1 citations · 1 across the 18 of their papers we have counts for
21 papers
RoboFollow: Unveiling the Instruction Following Mirage in Embodied Agents
Chang Guo, Yukun Xie, Bohan Tan +6
Modern embodied agents achieve impressive success rates, yet their actual instruction-following ability is far weaker than these numbers suggest. We trace this illusion to a struct…
ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog
Lingao Xiao, Yalun Dai, Yangyu Huang +17
Despite growing automation, turning a paper into a coherent poster, talk video, and blog piece often remains a labor-intensive last mile. Recent systems increasingly generate multi…
OmniPresent: Generating Coherent Presentation Suites from Scientific Papers
Qianli Ma, Jipeng Xiao, Siyu Wang +7
Transforming static research papers into dynamic media such as posters, slides, and videos is essential for effective dissemination but remains a labor-intensive challenge. Existin…
Why Struggle with Continuous Latents? Interpretable Discrete Latent Reasoning via Rendered Compression
Shuochen Chang, Qingyang Liu, Shaobo Wang +8
Large language models achieve high reasoning performance via explicit chain-of-thought and reinforcement learning, but require long output sequences and extended inference time. La…
Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention
Shuochen Chang, Tong Bai, Xiaofeng Zhang +5
Latent reasoning enables Large Language Models (LLMs) to perform multi-step inference within continuous hidden states, offering efficiency gains over explicit Chain-of-Thought (CoT…
The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models
Runhao Mao, Hanshi Wang, Yixiang Yang +3
The integration of Vision-Language Models (VLMs) into autonomous driving promises to solve long-tail scenarios, but this paradigm faces the critical and unaddressed challenge of ca…
Group Editing: Edit Multiple Images in One Go
Yue Ma, Xinyu Wang, Qianli Ma +9
In this paper, we tackle the problem of performing consistent and unified modifications across a set of related images. This task is particularly challenging because these images m…
Reliable Reasoning in SVG-LLMs via Multi-Task Multi-Reward Reinforcement Learning
Haomin Wang, Qi Wei, Qianli Ma +4
With the rapid advancement of vision-language models, an increasing number of studies have explored their potential for SVG generation tasks. Although existing approaches improve p…
Understanding Degradation with Vision Language Model
Guanzhou Lan, Chenyi Liao, Yuqi Yang +5
Understanding visual degradations is a critical yet challenging problem in computer vision. While recent Vision-Language Models (VLMs) excel at qualitative description, they often…
A Very Big Video Reasoning Suite
Maijunxian Wang, Ruisi Wang, Juyi Lin +53
Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grounds intelligence in spatiotemporally c…
The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms
Lingdong Kong, Shaoyuan Xie, Zeying Gong +135
Autonomous systems are increasingly deployed in open and dynamic environments -- from city streets to aerial and indoor spaces -- where perception models must remain reliable under…
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
Qianli Ma, Chang Guo, Zhiheng Tian +4
Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Curren…
CircuitSeer: Mining High-Quality Data by Probing Mathematical Reasoning Circuits in LLMs
Shaobo Wang, Yongliang Miao, Yuancheng Liu +3
Large language models (LLMs) have demonstrated impressive reasoning capabilities, but scaling their performance often relies on massive reasoning datasets that are computationally…
RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling
Bingjie Gao, Qianli Ma, Xiaoxue Wu +9
Prompt design plays a crucial role in text-to-video (T2V) generation, yet user-provided prompts are often short, unstructured, and misaligned with training data, limiting the gener…
Human-Agent Collaborative Paper-to-Page Crafting
Qianli Ma, Siyu Wang, Yilin Chen +7
In the quest for scientific progress, communicating research is as vital as the discovery itself. Yet, researchers are often sidetracked by the manual, repetitive chore of building…
AnimateScene: Camera-controllable Animation in Any Scene
Qingyang Liu, Bingjie Gao, Weiheng Huang +10
Recent advances in 3D scene reconstruction and 4D human animation have broadened adoption, but integrating the two remains difficult. Key challenges include placing humans at plaus…
LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint
Qianli Ma, Dongrui Liu, Qian Chen +2
Fine-tuning pre-trained Large Language Models (LLMs) for specialized tasks incurs substantial computational and data costs. While model merging offers a training-free solution to i…
Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for Free
Evelyn Zhang, Bang Xiao, Jiayi Tang +5
Stable Diffusion has achieved remarkable success in the field of text-to-image generation, with its powerful generative capabilities and diverse generation results making a lasting…
VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models
Ziqi Huang, Fan Zhang, Xiaojie Xu +14
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable…
Efficient Diffusion as Low Light Enhancer
Guanzhou Lan, Qianli Ma, Yuqi Yang +4
The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether tr…
Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning
Qianli Ma, Xuefei Ning, Dongrui Liu +2
Diffusion models are trained by learning a sequence of models that reverse each step of noise corruption. Typically, the model parameters are fully shared across multiple timesteps…