11 citations · 16 across the 16 of their papers we have counts for
18 papers
Beyond Correctness: Compute-Responsive Data Selection for Reasoning Model Post-Training
Liang He, Xiangyu Li, Yuxin Wang +4
Reasoning language models increasingly rely on test-time compute scaling, yet most existing work studies how to allocate inference computation while treating the model itself as fi…
SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision
Rong Wan, Wei Xie, Jiaxi Li +4
Audio deepfake detection (ADD) must remain effective when new spoofing attacks emerge after deployment. Emerging audio language model (ALM)-based ADD methods are built on predefine…
Grounded Checklist Partial Credit for Agent Skill Trajectories
Suliu Qin, Lu Yin, Xilu Wang
Language-model agents increasingly tackle long-horizon tasks in interactive environments, yet their evaluation commonly relies on task-level success rates by reducing an entire exe…
Not All Visual Tokens Are Equally Safe to Remove:Consequence-Sensitive Visual Token Compression
Jingbo Wen, Liang He, Mingyu Cao +4
Visual token compression for vision--language models (VLMs) has largely relied on criteria such as attention, redundancy, and uncertainty to maximize average accuracy under a fixed…
From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents
Liang He, Jingbo Wen, Hongyu Gu +5
Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge. Although recent work has substantially improved skill retrieval du…
CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning
Zijian Jiang, Chaoli Sun, Handing Wang +1
One-shot federated learning (OSFL) has emerged as a promising collaborative model learning framework with only a single round of communication, offering significant advantages in c…
BudgetDraft: Acceptance-Aware Multi-View Training for Sparse-KV Speculative Decoding
Liang He, Jingbo Wen, Qishi Zhan +4
Speculative decoding speeds up autoregressive decoding by using a drafter to propose multiple tokens that a verifier validates in parallel. In resource-constrained deployments, the…
ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity
Jiaxi Li, Lu Yin, Li Shen +5
Large Language Models (LLMs) have achieved remarkable capabilities, but their immense computational demands during training remain a critical bottleneck for widespread adoption. Lo…
Self-Supervised Federated Learning under Data Heterogeneity for Label-Scarce Diatom Classification
Mingkun Tan, Xilu Wang, Michael Kloster +1
Label-scarce visual classification under decentralized and heterogeneous data is a fundamental challenge in pattern recognition, especially when sites exhibit partially overlapping…
Long Chain-of-Thought Compression via Fine-Grained Group Policy Optimization
Xinchen Han, Hossam Afifi, Michel Marot +2
Large Language Models (LLMs) often generate unnecessarily verbose Chain-of-Thought (CoT) reasoning that increases computational costs and latency without proportional performance g…
TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents
Shaojie Zhuang, Lu Yin, Guangshun Wei +3
Automatic tooth segmentation and identification from intra-oral scanned 3D models are fundamental problems in digital dentistry, yet most existing approaches rely on task-specific…
LOST: Low-rank and Sparse Pre-training for Large Language Models
Jiaxi Li, Lu Yin, Li Shen +6
While large language models (LLMs) have achieved remarkable performance across a wide range of tasks, their massive scale incurs prohibitive computational and memory costs for pre-…
OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving Framework
Jiaxi Li, Lu Yin, Xilu Wang
The integration of Large Language Models (LLMs) into autonomous driving systems offers promising enhancements in environmental understanding and decision-making. However, the subst…
MO-EMT-NAS: Multi-Objective Continuous Transfer of Architectural Knowledge Between Tasks from Different Datasets
Peng Liao, XiLu Wang, Yaochu Jin +1
Deploying models across diverse devices demands tradeoffs among multiple objectives due to different resource constraints. Arguably, due to the small model trap problem in multi-ob…
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimizat…
Recent Advances in Bayesian Optimization
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the dev…
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is unten…
Learning pose variations within shape population by constrained mixtures of factor analyzers
Xilu Wang
Mining and learning the shape variability of underlying population has benefited the applications including parametric shape modeling, 3D animation, and image segmentation. The cur…