114 citations · 413 across the 46 of their papers we have counts for
49 papers
Event-Aligned Visual Action Reasoning for World Action Models
Xiaomeng Yang, Yushu Wu, Yi Gao +4
World-Action Models (WAMs) utilize future visual prediction as an intermediate reasoning process to guide action generation. However, existing WAMs typically structure visual imagi…
LEAP: Learned Block-wise Evidence Retrieval for Long Audio-Video Perception
Juyi Lin, Zhiqiang Lao, Jiali Cui +9
Hour-scale audio-visual question answering is constrained by a context dilemma: dense whole-recording encoding rapidly exhausts context limits, whereas uniform temporal compression…
P2Flow: Phoneme-aware Progressive Flow Matching for Extreme Speech Super-Resolution
Ningyuan Yang, Yize Li, Pu Zhao +5
Generative models have recently demonstrated considerable promise in speech super-resolution (SSR). Nevertheless, the majority of existing work has concentrated on standard or vers…
Hydra: Phase-Aware Workload Characterization of LLM Inference across Edge SoC Generations, Backends, and Quantization Levels
Amir Taherin, Sana Taghipour Anvari, Charles Amante +9
Edge LLM deployment is shaped by more than model size and precision: inference backend, hardware platform, memory traffic, and power management all affect latency and efficiency. W…
Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B
Rahul Chowdhury, Timothy A Rupprecht, Senhao Cao +5
Recent work has shown that large language models (LLMs) exhibit strong numerical sequence modeling capabilities and show promise in time-series prediction. While LLMs display in-co…
Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT
Pu Zhao, Changdi Yang, Yixiao Chen +4
Translating C code into safe, idiomatic Rust is a longstanding software-engineering goal because it can eliminate entire classes of memory-safety vulnerabilities while preserving t…
ScAle: Attention Head Scaling as a Minimal Adapter for Spatial Reasoning in Vision Language Models
Rahul Chowdhury, Timothy A Rupprecht, Xuan Shen +2
Spatial reasoning remains a persistent challenge for many vision language models (VLMs), and improving it typically requires fine-tuning with substantial additional parameters. Our…
OmniMem: Scalable and Adaptive Memory Retrieval for Long Video Generation
Lin Zhao, Yushu Wu, Yifan Gong +2
Autoregressive (AR) video generation extends videos by producing latent chunks sequentially, but scaling to long videos requires repeated access to a growing historical KV cache. E…
PhyWorld: Physics-Faithful World Model for Video Generation
Pu Zhao, Juyi Lin, Timothy Rupprecht +10
World simulators can provide safe and scalable environments for training Physical AI systems before real-world deployment. Large video generation models are emerging as a promising…
PhyGround: Benchmarking Physical Reasoning in Generative World Models
Juyi Lin, Arash Akbari, Yumei He +13
Generative world models are increasingly used for video generation, where learned simulators are expected to capture the physical rules that govern real-world dynamics. However, ev…
From History to State: Constant-Context Skill Learning for LLM Agents
Haoyang Xie, Xinyuan Wang, Yancheng Wang +2
Large language model (LLM) agents are increasingly used to operate browsers, files, code and tools, making personal assistants a natural deployment target. Yet personal agents face…
A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models
Ningyuan Yang, Yize Li, Diego A. Cuji +4
Audio super-resolution (SR), also referred to as bandwidth extension (BWE), aims to reconstruct high-fidelity signals from low-resolution (LR) or band-limited (BL) observations, an…
Human Cognition in Machines: A Unified Perspective of World Models
Timothy Rupprecht, Pu Zhao, Amir Taherin +20
This report of world models distinguishes prior works by the cognitive functions they innovate. Many works claim an almost human-like cognitive capability in their world models. To…
When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making
Jun Liu, Pu Zhao, Zhenglun Kong +12
Embodied robotic systems increasingly rely on large language model (LLM)-based agents to support high-level reasoning, planning, and decision-making during interactions with the en…
VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
Juyi Lin, Amir Taherin, Arash Akbari +11
Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language. However, current VLA models suffer…
Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality
Zhenglun Kong, Yize Li, Fanhu Zeng +7
In Transformer architectures, tokens\textemdash discrete units derived from raw data\textemdash are formed by segmenting inputs into fixed-length chunks. Each token is then mapped…
Structured Agent Distillation for Large Language Model
Jun Liu, Zhenglun Kong, Peiyan Dong +10
Large language models (LLMs) exhibit strong capabilities as decision-making agents by interleaving reasoning and actions, as seen in ReAct-style frameworks. Yet, their practical de…
Toward Adaptive Large Language Models Structured Pruning via Hybrid-grained Weight Importance Assessment
Jun Liu, Zhenglun Kong, Pu Zhao +9
Structured pruning for large language models (LLMs) has garnered significant academic interest due to its ability to efficiently compress and accelerate LLMs by eliminating redunda…
InstructGIE: Towards Generalizable Image Editing
Zichong Meng, Changdi Yang, Jun Liu +3
Recent advances in image editing have been driven by the development of denoising diffusion models, marking a significant leap forward in this field. Despite these advances, the ge…
DiffClass: Diffusion-Based Class Incremental Learning
Zichong Meng, Jie Zhang, Changdi Yang +3
Class Incremental Learning (CIL) is challenging due to catastrophic forgetting. On top of that, Exemplar-free Class Incremental Learning is even more challenging due to forbidden a…
Squat: Quant Small Language Models on the Edge
Xuan Shen, Peiyan Dong, Zhenglun Kong +9
A growing trend has emerged in designing high-quality Small Language Models (SLMs) with a few million parameters. This trend is driven by the increasing concerns over cloud costs,…
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning
Chenan Wang, Pu Zhao, Siyue Wang +1
Deep Neural Network (DNN) models when implemented on executing devices as the inference engines are susceptible to Fault Injection Attacks (FIAs) that manipulate model parameters t…
Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Network
Hao Cheng, Jiahang Cao, Erjia Xiao +2
Deploying energy-efficient deep learning algorithms on computational-limited devices, such as robots, is still a pressing issue for real-world applications. Spiking Neural Networks…
Less is More: Data Pruning for Faster Adversarial Training
Yize Li, Pu Zhao, Xue Lin +2
Deep neural networks (DNNs) are sensitive to adversarial examples, resulting in fragile and unreliable performance in the real world. Although adversarial training (AT) is currentl…