18 citations · 25 across the 28 of their papers we have counts for
31 papers
LoopREG: Representation Supervision Enables Test-Time Recurrent Depth in Diffusion Transformers
Zipeng Wang, Anping Xiong, Jing Zhang +17
Reusing Transformer blocks offers a way to increase diffusion-model computation at test time, but naive recurrence can degrade generation beyond its training depth. We introduce Lo…
ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification
Jieyun Huang, Yi Shen, Kaikai Zhao +7
Fraudulent messages sent via Short Message Service (SMS) are increasingly obfuscated to evade cost-conscious classifiers in production systems. In Chinese SMS, attackers can exploi…
Is Multimodal Speculative Decoding Ready for Diffusion-Based Parallel Drafting? A Survey and Empirical Diagnosis
Yantao Li, Huanlin Gao, Fang Zhao +12
Speculative decoding accelerates autoregressive generation by allowing a lightweight drafter to propose future tokens while a target model verifies them in parallel. Its lossless g…
Scalable Question-Centric Text-to-Image Evaluation: Reliable Ranking, Fine-Grained Diagnosis, and Cost-Aware Routing
Shaoan Zhao, Fang Zhao, Xueqiang Guo +9
Modern text-to-image (T2I) models often have similar total scores but different strengths, making practical selection difficult. Fine-grained benchmarks decompose prompts into ques…
GLM-5 Serving Parameter Tuning for OpenClaw: Single-Deployment MaaS Inference Optimization for Long-Context Agent Workloads
Minjie Hua, Ning Wang, Peijun Yang +2
OpenClaw requests are dominated by long, tool-augmented prefixes, including system prompts, conversation history, and tool outputs fed back into the context window. For this worklo…
OTCache: Optimal Transport for Geometry-Aware Caching in Diffusion Models
Huanlin Gao, Fang Zhao, Qiang Hui +8
We propose OTCache, a training-free framework for accelerating diffusion sampling via caching schedule prediction. Existing graph-based caching methods reduce redundant computation…
Mixture of Heterogeneous Grouped Experts for Language Modeling
Zhicheng Ma, Xiang Liu, Zhaoxiang Liu +5
Large Language Models (LLMs) based on Mixture-of-Experts (MoE) are pivotal in industrial applications for their ability to scale performance efficiently. However, standard MoEs enf…
Chain-of-Trajectories: Unlocking the Intrinsic Generative Optimality of Diffusion Models via Graph-Theoretic Planning
Ping Chen, Xiang Liu, Xingpeng Zhang +7
Diffusion models operate in a reflexive System 1 mode, constrained by a fixed, content-agnostic sampling schedule. This rigidity arises from the curse of state dimensionality, wher…
Beyond Geometry: Artistic Disparity Synthesis for Immersive 2D-to-3D
Ping Chen, Zezhou Chen, Xingpeng Zhang +8
Current 2D-to-3D conversion methods achieve geometric accuracy but are artistically deficient, failing to replicate the immersive and emotionally resonant experience of professiona…
HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation
Wenjing Zhang, Jiangze Yan, Jieyun Huang +7
Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitations of rejection sampling. Standard methods treat t…
PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment
Yantao Li, Qiang Hui, Chenyang Yan +8
Reinforcement learning has recently improved the reasoning ability of Large Language Models and Multimodal LLMs, yet prevailing reward designs emphasise final-answer correctness an…
MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference
Huanlin Gao, Ping Chen, Fuyuan Shi +11
We present MeanCache, a training-free caching framework for efficient Flow Matching inference. Existing caching methods reduce redundant computation but typically rely on instantan…
LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation
Huanlin Gao, Ping Chen, Fuyuan Shi +5
We present LeMiCa, a training-free and efficient acceleration framework for diffusion-based video generation. While existing caching strategies primarily focus on reducing local he…
HiMo-CLIP: Modeling Semantic Hierarchy and Monotonicity in Vision-Language Alignment
Ruijia Wu, Ping Chen, Fei Shen +8
Contrastive vision-language models like CLIP have achieved impressive results in image-text retrieval by aligning image and text representations in a shared embedding space. Howeve…
PSTF-AttControl: Per-Subject-Tuning-Free Personalized Image Generation with Controllable Face Attributes
Xiang liu, Zhaoxiang Liu, Huan Hu +5
Recent advancements in personalized image generation have significantly improved facial identity preservation, particularly in fields such as entertainment and social media. Howeve…
Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity
Ping Chen, Xiang Liu, Zhaoxiang Liu +7
With the rapid advancement of large language models (LLMs), natural language processing (NLP) has achieved remarkable progress. Nonetheless, significant challenges remain in handli…
MITS: A Large-Scale Multimodal Benchmark Dataset for Intelligent Traffic Surveillance
Kaikai Zhao, Zhaoxiang Liu, Peng Wang +7
General-domain large multimodal models (LMMs) have achieved significant advances in various image-text tasks. However, their performance in the Intelligent Traffic Surveillance (IT…
iLearnRobot: An Interactive Learning-Based Multi-Modal Robot with Continuous Improvement
Kohou Wang, ZhaoXiang Liu, Lin Bai +5
It is crucial that robots' performance can be improved after deployment, as they are inherently likely to encounter novel scenarios never seen before. This paper presents an innova…
Quantitative Analysis of Performance Drop in DeepSeek Model Quantization
Enbo Zhao, Yi Shen, Shuming Shi +7
Recently, there is a high demand for deploying DeepSeek-R1 and V3 locally, possibly because the official service often suffers from being busy and some organizations have data priv…
SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models
Xiang Liu, Zhaoxiang Liu, Peng Wang +4
When using supervised fine-tuning (SFT) to adapt large language models (LLMs) to specific domains, a significant challenge arises: should we use the entire SFT dataset for fine-tun…
A Large Vision-Language Model based Environment Perception System for Visually Impaired People
Zezhou Chen, Zhaoxiang Liu, Kai Wang +2
It is a challenging task for visually impaired people to perceive their surrounding environment due to the complexity of the natural scenes. Their personal and social activities ar…
Optimizing for the Shortest Path in Denoising Diffusion Model
Ping Chen, Xingpeng Zhang, Zhaoxiang Liu +6
In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality.…
A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis
Xiang Liu, Zhaoxiang Liu, Huan Hu +4
While conversational generative AI has shown considerable potential in enhancing decision-making for agricultural professionals, its exploration has predominantly been anchored in…
Safety Evaluation and Enhancement of DeepSeek Models in Chinese Contexts
Wenjing Zhang, Xuejiao Lei, Zhaoxiang Liu +11
DeepSeek-R1, renowned for its exceptional reasoning capabilities and open-source strategy, is significantly influencing the global artificial intelligence landscape. However, it ex…