MJev-Spatial: Faster VLM Spatial Reasoning Without Generating Text
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…
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