3 citations · 4 across the 22 of their papers we have counts for
26 papers
Task-Oriented Visual Feature Compression via Residual Vector Quantization for Device-Edge Multimodal Inference
Luning Pang, Cheng Yuan, Jiawei Shao +2
Large multimodal models (LMMs) support diverse visual understanding and reasoning tasks but are often impractical to run entirely on resource-constrained devices. Device-edge co-in…
RelaxKV: Recomputation Guided by the Query with Sparse Context Attention for Efficient KV Cache Reuse
Ruoling Qi, Yirui Liu, Xuaner Wu +5
Cross-request KV caching reduces the prefill cost of Retrieval-Augmented Generation (RAG), but conventional prefix caching severely limits cache reuse across requests. Position-Ind…
Just Let Linear States Forget the Distant Past: Prefix Caching via Suffix Replay for Hybrid LLMs
Yirui Liu, Ruoling Qi, Xuaner Wu +6
Hybrid LLMs interleave full-attention layers with linear-attention layers to reduce long-context inference cost, but this structure complicates prefix caching. Full-attention KV ca…
LinearKV: One Cached State Suffices for Position-Independent Caching in Hybrid LLMs
Yirui Liu, Ruoling Qi, Longwen Wang +5
LLM serving is increasingly accelerated by position-independent caching (PIC). Existing PIC methods, however, are built for full-attention models, where a token-indexed KV cache un…
Prefix-Guided On-Policy Distillation: Mining Golden Trajectories from Rollouts
Qingfei Zhao, Huan Song, Shuyu Tian +2
On-policy distillation (OPD) improves reasoning models by applying dense teacher supervision on student-sampled trajectories. However, scaling OPD to long-horizon reasoning exposes…
BPPO: Binary Prefix Policy Optimization for Efficient GRPO-Style Reasoning RL with Concise Responses
Qingfei Zhao, Huan Song, Shuyu Tian +2
Group Relative Policy Optimization (GRPO) is widely used for training reasoning models, but updating all sampled completions in each group incurs substantial cost and can reinforce…
Silicon Bureaucracy and AI Test-Oriented Education: Contamination Sensitivity and Score Confidence in LLM Benchmarks
Yiliang Song, Hongjun An, Jiangan Chen +4
Public benchmarks increasingly govern how large language models (LLMs) are ranked, selected, and deployed. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Tes…
Enhancing Neural Video Compression of Static Scenes with Positive-Incentive Noise
Cheng Yuan, Zhenyu Jia, Jiawei Shao +1
Static scene videos, such as surveillance feeds and videotelephony streams, constitute a dominant share of storage consumption and network traffic. However, both traditional standa…
Ruyi2.5 Technical Report
Huan Song, Shuyu Tian, Qingfei Zhao +5
We present Ruyi2.5, a multimodal familial model built on the AI Flow framework. Extending Ruyi2's "Train Once, Deploy Many" paradigm to the multimodal domain, Ruyi2.5 constructs a…
Privacy-Aware Camera 2.0 Technical Report
Huan Song, Shuyu Tian, Ting Long +5
With the increasing deployment of intelligent sensing technologies in highly sensitive environments such as restrooms and locker rooms, visual surveillance systems face a profound…
Ruyi2 Technical Report
Huan Song, Shuyu Tian, Junyi Hao +5
Large Language Models (LLMs) face significant challenges regarding deployment costs and latency, necessitating adaptive computing strategies. Building upon the AI Flow framework, w…
Boosting SAM for Cross-Domain Few-Shot Segmentation via Conditional Point Sparsification
Jiahao Nie, Yun Xing, Wenbin An +6
Motivated by the success of the Segment Anything Model (SAM) in promptable segmentation, recent studies leverage SAM to develop training-free solutions for few-shot segmentation, w…
CreditAudit: 2 Dimension for LLM Evaluation and Selection
Yiliang Song, Hongjun An, Jiangong Xiao +3
Leaderboard scores on public benchmarks have been steadily rising and converging, with many frontier language models now separated by only marginal differences. However, these scor…
A Real-Time Privacy-Preserving Behavior Recognition System via Edge-Cloud Collaboration
Huan Song, Shuyu Tian, Junyi Hao +4
As intelligent sensing expands into high-privacy environments such as restrooms and changing rooms, the field faces a critical privacy-security paradox. Traditional RGB surveillanc…
The Law of Multi-Model Collaboration: Scaling Limits of Model Ensembling for Large Language Models
Dakuan Lu, Jiaqi Zhang, Cheng Yuan +2
Recent advances in large language models (LLMs) have been largely driven by scaling laws for individual models, which predict performance improvements as model parameters and data…
Theoretical Foundations of Scaling Law in Familial Models
Huan Song, Qingfei Zhao, Ting Long +4
Neural scaling laws have become foundational for optimizing large language model (LLM) training, yet they typically assume a single dense model output. This limitation effectively…
Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence
Hongjun An, Yiliang Song, Jiawei Shao +2
Adverse social interactions, such as bullying, harassment, and other illicit activities, pose significant threats to individual well-being and public safety, leaving profound impac…
Are LLMs Vulnerable to Preference-Undermining Attacks (PUA)? A Factorial Analysis Methodology for Diagnosing the Trade-off between Preference Alignment and Real-World Validity
Hongjun An, Yiliang Song, Jiangan Chen +3
Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and interaction-friendly. However, this preference-ori…
Multimodal Continual Learning with MLLMs from Multi-scenario Perspectives
Kai Jiang, Siqi Huang, Xiangyu Chen +4
Multimodal large language models (MLLMs) deployed on devices must adapt to continuously changing visual scenarios such as variations in background and perspective, to effectively p…
Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression
Cheng Yuan, Jiawei Shao, Xuelong Li
Recent years have witnessed the rapid advancements of large language models (LLMs) and their expanding applications, leading to soaring demands for computational resources. The wid…
ScRPO: From Errors to Insights
Lianrui Li, Dakuan Lu, Jiawei Shao +1
We introduce Self-correction Relative Policy Optimization (ScRPO), a novel reinforcement learning framework designed to empower large language models with advanced mathematical rea…
Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding
Ruanjun Li, Ziheng Liu, Yuanming Shi +3
Large language models (LLMs) deliver impressive generation quality, but incur very high inference cost because each output token is generated auto-regressively through all model la…
AI Flow: Perspectives, Scenarios, and Approaches
Hongjun An, Wenhan Hu, Sida Huang +11
Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and c…
What You Have is What You Track: Adaptive and Robust Multimodal Tracking
Yuedong Tan, Jiawei Shao, Eduard Zamfir +7
Multimodal data is known to be helpful for visual tracking by improving robustness to appearance variations. However, sensor synchronization challenges often compromise data availa…