PhotoFlow is a closed-loop agent framework that searches for camera parameters in 3D scenes according to language intent and outperforms one-shot, reflection, and random baselines on the new VPhotoBench of 47 scenes and 141 missions.
Visual spatial reasoning.Transactions of the Association for Computational Linguistics, 11:635–651
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DriveSpatial benchmark shows the best of 15 VLMs trails humans by 28.4 points on spatiotemporal driving tasks, with cognitive scene construction as the main failure mode.
VGenST-Bench is a new video benchmark for MLLM spatio-temporal reasoning built via generative synthesis, a multi-agent pipeline with human oversight, a 3x2x2 taxonomy, and hierarchical tasks separating perception from reasoning.
VLMs fail to ground numerical values in spatial perception on new bidirectional tasks, relying on shallow cues instead of coordinate-aware representations.
VLMs achieve 53-97% on volumetric rearrangement planning but only 6-45% on occlusion and under 7% on reflections in a new 3,034-sample benchmark, with white-box analysis localizing the failure to visual-token merger in Qwen3-VL-8B-Thinking.
Spatial Prediction pretext task learns spatial structure in self-supervised learning by regressing relative position and scale between image views, yielding more structured representations and better generalization.
Slot-MLLM introduces a slot-attention-based object-centric visual tokenizer with Q-Former encoder, diffusion decoder, and residual vector quantization for improved local visual comprehension and generation in multimodal LLMs.
LENS is a new multi-level benchmark dataset for evaluating MLLMs on perception-to-reasoning tasks using the same images across all levels with recent social media content.
citing papers explorer
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PhotoFlow: Agentic 3D Virtual Photography Missions
PhotoFlow is a closed-loop agent framework that searches for camera parameters in 3D scenes according to language intent and outperforms one-shot, reflection, and random baselines on the new VPhotoBench of 47 scenes and 141 missions.
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DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving
DriveSpatial benchmark shows the best of 15 VLMs trails humans by 28.4 points on spatiotemporal driving tasks, with cognitive scene construction as the main failure mode.
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VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis
VGenST-Bench is a new video benchmark for MLLM spatio-temporal reasoning built via generative synthesis, a multi-agent pipeline with human oversight, a 3x2x2 taxonomy, and hierarchical tasks separating perception from reasoning.
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SPACENUM: Revisiting Spatial Numerical Understanding in VLMs
VLMs fail to ground numerical values in spatial perception on new bidirectional tasks, relying on shallow cues instead of coordinate-aware representations.
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Do Vision--Language Models Understand 3D Scenes or Just Catalogue Objects?
VLMs achieve 53-97% on volumetric rearrangement planning but only 6-45% on occlusion and under 7% on reflections in a new 3,034-sample benchmark, with white-box analysis localizing the failure to visual-token merger in Qwen3-VL-8B-Thinking.
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Learning to Perceive "Where": Spatial Pretext Tasks for Robust Self-Supervised Learning
Spatial Prediction pretext task learns spatial structure in self-supervised learning by regressing relative position and scale between image views, yielding more structured representations and better generalization.
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Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM
Slot-MLLM introduces a slot-attention-based object-centric visual tokenizer with Q-Former encoder, diffusion decoder, and residual vector quantization for improved local visual comprehension and generation in multimodal LLMs.
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LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models
LENS is a new multi-level benchmark dataset for evaluating MLLMs on perception-to-reasoning tasks using the same images across all levels with recent social media content.