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MJev-Spatial: System One Spatial Intelligence via Native Attention Matching

Paper recorded by Signals 4 on 2026-10-15 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.CV · 计算机视觉 · first seen 2026-10-16

Abstract

Spatial intelligence lets an agent perceive and act in the physical world, and many of its judgments must be made at interaction rates: which spatial relation holds, how far an object is, or where to place it. Vision-language models (VLMs) produce these answers by free-form text generation, paying for autoregressive decoding and answer parsing even though the output space is compact: one of a few

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#263 most recent of 756 cs.CV papers we have recorded · ↑ newer: Routing Policy Optimization for Online Diffusion Reinforcement Learnin · ↓ older: Can Learning to Answer Spatial Questions Improve Novel-View Generation
Cite this page: MJev-Spatial: System One Spatial Intelligence via Native Attention Matching: the #263 most recent of 756 cs.CV papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mjev-spatial-system-one-spatial-intelligence-via-native-attention-matching.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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