Papers22

#vision-language model

PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation

Yuanhao Cai, Kunpeng Li et al.Dec 31arXiv

This paper teaches text-to-video models to follow real-world physics, so people, balls, water, glass, and fire act the way they should.

#text-to-video generation#physical consistency#direct preference optimization

SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning

Intermediate

Yong Xien Chng, Tao Hu et al.Dec 30arXiv

SenseNova-MARS is a vision-language model that can think step-by-step and use three tools—text search, image search, and image cropping—during its reasoning.

#multimodal agent#vision-language model#reinforcement learning

Figure It Out: Improve the Frontier of Reasoning with Executable Visual States

Intermediate

Meiqi Chen, Fandong Meng et al.Dec 30arXiv

FIGR is a new way for AI to ‘think by drawing,’ using code to build clean, editable diagrams while it reasons.

#executable visual states#diagrammatic reasoning#reinforcement learning for reasoning

Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion

Intermediate

Yi Zhou, Xuechao Zou et al.Dec 28arXiv

Co2S is a new way to train segmentation models with very few labels by letting two different students (CLIP and DINOv3) learn together and correct each other.

#semi-supervised segmentation#remote sensing#pseudo-label drift

Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone

Intermediate

Jiacheng Ye, Shansan Gong et al.Dec 27arXiv

Dream-VL and Dream-VLA use a diffusion language model backbone to understand images, talk about them, and plan actions better than many regular (autoregressive) models.

#diffusion language model#vision-language model#vision-language-action

PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence

Intermediate

Xiaopeng Lin, Shijie Lian et al.Dec 18arXiv

Robots learn best from what they would actually see, which is a first-person (egocentric) view, but most AI models are trained on third-person videos and get confused.

#egocentric vision#first-person video#vision-language model

RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics

Intermediate

Enshen Zhou, Cheng Chi et al.Dec 15arXiv

RoboTracer is a vision-language model that turns tricky, word-only instructions into safe, step-by-step 3D paths (spatial traces) robots can follow.

#RoboTracer#spatial trace#3D spatial referring

InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models

Intermediate

Hongyuan Tao, Bencheng Liao et al.Dec 9arXiv

InfiniteVL is a vision-language model that mixes two ideas: local focus with Sliding Window Attention and long-term memory with a linear module called Gated DeltaNet.

#InfiniteVL#linear attention#Gated DeltaNet

MIND-V: Hierarchical Video Generation for Long-Horizon Robotic Manipulation with RL-based Physical Alignment

Intermediate

Ruicheng Zhang, Mingyang Zhang et al.Dec 7arXiv

Robots need lots of realistic, long videos to learn, but collecting them is slow and expensive.

#hierarchical video generation#robotic manipulation#long-horizon planning

Self-Improving VLM Judges Without Human Annotations

Intermediate

Inna Wanyin Lin, Yushi Hu et al.Dec 2arXiv

The paper shows how a vision-language model (VLM) can train itself to be a fair judge of answers about images without using any human preference labels.

#vision-language model#VLM judge#reward model

1 2