Papers12

#Vision-Language Models

AdaptMMBench: Benchmarking Adaptive Multimodal Reasoning for Mode Selection and Reasoning Process

Xintong Zhang, Xiaowen Zhang et al.Feb 2arXiv

AdaptMMBench is a new test that checks if AI models know when to just look and think, and when to use extra visual tools like zooming or brightening an image.

#Adaptive Multimodal Reasoning#Vision-Language Models#Tool Invocation

A2Eval: Agentic and Automated Evaluation for Embodied Brain

Intermediate

Shuai Zhang, Jiayu Hu et al.Feb 2arXiv

A2Eval is a two-agent system that automatically builds and runs fair tests for robot-style vision-language models, cutting wasted work while keeping results trustworthy.

#Embodied AI#Vision-Language Models#Agentic Evaluation

Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision

Intermediate

Zhixiang Wei, Yi Li et al.Jan 27arXiv

Youtu-VL is a new kind of vision-language model that learns to predict both words and tiny image pieces, not just words.

#Vision-Language Models#Unified Autoregressive Supervision#Visual Tokenization

Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning

Intermediate

Yifan Wang, Shiyu Li et al.Jan 21arXiv

Render-of-Thought (RoT) turns the model’s step-by-step thinking from long text into slim images so the model can think faster with fewer tokens.

#Render-of-Thought#Chain-of-Thought#Latent Reasoning

ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch

Intermediate

Zheng Liu, Honglin Lin et al.Jan 20arXiv

ChartVerse is a new way to make lots of tricky, realistic charts and perfectly checked questions so AI can learn to read charts better.

#Chart reasoning#Vision-Language Models#Rollout Posterior Entropy

CoV: Chain-of-View Prompting for Spatial Reasoning

Intermediate

Haoyu Zhao, Akide Liu et al.Jan 8arXiv

This paper teaches AI to look around a 3D place step by step, instead of staring at a fixed set of pictures, so it can answer tricky spatial questions better.

#Chain-of-View Prompting#Embodied Question Answering#Active Viewpoint Reasoning

Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization

Intermediate

Mizanur Rahman, Mohammed Saidul Islam et al.Jan 8arXiv

This paper teaches a model to turn a question about a table into both a short answer and a clear, correct chart.

#Text-to-Visualization#Reinforcement Learning#GRPO

CPPO: Contrastive Perception for Vision Language Policy Optimization

Intermediate

Ahmad Rezaei, Mohsen Gholami et al.Jan 1arXiv

CPPO is a new way to fine‑tune vision‑language models so they see pictures more accurately before they start to reason.

#CPPO#Contrastive Perception Loss#Vision-Language Models

QuantiPhy: A Quantitative Benchmark Evaluating Physical Reasoning Abilities of Vision-Language Models

Intermediate

Li Puyin, Tiange Xiang et al.Dec 22arXiv

QuantiPhy is a new test that checks if AI models can measure real-world physics from videos using numbers, not guesses.

#QuantiPhy#Vision-Language Models#Physical reasoning

Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection

Intermediate

Juil Koo, Daehyeon Choi et al.Dec 15arXiv

This paper teaches robots to move their camera to a better spot before answering a question about what they see.

#Active Perception#Embodied AI#Vision-Language Models

ReVSeg: Incentivizing the Reasoning Chain for Video Segmentation with Reinforcement Learning

Intermediate

Yifan Li, Yingda Yin et al.Dec 2arXiv

ReVSeg teaches an AI to segment objects in videos by thinking step-by-step instead of guessing everything at once.

#Reasoning Video Object Segmentation#Vision-Language Models#Temporal Grounding

Visual Generation Tuning

Intermediate

Jiahao Guo, Sinan Du et al.Nov 28arXiv

Before this work, big vision-language models (VLMs) were great at understanding pictures and words together but not at making new pictures.

#Visual Generation Tuning#VGT-AE#Vision-Language Models