83 citations · 242 across the 62 of their papers we have counts for
11 papers · 1 filter
UA-Bench: Benchmarking Large Audio-Language Models on Underwater Acoustic Recognition
Zhe Sun, Liang An, Jiayu Yao +9
Large audio-language models (LALMs) are fast becoming a general interface to sound, yet how far their broad pretraining carries into specialized domains remains largely open. Vesse…
Thinking with Anchors: Grounded and Efficient Document Reasoning
Sichen Zhu, Yuchen Zhu, Wenzhuo Xu +13
Existing document understanding benchmarks have largely focused on locating page elements, yet real-world document intelligence requires models to reason jointly about region seman…
Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation
Shanshan Lyu, Yiwei Wang, Yujun Cai +2
Dense retrieval ranks one query vector against one document vector. On long documents, this interface can fail when a short but decisive span is weakened during document encoding b…
Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models
Boyan Han, Yiwei Wang, Yi Song +2
Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally support format-constrained task…
Semantic-Aware Adaptive Visual Memory for Streaming Video Understanding
Hang Wu, Sherin Mary Mathews, Yujun Cai +2
Online streaming video understanding requires models to process continuous visual inputs and respond to user queries in real time, where the unbounded stream and unpredictable quer…
VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG
Honghao Fu, Miao Xu, Yiwei Wang +3
Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generation (RAG) is a promising remedy by organ…
Hierarchical, Interpretable, Label-Free Concept Bottleneck Model
Haodong Xie, Yujun Cai, Rahul Singh Maharjan +3
Concept Bottleneck Models (CBMs) introduce interpretability to black-box deep learning models by predicting labels through human-understandable concepts. However, unlike humans, wh…
Finding Distributed Object-Centric Properties in Self-Supervised Transformers
Samyak Rawlekar, Amitabh Swain, Yujun Cai +3
Self-supervised Vision Transformers (ViTs) like DINO show an emergent ability to discover objects, typically observed in [CLS] token attention maps of the final layer. However, the…
HighlightBench: Benchmarking Markup-Driven Table Reasoning in Scientific Documents
Lexin Wang, Shenghua Liu, Yiwei Wang +5
Visual markups such as highlights, underlines, and bold text are common in table-centric documents. Although multimodal large language models (MLLMs) have made substantial progress…
CamReasoner: Reinforcing Camera Movement Understanding via Structured Spatial Reasoning
Hang Wu, Yujun Cai, Zehao Li +4
Understanding camera dynamics is a fundamental pillar of video spatial intelligence. However, existing multimodal models predominantly treat this task as a black-box classification…
Self-Manager: Parallel Agent Loop for Long-form Deep Research
Yilong Xu, Zhi Zheng, Xiang Long +2
Long-form deep research requires multi-faceted investigations over extended horizons to get a comprehensive report. When handling such complex tasks, existing agents manage context…