21 citations · 32 across the 22 of their papers we have counts for
27 papers
Gamma: Grounded Multi-Agent Memory with Mechanical Audit for Memory-Intensive Manipulation
Yudong Liu, Yueqian Lin, Zijia Tang +11
Pretrained vision-language-action (VLA) policies fail on memory tasks, behaviour that depends on events observed long before the current frame, because their training data is overw…
Will My Assistant Remember My Allergy? What Personal LLM Assistants Forget When Conversation Memory Is Compressed
Lichen Zhu, Yueqian Lin, Yiheng Wang +3
Personal LLM assistants (health companions, elder-care agents, accessibility aides) are judged by what they remember about a person: a medication or an allergy mentioned in passing…
When No Answer Is Correct: Diagnosing Absent Answer Detection for MLLMs in Video Understanding
Yiheng Wang, Yueqian Lin, Lichen Zhu +3
Multimodal large language models (MLLMs) have made substantial advancements in video understanding, yet the reliability of their responses remains underexplored. This work presents…
Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
Yudong Liu, Yuan Li, Zijia Tang +12
Dual-system Vision-Language-Action (VLA) models achieve state-of-the-art robotic manipulation but are bottlenecked by the VLM backbone, which must execute at every control step whi…
FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching
Yuzhe Fu, Hancheng Ye, Cong Guo +7
Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sampling (FPS), often introduces…
Query-Conditioned Evidential Keyframe Sampling for MLLM-Based Long-Form Video Understanding
Yiheng Wang, Lichen Zhu, Yueqian Lin +4
Multimodal Large Language Models (MLLMs) have shown strong performance on video question answering, but their application to long-form videos is constrained by limited context leng…
T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning
Qinsi Wang, Hancheng Ye, Jinhee Kim +12
Think about how human handles complex reading tasks: marking key points, inferring their relationships, and structuring information to guide understanding and responses. Likewise,…
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
Zhongxi Wang, Yueqian Lin, Jingyang Zhang +3
Safety evaluation of multimodal large language models requires tracking not only whether an attack succeeds, but also how the interaction unfolds across turns and input modalities.…
Bridging the Perception Gap: A Lightweight Coarse-to-Fine Architecture for Edge Audio Systems
Hengfan Zhang, Yueqian Lin, Hai Helen Li +1
Deploying Audio-Language Models (Audio-LLMs) on edge infrastructure exposes a persistent tension between perception depth and computational efficiency. Lightweight local models ten…
LLaViDA: A Large Language Vision Driving Assistant for Explicit Reasoning and Enhanced Trajectory Planning
Yudong Liu, Spencer Hallyburton, Jiwoo Kim +8
Trajectory planning is a fundamental yet challenging component of autonomous driving. End-to-end planners frequently falter under adverse weather, unpredictable human behavior, or…
KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems
Hancheng Ye, Zhengqi Gao, Mingyuan Ma +8
Multi-agent large language model (LLM) systems are increasingly adopted for complex language processing tasks that require communication and coordination among agents. However, the…
AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning
Yueqian Lin, Zhengmian Hu, Jayakumar Subramanian +4
Effective human-AI collaboration on complex reasoning tasks requires that users understand and interact with the model's process, not just receive an output. However, the monolithi…
Voice Evaluation of Reasoning Ability: Diagnosing the Modality-Induced Performance Gap
Yueqian Lin, Zhengmian Hu, Qinsi Wang +6
We present Voice Evaluation of Reasoning Ability (VERA), a benchmark for evaluating reasoning ability in voice-interactive systems under real-time conversational constraints. VERA…
Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play
Qinsi Wang, Bo Liu, Tianyi Zhou +6
Although reinforcement learning (RL) has emerged as a promising approach for improving vision-language models (VLMs) and multimodal large language models (MLLMs), current methods r…
Angles Don't Lie: Unlocking Training-Efficient RL Through the Model's Own Signals
Qinsi Wang, Jinghan Ke, Hancheng Ye +6
Current Reinforcement Fine-tuning (RFT) paradigms for Large Language Models (LLMs) suffer from sample inefficiency due to the redundant exposure of identical queries under uniform…
CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models
Qinsi Wang, Hancheng Ye, Ming-Yu Chung +6
Vision-Language Models (VLMs) excel across diverse tasks but suffer from high inference costs in time and memory. Token sparsity mitigates inefficiencies in token usage, while neur…
Keyframe-oriented Vision Token Pruning: Enhancing Efficiency of Large Vision Language Models on Long-Form Video Processing
Yudong Liu, Jingwei Sun, Yueqian Lin +6
Vision language models (VLMs) demonstrate strong capabilities in jointly processing visual and textual data. However, they often incur substantial computational overhead due to red…
HippoMM: Hippocampal-inspired Multimodal Memory for Long Audiovisual Event Understanding
Yueqian Lin, Jingyang Zhang, Qinsi Wang +5
Comprehending extended audiovisual experiences remains challenging for computational systems, particularly temporal integration and cross-modal associations fundamental to human ep…
GenAI at the Edge: Comprehensive Survey on Empowering Edge Devices
Mozhgan Navardi, Romina Aalishah, Yuzhe Fu +4
Generative Artificial Intelligence (GenAI) applies models and algorithms such as Large Language Model (LLM) and Foundation Model (FM) to generate new data. GenAI, as a promising ap…
SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval
Yueqian Lin, Yuzhe Fu, Jingyang Zhang +5
We introduce Speech Information Retrieval (SIR), a new long-context task for Speech Large Language Models (Speech LLMs), and present SPIRAL, a 1,012-sample benchmark testing models…
Muskits-ESPnet: A Comprehensive Toolkit for Singing Voice Synthesis in New Paradigm
Yuning Wu, Jiatong Shi, Yifeng Yu +7
This research presents Muskits-ESPnet, a versatile toolkit that introduces new paradigms to Singing Voice Synthesis (SVS) through the application of pretrained audio models in both…
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang +10
Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection. Meanwhile, several other probl…
Singing Voice Data Scaling-up: An Introduction to ACE-Opencpop and ACE-KiSing
Jiatong Shi, Yueqian Lin, Xinyi Bai +6
In singing voice synthesis (SVS), generating singing voices from musical scores faces challenges due to limited data availability. This study proposes a unique strategy to address…
SD-NAE: Generating Natural Adversarial Examples with Stable Diffusion
Yueqian Lin, Jingyang Zhang, Yiran Chen +1
Natural Adversarial Examples (NAEs), images arising naturally from the environment and capable of deceiving classifiers, are instrumental in robustly evaluating and identifying vul…