1 citations · 1 across the 10 of their papers we have counts for
12 papers
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents
Xingjian Diao, Tianyu Yang, Wenjun Huang +8
Reinforcement learning improves tool-use agents, yet fine-grained credit assignment remains challenging even with step-level feedback. An observed error may be inherited from an ea…
AudioLens: Multi-Perspective Speech Clustering with Reasoning Audio-Language Models
Wenjun Huang, Qiaosong Chu, Tiger Shao +11
Audio clustering is a fundamental task for organizing rapidly growing speech collections, supporting applications such as conversational analysis and speech-driven discovery. Howev…
The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering
Yefan Zhou, Yilun Zhou, Austin Xu +3
Generative verifiers have emerged as a promising paradigm for step-wise verification, but their verification behavior is often poorly calibrated: they may be under-critical and mis…
Exploiting Label-Independent Regularization from Spatial Dependencies for Whole Slide Image Analysis
Weiyi Wu, Xinwen Xu, Chongyang Gao +3
Whole slide images, with their gigapixel-scale panoramas of tissue samples, are pivotal for precise disease diagnosis. However, their analysis is hindered by immense data size and…
Addressing Overthinking in Large Vision-Language Models via Gated Perception-Reasoning Optimization
Xingjian Diao, Zheyuan Liu, Chunhui Zhang +6
Large Vision-Language Models (LVLMs) have exhibited strong reasoning capabilities through chain-of-thought mechanisms that generate step-by-step rationales. However, such slow-thin…
ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering
Xingjian Diao, Weiyi Wu, Keyi Kong +5
Visual Question Answering (VQA) is increasingly used in diverse applications ranging from general visual reasoning to safety-critical domains such as medical imaging and autonomous…
Learning Sparsity for Effective and Efficient Music Performance Question Answering
Xingjian Diao, Tianzhen Yang, Chunhui Zhang +3
Music performances, characterized by dense and continuous audio as well as seamless audio-visual integration, present unique challenges for multimodal scene understanding and reaso…
SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models
Xingjian Diao, Chunhui Zhang, Keyi Kong +6
While large language models have demonstrated impressive reasoning abilities, their extension to the audio modality, particularly within large audio-language models (LALMs), remain…
Music Audio-Visual Question Answering Requires Specialized Multimodal Designs
Wenhao You, Xingjian Diao, Wenjun Huang +9
While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music necessitate tailored approaches. Music Au…
Assessing and Mitigating Medical Knowledge Drift and Conflicts in Large Language Models
Weiyi Wu, Xinwen Xu, Chongyang Gao +4
Large Language Models (LLMs) have great potential in the field of health care, yet they face great challenges in adapting to rapidly evolving medical knowledge. This can lead to ou…
Learning Musical Representations for Music Performance Question Answering
Xingjian Diao, Chunhui Zhang, Tingxuan Wu +4
Music performances are representative scenarios for audio-visual modeling. Unlike common scenarios with sparse audio, music performances continuously involve dense audio signals th…
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
Xingjian Diao, Chunhui Zhang, Weiyi Wu +5
Multimodal foundation models (MFMs) have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval. However, these models fa…