Papers by Xingjian Diao
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding (2025.findings-naacl)
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Xingjian Diao, Chunhui Zhang, Weiyi Wu, Zhongyu Ouyang, Peijun Qing, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Multimodal foundation models have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval. |
| Approach: | They propose a specialized cognitive module, temporal working memory, which selectively retains task-relevant information across temporal dimensions. |
| Outcome: | The module retains task-relevant information across temporal dimensions, ensuring that critical details are preserved throughout the processing of video and audio content. |
Learning Sparsity for Effective and Efficient Music Performance Question Answering (2025.acl-short)
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| Challenge: | Existing Music AVQA methods rely on dense and unoptimized representations, leading to inefficiencies in the isolation of key information, reduction of redundancy, and prioritization of critical samples. |
| Approach: | They propose a sparse learning framework specifically designed for Music AVQA to address these challenges. |
| Outcome: | The proposed framework reduces training time by 28.32% while maintaining accuracy while maintaining state-of-the-art performance on the Music AVQA datasets. |
Knowing More, Acting Better: Hierarchical Representation for Embodied Decision-Making (2025.findings-emnlp)
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| Challenge: | Modern embodied AI uses multimodal large language models as policy models, predicting actions from final-layer hidden states. |
| Approach: | They propose a hierarchical action probing method that aggregates representations from all layers, mirroring the brain's multi-level organization. |
| Outcome: | Experiments show that hierarchical probing improves on last-layer embodied models and achieves a 46.6% success rate and a 62.5% gain in spatial reasoning tasks. |
Music Audio-Visual Question Answering Requires Specialized Multimodal Designs (2026.findings-acl)
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Wenhao You, Xingjian Diao, Wenjun Huang, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Tingxuan Wu, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Music audio-visual question answering presents unique challenges with dense audio-visual content, intricate temporal dynamics, and the need for domain-specific knowledge. |
| Approach: | They analyze Music AVQA datasets and analyze their results to identify key design patterns . they propose concrete future directions for incorporating musical priors . |
| Outcome: | The proposed architectures are critical for success in Music AVQA, the authors argue . they suggest concrete future directions for incorporating musical priors . |
Addressing Overthinking in Large Vision-Language Models via Gated Perception-Reasoning Optimization (2026.findings-acl)
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Xingjian Diao, Zheyuan Liu, Chunhui Zhang, Weiyi Wu, Keyi Kong, Lin Shi, Kaize Ding, Soroush Vosoughi, Jiang Gui
| Challenge: | Prior work has attempted to mitigate this issue by using adaptive reasoning strategies, but these methods overlook a fundamental bottleneck: visual perception failures. |
| Approach: | They propose a meta-reasoning controller that dynamically routes computation among three decision paths at each generation step. |
| Outcome: | The proposed method outperforms slow-thinking methods while producing shorter responses. |
Learning Musical Representations for Music Performance Question Answering (2024.findings-emnlp)
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| Challenge: | Existing methods for audio-visual learning fail to consider the distinctive characteristics of instruments and music. |
| Approach: | They propose to integrate multimodal interactions within the context of music data and annotate and release rhythmic and music sources in the current music datasets to enable the model to learn music characteristics. |
| Outcome: | The proposed model can learn music characteristics from the current music datasets and align its predictions with the temporal dimension. |
Tailoring Memory Granularity for Multi-Hop Reasoning over Long Contexts (2026.findings-eacl)
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| Challenge: | Extensive experiments on long-context multi-hop question answering benchmarks show TAG achieves state-of-the-art performance. |
| Approach: | They propose a framework that prestructures memory into diverse granularities and employs a reward-guided navigator to adaptively compose hybrid memory tailored to each query. |
| Outcome: | Experiments on long-context multi-hop question answering show that the framework achieves state-of-the-art performance. |
AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality (2024.emnlp-main)
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| Challenge: | Recent studies combine LoRA with Mixture-of-Experts (MoE) to improve performance in Large Language Models. |
| Approach: | They propose a method to combine LoRA and Mixture-of-Experts (MoE) to improve performance in Large Language Models. |
| Outcome: | The proposed method reduces redundancy in LoRA experts within the MoE architecture, and improves training quality across layers. |
SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models (2025.emnlp-main)
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Xingjian Diao, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Peijun Qing, Soroush Vosoughi, Jiang Gui
| Challenge: | Recent large language models have demonstrated impressive reasoning abilities, but their extension to the audio modality remains underexplored. |
| Approach: | They propose a rule-based reinforcement learning algorithm to equip LALMs with robust reasoning capabilities. |
| Outcome: | The proposed algorithm improves on the SoundMind benchmark. |
Assessing and Mitigating Medical Knowledge Drift and Conflicts in Large Language Models (2025.findings-emnlp)
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| Challenge: | Rapid medical concept drift can lead LLMs to provide incorrect or outdated advice. |
| Approach: | They propose to evaluate how large language models manage knowledge conflicts in clinical guidelines. |
| Outcome: | The proposed benchmark evaluates how LLMs manage varied knowledge conflicts in clinical guidelines. |
ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering (2025.emnlp-main)
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| Challenge: | Visual Question Answering (VQA) is increasingly used in diverse applications where models must provide accurate answers and explanations that humans can easily understand and verify. |
| Approach: | They propose a unified prototypical framework that learns question-aware prototypes that serve as reasoning anchors and applies spatially constrained matching to ensure that the selected evidence is coherent and semantically relevant. |
| Outcome: | The proposed framework yields faithful, fine-grained explanations while maintaining competitive accuracy. |
What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning (2026.acl-long)
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| Challenge: | Curriculum learning (CL) orders data corpus by difficulty, but prior work employs disparate difficulty metrics and training setups. |
| Approach: | They propose a framework that decomposes curriculum difficulty into five dimensions: Problem Difficulty, Model Surprisal, Confidence Margin, Predictive Uncertainty and Decision Variability. |
| Outcome: | The proposed framework decomposes curriculum difficulty into five dimensions . the results show that no curriculum strategy dominates universally . |