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Can a vision-language model make useful spatial judgments without generating an answer sentence? This episode of *Embodied AI 101* explains how MJev-Spatial turns that question into a concrete model design and experimental comparison. We cover three connected ideas: reusing pretrained attention projections to score candidates, refining numerical and pointing outputs through successive choices, and evaluating accuracy alongside end-to-end inference latency. The discussion follows the Qwen3.5-based model's spatial-perception interface across choices, quantities, and image locations, including its crop-based pointing procedure and publicly released implementation. We also connect the work to Jev-style decision models, MolmoPoint's grounding-token approach, and the placement problems that make image pointing useful for robotics. The experimental focus is offline visual spatial prediction, including robotic grounding and placement benchmarks. The central limitation is the missing same-backbone, same-data autoregressive fine-tuning baseline, which leaves the source of the accuracy improvement unresolved. For researchers and practitioners building perception–planning systems, the episode asks a practical architectural question: if your next component needs a coordinate or a choice, why make the model write a sentence first?
