36.0% to 76.0% (+40.0pp), VSIBench rises 24.1pp to 55.1% — surpassing GPT-5 (55.0%) and Gemini-2.5-pro (53.5%). Even more interesting is the System 1/2 self-adaptation: simple visual perception tasks use only 1.19 rounds, 18.5% trigger SVG, 109 tokens; complex reasoning tasks switch to 1.64 rounds, 63.7% SVG, 544 tokens — the model spontaneously throws more test-time compute at harder geometry problems. Opinion: MentalThink's real contribution isn't yet another CoT extension, but elevating SVG, this "machine-friendly structured visual language", to a reasoning medium, sidestepping the sparseness of BBox and the geometric drift of pure text. If extended to longer spatiotemporal planning and embodied decision-making, "let the model draw first, then think" could become the de facto standard of the next wave of spatial intelligence.