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  3. GAP-MLLM: Geometry-Aligned Pre-training for Activating 3D Sp
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GAP-MLLM: Geometry-Aligned Pre-training for Activating 3D Spatial Perception in Multimodal Large Language Models

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0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: GAP-MLLM: Geometry-Aligned Pre-training for Activating 3D Spatial Perception in Multimodal Large Language Models

PDF: https://arxiv.org/pdf/2603.16461v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Paper Mode

GAP-MLLM: Geometry-Aligned Pre-training for Activating 3D Spatial Perception in Multimodal Large Language Models

Overall score: 7/10
Lineage: 6e2555786451…
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Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

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Dimensions overall score 7.0

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Keep exploring

Builds On This
GeoSense: Internalizing Geometric Necessity Perception for Multimodal Reasoning
Score 6.0down
Builds On This
Multimodal Language Models Cannot Spot Spatial Inconsistencies
Score 3.0down
Builds On This
Concise Geometric Description as a Bridge: Unleashing the Potential of LLM for Plane Geometry Problem Solving
Score 6.0down
Prior Work
Boosting MLLM Spatial Reasoning with Geometrically Referenced 3D Scene Representations
Score 7.0stable
Prior Work
Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training
Score 7.0stable
Prior Work
LatentGeo: Learnable Auxiliary Constructions in Latent Space for Multimodal Geometric Reasoning
Score 7.0stable
Prior Work
Generation Models Know Space: Unleashing Implicit 3D Priors for Scene Understanding
Score 7.0stable
Higher Viability
Point Cloud as a Foreign Language for Multi-modal Large Language Model
Score 9.0up

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