1 citations · 1 across the 11 of their papers we have counts for
14 papers
VideoResearcher: Self-Improving Tool Design for Long-Video Understanding
Dingqiang Ye, Dongdi Zhao, Kaishen Wang +11
Video agents have made substantial progress in long-video understanding. Yet effective video-agent systems require costly, time-consuming manual design and trial and error. Current…
Where to Look Matters: On-Policy Self-Distillation for Long-Video Understanding
Kaishen Wang, Dongdi Zhao, Yijun Liang +4
Vision-language models (VLMs) have made substantial progress in long-video understanding, with standard backbone models typically answering questions from frames sampled across the…
Self-Supervised Visual On-Policy Distillation
Yijiang Li, Yijun Liang, Yunjie Tian +6
Visual on-policy distillation relies heavily on an informative teacher-student asymmetry, through either a larger, stronger teacher or privileged supervision, such as reference ans…
On-Policy Self-Distillation without Any Supervision
Yijiang Li, Bingyang Wang, Yijun Liang +3
On-policy (Self-)Distillation (OPD / OPSD) has shown strong potential for post-training large language models (LLMs). However, existing methods still rely heavily on external super…
Visual Contrastive Self-Distillation
Yijun Liang, Yunjie Tian, Yijiang Li +5
On-policy self-distillation (OPSD) removes the external teacher required by on-policy distillation (OPD), but still needs asymmetric information between teacher and student so that…
Self-Evolving Visual Questioner
Yijun Liang, Hengguang Zhou, Ming Li +3
Vision-language models (VLMs) are typically trained as passive answerers, while their ability to actively ask diverse, non-trivial, visual-centric and grounded questions remains un…
LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment
Han Chen, Ming Li, Chenguang Wang +4
Existing work on LLM-based educational assessment has focused largely on item difficulty, but difficulty alone does not indicate whether an item meaningfully distinguishes higher-…
Do Prompt-Elicited Trajectories Reflect Training-Time Reward Hacking? A Systematic Study on Monitoring Training-Time Reward Hacking in Code Generation
Lichen Li, Hengguang Zhou, Yijun Liang +2
Reward hacking in code generation, where models exploit evaluation loopholes to obtain high reward without correctly solving the intended task, poses a critical challenge for Reinf…
History-Conditioned Spatio-Temporal Visual Token Pruning for Efficient Vision-Language Navigation
Qitong Wang, Yijun Liang, Ming Li +2
Vision-Language Navigation (VLN) enables robots to follow natural-language instructions in visually grounded environments, serving as a key capability for embodied robotic systems.…
V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions
Chenrui Fan, Yijun Liang, Shweta Bhardwaj +3
While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle i…
CaughtCheating: Is Your MLLM a Good Cheating Detective? Exploring the Boundary of Visual Perception and Reasoning
Ming Li, Chenguang Wang, Yijun Liang +6
Recent agentic Multi-Modal Large Language Models (MLLMs) such as GPT-o3 have achieved near-ceiling scores on various existing benchmarks, motivating a demand for more challenging t…
ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness
Yijun Liang, Ming Li, Chenrui Fan +7
Color plays an important role in human perception and usually provides critical clues in visual reasoning. However, it is unclear whether and how vision-language models (VLMs) can…
Diffusion Curriculum: Synthetic-to-Real Data Curriculum via Image-Guided Diffusion
Yijun Liang, Shweta Bhardwaj, Tianyi Zhou
Low-quality or scarce data has posed significant challenges for training deep neural networks in practice. While classical data augmentation cannot contribute very different new da…
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning
Ming Li, Pei Chen, Chenguang Wang +5
Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primar…