arXiv AI

Modeling The Object Representations Underlying Human Physical Reasoning

arXiv AI

Jul 7

Human-like Object Grouping in Self-supervised Vision Transformers

arXiv:2603. 13994v2 Announce Type: replace-cross Abstract: Vision foundation models trained with self-supervised objectives achieve strong performance across diverse tasks and exhibit emergent object segmentation properties.

By Hossein Adeli, Seoyoung Ahn, Andrew Luo, Mengmi Zhang, Nikolaus Kriegeskorte, Gregory Zelinsky

llms computer-vision efficiency benchmarks safety

arXiv Computer Vision

Sep 16

Extremely coarse learning objectives induce human-aligned representations in AI vision models

arXiv:2605.05556v2 Announce Type: replace Abstract: Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that...

By Yash Mehta, Michael F. Bonner

llms rag safety

arXiv AI

Jul 8

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction

arXiv:2607. 05568v1 Announce Type: cross Abstract: Representing 3D shapes as compact sets of geometric primitives is fundamental to robotics, simulation, and scene understanding.

By Gregor Kobsik, Tim Elsner, Leif Kobbelt

llms computer-vision robotics fine-tuning multimodal

arXiv Computer Vision

Sep 16

PiPS: Post-Hoc Prototypical Explanations for Interpretable Semantic Segmentation

arXiv:2609.16909v1 Announce Type: new Abstract: With the increasing deployment of deep neural networks in critical systems, such as medical diagnostics and autonomous vehicles, ensuring their interpr...

By Mi{\l}osz Adamczyk, Tymoteusz Zapala, Piotr Borycki, Przemys{\l}aw Spurek

agents computer-vision fine-tuning safety

arXiv AI

Aug 18

FloodReasonBench: Benchmarking VLM Reasoning Segmentation for Embodied Flood Response at the Edge

arXiv:2608. 15410v1 Announce Type: cross Abstract: Reasoning segmentation enables vision-language models (VLMs) to translate mission-relevant language requests into pixel-level visual grounding, offering a natural perception interface for embodied agents.

By Rajat Bhattacharjya, Yoomee Jung, Minwoo Kim, Sing-Yao Wu, Eli Bozorgzadeh, Nalini Venkatasubramanian, Nikil Dutt

llms agents computer-vision robotics multimodal benchmarks

arXiv Computer Vision

Sep 10

RAU: Reference-based Anatomical Understanding with Vision Language Models

arXiv:2509.22404v2 Announce Type: replace Abstract: Anatomical understanding, which is the ability to identify, localize, or segment anatomical structures, is critical in medical image analysis; howe...

By Yiwei Li, Yikang Liu, Jiaqi Guo, Lin Zhao, Zheyuan Zhang, Xiao Chen, Boris Mailhe, Ankush Mukherjee, Terrence Chen, Shanhui Sun

llms computer-vision nlp fine-tuning multimodal

arXiv Computer Vision

3d ago

SYNCR: A Cross-Video Reasoning Benchmark with Synthetic Grounding

SYNCR is a synthetic benchmark designed to evaluate multimodal large language models on cross‑video reasoning. It contains 4,000 question‑answer pairs across 4,827 unique videos, covering tasks in temporal alignment, spatial tracking, comparative reasoning, and holistic synthesis. The benchmark reveals a significant performance gap between current models and humans, with models excelling at temporal ordering but struggling with precise physical and spatial reasoning.

By Sara Ghazanfari, Siddharth Garg, Prashanth Krishnamurthy, Farshad Khorrami

llms robotics multimodal benchmarks safety

arXiv AI

Jul 28

Spatial-IQ: Deconstructing Spatial Intelligence via Hierarchical Capability Tests

arXiv:2607. 22864v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) excel at visual interpretation but fail on spatial reasoning tasks that humans solve reliably.

By Patrick Rim, Tom Long, Ekta Prashnani, Ruth Rosenholtz, Ben Boudaoud, Peter Xenopoulos, Alex Wong, Joohwan Kim, Jae-Hyun Jung

llms reinforcement-learning multimodal benchmarks

arXiv Computer Vision

Sep 10

StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning

arXiv:2605.03927v3 Announce Type: replace Abstract: Vision-language models have demonstrated strong performance across robotic perception and instruction-following tasks. However, they still struggle...

By Xiaowen Sun, Matthias Kerzel, Mengdi Li, Xufeng Zhao, Paul Striker, Stefan Wermter

llms robotics multimodal benchmarks

arXiv AI

3d ago

Prompting Image Generators for Training-free Primitive Shape Abstraction

arXiv:2607.05568v2 Announce Type: replace-cross Abstract: Compact primitive abstractions represent 3D shapes with a few geometric primitives while preserving recognizable components. Learned methods...

By Gregor Kobsik, Tim Elsner, Leif Kobbelt

llms computer-vision fine-tuning multimodal

arXiv AI

Jul 14

PhysMRV: Physical Memory Retrieval and Verification for Physics Plausibility Reasoning

arXiv:2607. 10190v1 Announce Type: cross Abstract: Video-language models (VLMs) have achieved remarkable performance on video understanding and visual question answering, yet they remain unreliable in reasoning about physical plausibility, where understanding object interactions, causal dynamics, and fundamental physical principles is essential.

By Wenyuan Wang, Lianyu Hu, Hao Wang, Yang Liu

llms rag nlp fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 19

Do Vision-Language Models Understand 3D Scenes or Just Catalogue Objects?

arXiv:2605. 20448v2 Announce Type: replace-cross Abstract: Vision-language models reliably name objects in a scene, but do they represent the 3D layout those objects inhabit?

By Animesh Maheshwari, Divyansh Sahu, Nishit Verma

llms robotics multimodal benchmarks

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