activity
20212026
most citedGIT: A Generative Image-to-text Transformer for Vision and Language

211 citations · 822 across the 80 of their papers we have counts for

collaborators

90 papers

cs.CV2026

Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation

Tianyi Xiong, Zhengyuan Yang, Xiaofei Wang +10

Large vision-language models have shown strong progress in UI-to-code generation, yet their test-time self-evolution remains unstable. We first identify a fundamental obstacle, ter…

cs.CV2026

Efficient Frame Selection for Long Videos at Test Time with Attention-Based MLLM Selectors

Yilin Wang, Xiangxi Zheng, Dongxing Mao +6

Understanding long videos with multimodal large language models (MLLMs) requires selecting a compact set of frames from thousands of candidates, yet identifying the right frames se…

cs.AI2026

Planning with the Views

Kangrui Wang, Linjie Li, Zhengyuan Yang +7

Can VLMs predict how each camera move changes the view, and plan many such moves ahead? We call this capability view planning, requiring (1)understanding how a single action transf…

cs.CL2026

Toward Generalist Autonomous Research via Hypothesis-Tree Refinement

Jiajie Jin, Yuyang Hu, Kai Qiu +15

Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the result…

cs.CV2026

3D-CoS: A New 3D Reconstruction Paradigm Based on VLM Code Synthesis

Yuhao Wang, Puyi Wang, Linjie Li +3

Most recent 3D reconstruction and editing systems operate on implicit and explicit representations such as NeRF, point clouds, or meshes. While these representations enable high-fi…

cs.CV2026

Residual Decoder Adapter: ID-Preserving Tokenizer Adaption for Autoregressive Text Rendering

Dongxing Mao, Jinpeng Wang, Jiahao Tang +6

Visual Autoregressive (AR) models generate images by predicting discrete tokens that are decoded by a visual tokenizer. Despite demonstrating strong overall image generation abilit…

cs.AI2026

SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects

Puyi Wang, Yuhao Wang, Linjie Li +4

Indoor scene synthesis underpins embodied AI, robotic manipulation, and simulation-based policy evaluation, where a useful scene must specify not only what the environment looks li…

cs.CV2026

TextGround4M: A Prompt-Aligned Dataset for Layout-Aware Text Rendering

Dongxing Mao, Yilin Wang, Linjie Li +2

Despite recent advances in text-to-image generation, models still struggle to accurately render prompt-specified text with correct spatial layout -- especially in multi-span, struc…

cs.LG2026

RAGEN-2: Reasoning Collapse in Agentic RL

Zihan Wang, Chi Gui, Xing Jin +13

RL training of multi-turn LLM agents is inherently unstable, and reasoning quality directly determines task performance. Entropy is widely used to track reasoning stability. Howeve…

cs.CV2026

FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching

Junchao Yi, Rui Zhao, Jiahao Tang +7

Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We challenge this paradigm by asking wh…

stat.ML2026

Beyond identifiability: Learning causal representations with few environments and finite samples

Inbeom Lee, Tongtong Jin, Bryon Aragam

We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representation learning seeks to provide…

cs.CL2026

RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents

Jialiang Zhu, Gongrui Zhang, Xiaolong Ma +17

LLM-based deep research agents are largely built on the ReAct framework. This linear design makes it difficult to revisit earlier states, branch into alternative search directions,…

cs.HC2026

CoEditor++: Instruction-based Visual Editing via Cognitive Reasoning

Minheng Ni, Yutao Fan, Zhengyuan Yang +6

Recent advances in large multimodal models (LMMs) have enabled instruction-based image editing, allowing users to modify visual content via natural language descriptions. However,…

cs.CV2025

Glance: Accelerating Diffusion Models with 1 Sample

Zhuobai Dong, Rui Zhao, Songjie Wu +5

Diffusion models have achieved remarkable success in image generation, yet their deployment remains constrained by the heavy computational cost and the need for numerous inference…

cs.CV2025

TechImage-Bench: Rubric-Based Evaluation for Technical Image Generation

Minheng Ni, Zhengyuan Yang, Yaowen Zhang +9

We study technical image generation, where a model must synthesize information-dense, scientifically precise illustrations from detailed descriptions rather than merely produce vis…

cs.CV2025

Computer-Use Agents as Judges for Generative User Interface

Kevin Qinghong Lin, Siyuan Hu, Linjie Li +4

Computer-Use Agents (CUA) are becoming increasingly capable of autonomously operating digital environments through Graphical User Interfaces (GUI). Yet, most GUI remain designed pr…

cs.CV2025

Exploring a Unified Vision-Centric Contrastive Alternatives on Multi-Modal Web Documents

Yiqi Lin, Alex Jinpeng Wang, Linjie Li +2

Contrastive vision-language models such as CLIP have demonstrated strong performance across a wide range of multimodal tasks by learning from aligned image-text pairs. However, the…

cs.AI2025

VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agents

Kangrui Wang, Pingyue Zhang, Zihan Wang +13

A key challenge in training Vision-Language Model (VLM) agents, compared to Language Model (LLM) agents, lies in the shift from textual states to complex visual observations. This…

cs.CL2025

SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models

Cheng-Han Chiang, Xiaofei Wang, Linjie Li +7

Current large language models (LLMs) and spoken language models (SLMs) begin thinking and taking actions only after the user has finished their turn. This prevents the model from i…

cs.CL2025

InfoAgent: Advancing Autonomous Information-Seeking Agents

Gongrui Zhang, Jialiang Zhu, Ruiqi Yang +15

Building Large Language Model agents that expand their capabilities by interacting with external tools represents a new frontier in AI research and applications. In this paper, we…

cs.CV2025

EdiVal-Agent: An Object-Centric Framework for Automated, Fine-Grained Evaluation of Multi-Turn Editing

Tianyu Chen, Yasi Zhang, Zhi Zhang +13

Instruction-based image editing has advanced rapidly, yet reliable and interpretable evaluation remains a bottleneck. Current protocols either (i) depend on paired reference images…

cs.CV2025

GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them?

Yiyang Zhou, Linjie Li, Shi Qiu +10

Existing video benchmarks often resemble image-based benchmarks, with question types like "What actions does the person perform throughout the video?" or "What color is the woman's…

cs.CV2025

Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers

Zhaochen Su, Peng Xia, Hangyu Guo +12

Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-cent…

cs.CL2025

STITCH: Simultaneous Thinking and Talking with Chunked Reasoning for Spoken Language Models

Cheng-Han Chiang, Xiaofei Wang, Linjie Li +7

Spoken Language Models (SLMs) are designed to take speech inputs and produce spoken responses. However, current SLMs lack the ability to perform an internal, unspoken thinking proc…